Solar Radiation Forecasting Models and their Thermodynamic Analysis in Asaba: Least Square Regression and Machine Learning Approach

Abstract: Least square regression and machine learning tools were used for the development of global solar radiation forecasting models for Asaba region. Data from the year 2013-2022 from Nigerian Meteorological Agency, Asaba was used for this study. The least square regression method was used to develop four global solar radiation -based models, tagged H1, H2, H3 and H4 with characteristic day length, solar declination angle, rainfall amount, etc. as its model terms while the machine learning models produced multilayer perceptron, coarse Gaussian model (SVM-based model) and XGBoost model. The prediction factors like mean bias error, mean percentage error, root mean square error, Nash-Sutcliffe equation, coefficient of correlation (R), t-test, and coefficient of determination (R2) were considered using the model terms. The results indicates that H4 model outperformed H1, H2, H3, machine learning models (SVM-based model, multilayer perceptron and XGBoost) and other existing models (MA-MME and MLR) with a mean percentage error value of 0.740, RMSE value of 46.588, Nash-Sutcliffe equation value of 0.739, higher R2 value of 0.7391, t-test value of 2.595E-24 and mean bias error value of -6.88E-12. Thus, H4 model results fell within accepted range. Additionally, the exergy of the global solar radiation of Asaba varied from 20-185 W/m2 which are good. This shows that a more efficient and ideal global solar radiation prediction model (H4) has been developed for Asaba and other regions that share similar climatic conditions.


Automated Detection and Machine Learning‐Based Classification of Seismic Tremors Associated With a Non‐Volcanic Gas Emission (Mefite d’Ansanto, Southern Italy)

Abstract: Abstract A major aim in the study of crustal fluids is the development of automatic methodologies for monitoring deep‐source, non‐volcanic gas emissions’ spatio‐temporal evolution. Crustal fluids play a significant role in the generation of large earthquakes and the characterization of their emissions on the surface can be essential for better understanding crustal processes generating earthquakes. We investigate seismic tremors recorded over 4 days in 2019 at the Mefite d’Ansanto (southern Apennines, Italy) that is located at the northern end of the fault system that generated the Mw 6.9 1980 Irpinia Earthquake. The Mefite d’Ansanto is hypothesized to be the largest natural, non‐volcanic, CO 2 ‐rich gas emission on Earth. The seismic tremor is studied by employing a dense temporary seismic network and an automated detection algorithm based on non‐parametric statistics of the recorded signal amplitudes. We extracted signal characteristics (RMS amplitude and statistical moments of amplitudes both in time and frequency domains) for use in the subsequent supervised machine‐learning classification of the target tremor and accidently detected anthropogenic and background noise. The data set is used for the training and optimization of station‐based KNN (k‐Nearest‐Neighbors) binary classifiers obtaining good classification performances with a median overall accuracy across all stations of 92.8%. The classified tremor displayed common features at all stations: variable duration (16 s to 30–40 min), broad peak frequency (4–20 Hz) with varying amplitudes, and two types of signals: (a) long‐duration, high‐amplitude tremor and (b) pulsating tremor. Higher tremor amplitudes recorded at stations closer to local bubbling and pressurized vents suggest multiple local tremor sources.


Offline Handwritten Signature Identification based on Hybrid Features and Proposed Deep Model

Abstract: Handwritten signature identification is the process of identifying the true identity of an individual by analyzing their signature. This is an important task in applications such as financial transactions, legal documents, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. This paper proposes an authentic signature identification method based on integrating static (off-line) signature data and proposed deep-based model, this is done by fused three types of signature features, Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, the fused features are inputted to the proposed deep-based model of 25 layers for identifying each person. For experiments, we have used three datasets: Our own private collected dataset, called SigArab, and two public datasets called SigComp11 and CEDAR respectively. The proposed deep model achieves 99.23%, 100%, 100% accuracy on SigArab, CEDAR and SigComp2011 datasets.


Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

Abstract: Abstract The Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.


LoHDP: Adaptive local differential privacy for high‐dimensional data publishing

Abstract: Summary The increasing availability of high‐dimensional data collected from numerous users has led to the need for multi‐dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters. These models are then used to derive low‐dimensional marginals of these clusters, allowing for an approximation of the distribution of the original dataset and the generation of synthetic datasets. Existing solutions have limitations in computing the marginals of pairwise attributes and multi‐dimensional distribution on attribute clusters, as well as constructing relational dependency graphs that contain large clusters. To address these problems, we propose LoHDP, a high‐dimensional data publishing method composed of adaptive marginal computing and an effective attribute clustering method. The adaptive local marginal calculates any k ‐dimensional marginals required in the algorithm. In particular, methods such as sampling‐based randomized response are used instead of privacy budget splits to perturb user data. The attribute clustering method measures the correlation between pairwise attributes using an effective method, reduces the search space during the construction of the dependency graph using high‐pass filtering technology, and realizes dimensionality reduction by combining sufficient triangulation operation. We demonstrate through extensive experiments on real datasets that our LoHDP method outperforms existing methods in terms of synthetic dataset quality.


Research on Governance of Higher Engineering Education Quality in China after Accessing the Washington Accord

Abstract: Abstract As an important quality assurance mechanism, the program accreditation of engineering education has become the basic mechanism for improving the quality of engineering education in various countries. Under the guidance of the CPC Central Committee and the State Council, China has established quality assurance mechanism of engineering education. In June 2016, China officially accessed the Washington Accord, which symbolized that its engineering education accreditation system has been recognized by the international engineering community. However, China's accessing the Washington Accord as a full signatory is only the beginning of integrating into the international engineering community. China must actively respond to the contrasts with international engineering education, and optimize the accreditation system of engineering education. Thus, this research attempts to propose an emerging concept, i.e., governance of higher engineering education quality; draw on the Governance Theory to establish a logical framework for the governance of higher engineering education quality in China around the dimensions of governance motivation, subjects, objects, purposes and approaches, so as to provide "China Solution" in the context of global quality assurance in engineering education.


Implementation of Actionable Gamification Design Framework in Machining Training

Abstract: Abstract With the development of Industry 4.0 technologies adopted in machining industry, more and more machine tools and systems become smarter and automatic. However, contrary to our expectations, the machining automation has not much reduced the labor shortage issue in the machining industry, and this demand gap is gradually expanding in recent years. Although this issue is multiaspect and complicated, it is evident that one of the main reasons comes from the conventional training system. By comparing with the outdated training content, which has been realized and emphasized by machining training providers, the problems regarding the training method have not yet attracted sufficient attention. In order to realize the training outcomes efficiently, instructor-led and on-the-job training methods are widely adopted by most of the machining training programs. However, trainee's engagement, i.e., interest and involvement, is often ignored, which is one of the main reasons for downward enrollment and retention rate of many machining related programs from trade schools or community colleges. To engage the younger generation into this career field, more attention should be paid to incorporate more training methods favorite with young students, and the interactive training method with game-based elements has been proved to be a good solution in many similar applications. Gamification, a technique to facilitate training and learning within the playable context, has the potential to enhance training engagement. In a gamified application, game design elements and gameplay mechanics are added to motivate trainees to stay in the loop of repeating necessary equipment operations while mitigating the mental fatigue they might have due to lack of interest and the boreout syndrome at machining workspace. Gamification is promising to improve self-motivation by stimulating the desire to pursue a gaming-inspired goal (i.e., reaching a winning condition in the game or earning a level-up badge to show off on the profile). By connecting the training tasks to a gaming goal, trainees would be more likely to enjoy doing the practice and feel less forced. The objective of this study is to systematically evaluate the conventional machining training under the guidance of the Octalysis Framework with eight core drives of gamification and come up with the Octalysis score to each core drive. In addition, the Octalysis for conventional machining training will be compared with the designs and mechanics of successful skill-learning games to identify the strong and weak aspects of learning engagement. From this study, it is expected to propose a new perspective to evaluate the current machining training method on student attraction and engagement and improve it with appropriate game-based elements.


Reweighting trajectory segments into steady state without knowledge of the sampling distribution

Abstract: Molecular dynamics (MD) and enhanced sampling simulations have become fundamental tools for studying biomolecular events. A significant challenge in these simulations is ensuring that sampled configurations and transitions converge to the stationary distribution of interest, whether equilibrium or nonequilibrium. Lack of convergence constrains the estimation of mechanisms, free energy, and rates of complex molecular events. Here, we introduce the “randomized iterative trajectory reweighting” algorithm to estimate a stationary distribution from unconverged simulation data, applicable to trajectories generated without biasing forces. This method iteratively reweights trajectories based on the stationary solution of a coarse-grained transition matrix, employing a new random clustering in each iteration. The iterative random clustering mitigates the phase-space discretization error inherent in existing trajectory reweighting techniques based on one-shot clustering and ultimately yields numerically unbiased quasi-continuous configuration-space distributions and estimates of observables. We demonstrate this procedure for both equilibrium and nonequilibrium steady states, using synthetic molecular dynamics (synMD) trajectories to enable comparison with exact reference values.


Classifying Negative Objects With Neural Networks

Abstract: The term object is generally only associated with something that can be sensed. However, the empty space between and around objects can also be considered as objects, which we will define as negative objects. While incredibly important in some instances, they have been neglected by the research community in comparison to their positive counterparts. Without a properly developed lexicon for them, negative objects are hard to discuss and describe. This thesis develops the starting point for a lexicon for negative objects, builds a publicly accessible dataset, and demonstrates that they can be identified within an image with the application of a machine learning algorithm. The neural network performs at an average precision of 74% when identifying a certain type of negative object (holes). This algorithm also shows promise in being able to differentiate between holes and tunnels.


Distance Measure Concepts for Bayesian Inference of Chaotic Dynamical System Parameters

Abstract: Abstract The problem of determining the parameters of dynamical systems often reduces to developing some notion of a "distance" between observed and simulated system trajectory data. The best parameter fit can then be found by adjusting the parameters that generate the simulated trajectory until the distance is minimized. However, in the case of chaotic dynamical systems, traditional distance measures such as the Mean Square Error (MSE) often fail to produce good results, a consequence of these systems' inherent sensitivity to changes in parameters and initial conditions. In this paper, we adopt the perspective that more robust distance measures can be formulated when the trajectories of these chaotic systems are treated as samples from probability distributions, rather than as time series data. Within this perspective, we evaluate the efficacy of three candidate distance measure concepts: the correlation integral likelihood (proposed by Haario et al.), the Wasserstein metric, and a family of information-theoretic distances based on the Kullback-Leibler divergence. We give particular emphasis to the performance of these methods on the Lorenz63 system, a canonical chaotic system with applications in modeling atmospheric convection.


Closed-loop circuit model of coagulation physiology

Abstract: Clinical anticoagulation therapy is dosed at a standard dose or according to weight, with adjustments made based on laboratory test targets. Patients are at risk of over or under-anticoagulation given lengthy return times for many of these tests. To this end, the authors aim to develop a model of the physiologic coagulative response to anticoagulation administration that reflects real-time dose adjustments. Here, we present our closed-loop multi-element model of physiologic coagulation which is the foundation for this project. The authors have accomplished the basic circuit element model of 64 coupled differential equations describing the human coagulation cascade and feedback paths that incorporate rate kinetics of factor activation. The authors developed a Python script able to convert a large number of equations to a form compatible with SPICE circuit modeling software, which was then used to generate a multi-input circuit model of the differential equations. The equations were converted to a state space representation and used to derive a linearized approximation about the steady state. SPICE simulates internal feedback loops of the coagulation cascade over time with initial conditions representing normal coagulability. The authors are employing the law of conservation of mass to piecewise validate this model by observing species for concentration changes. Our model demonstrates changes in each coagulation factor concentration as an output over a specified time, given initial species concentrations as inputs. These initial parameters may be adjusted to reflect a starting physiologic state of coagulation. The authors observed little change in concentration for components whose eigenvalues equal 0, suggesting initial conditions are very close to steady state values. Coagulation physiology may be simplified into a circuit model that allows for real-time physiologic visualization. This will be useful in timely adjustments of anticoagulation therapy.


Continuous-Variable Quantum State Designs: Theory and Applications

Abstract: We generalize the notion of quantum state designs to infinite-dimensional spaces. We first prove that, under the definition of continuous-variable (CV) state t-designs from [Blume-Kohout et al., Commun. Math. Phys. 326, 755 (2014)], no state designs exist for t≥2. Similarly, we prove that no CV unitary t-designs exist for t≥2. We propose an alternative definition for CV state designs, which we call rigged t-designs, and provide explicit constructions for t=2. As an application of rigged designs, we develop a design-based shadow-tomography protocol for CV states. Using energy-constrained versions of rigged designs, we define an average fidelity for CV quantum channels and relate this fidelity to the CV entanglement fidelity. As an additional result of independent interest, we establish a connection between torus 2-designs and complete sets of mutually unbiased bases.Received 13 December 2022Revised 25 October 2023Accepted 12 December 2023DOI:https://doi.org/10.1103/PhysRevX.14.011013Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum harmonic oscillatorQuantum information processingQuantum information processing with continuous variablesQuantum information theoryQuantum measurementsQuantum Information, Science & Technology


This issue of G<scp>eophysics</scp>

Abstract: In this article, the Editor of Geophysics provides an overview of all technical articles in this issue of the journal.


Visualisations for Bayesian Additive Regression Trees

Abstract: Tree-based regression and classification has become a standard tool in modern data science. Bayesian Additive Regression Trees (BART) has in particular gained wide popularity due its flexibility in dealing with interactions and non-linear effects. BART is a Bayesian tree-based machine learning method that can be applied to both regression and classification problems and yields competitive or superior results when compared to other predictive models. As a Bayesian model, BART allows the practitioner to explore the uncertainty around predictions through the posterior distribution. In this paper, we present new Visualisation techniques for exploring BART models. We construct conventional plots to analyse a model’s performance and stability as well as create new tree-based plots to analyse variable importance, interaction, and tree structure. We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance and interactions jointly using colour scale to represent posterior uncertainty. Our new Visualisations are designed to work with the most popular BART R packages available, namely BART, dbarts, and bartMachine. Our approach is implemented in the R package bartMan (BART Model ANalysis).


An Algorithm to Analyze Arabic Verbs Morphologically

Abstract: Morphological analysis describes a word in isolation from the context, which is an important stage when trying to automatically understand and interpret the text. Many studies have provided morphological analyzers of Arabic verbs. However, although accurate, they do not deal with all cases of Arabic verbs. This research introduces an algorithm to analyze Arabic verbs morphologically based on regular expressions then on Morphological Database. The advantages of this algorithm are that it deals with verbs in their morphological cases, extracting their possible structures and some of their morphological data. This algorithm yielded highly accurate results after being tested on a sample of vocabulary and text. Furthermore, the extracted data is completely error-free. KEYWORDS language processing, match, pattern, regular expression, root, word structure


Artificial Intelligence Services at Academic Libraries in Tanzania: Awareness, Adoption and Prospects

Abstract: Libraries use various information management systems for organizing, packaging, and repackaging services to mention a few. The reliability of these services is highly affected by several factors including; an increase in the number of users, limited resources, decentralized learning, and the emergence of digital resources. Bearing the benefits that AI technologies offer to libraries including cost-effective operations, improved services, and timely analyses, research to investigate the awareness and prospects have been conducted in various countries. Several studies investigated the level of adopting AI technologies for effective services in academic libraries for a particular study area. The general observation from such studies indicates that the level of AI adoption and awareness varies depending on a particular country under investigation. Therefore, due to the diversification of awareness and adoption levels from various areas, it is vital to investigate it in the Tanzanian context. This study aims to investigate the level of awareness and prospects of AI adoption in Tanzanian academic libraries using a qualitative approach in which 36 librarians from 7 giant and widespread higher learning institutions (HLI) are interviewed. The findings reported in this study indicate that the level of awareness is high (68.3%) while that of adoption is low (23%). Furthermore, the findings imply that the demand and readiness for the adoption of AI among librarians is very high. Therefore, this work provides new information to librarians, HLIs’ management, and policymakers regarding the trend of artificial intelligence adoption in academic libraries. The findings reported in this paper can be used by librarians and management to align their plans toward AI adoption for effective and better service delivery.


WIP: The Importance of Freehand Sketching and Technical Drawing

Abstract: Abstract Work in Progress Paper: The Importance of Freehand Sketching and Technical Drawing Techniques Abstract This Work in Progress Paper discusses the importance of freehand sketching and technical drawing skills for students studying computer aided drawing. Freehand sketching is the process of drawing without any measuring instruments and is accomplished via pencil and eraser only whereas technical drawing is drawing by use of drafting equipment including t-squared, drawing triangles, and French curves. Freehand sketching has been shown to be important because it connects students' hand movements to their mental thinking ability. In addition, freehand sketching is a quick method of communicating and idea in a graphical format. With the advancement of computer technology, many universities have deemphasized freehand sketching and technical drawing skills over computer-aided design methods. This research aims to show how freehand sketching and technical drawing techniques taught to first-year civil engineering students impacts the students' understanding of computer-aided design (CAD). The objective of this research is to determine the students' perception of the helpfulness of the freehand and technical drawing and correlate these perceptions to the understanding of CAD techniques. Current as well as former students who have completed a first-year civil engineering graphics course will be surveyed to determine the effectiveness of this process.


Digital pathways in education: analysis of the evolution and future trends of educational technologies among teachers in the basic education network

Abstract: This study delves into the intricate issue of implementing educational technologies in primary education, with a detailed focus on the challenges and opportunities that arise in this ever-evolving landscape. The main objective of the research is to analyze the past trajectory and future trends of educational technologies, with a special emphasis on the pivotal role played by teachers in the teaching-learning process. The adopted methodology involved a comprehensive literature review, allowing for a meticulous analysis of the extensive existing literature on the subject. The results obtained reveal that, despite the hurdles faced, such as inadequate technological infrastructure and resistance to change from some educators, educational technologies offer significant opportunities for innovation in the field of education. Furthermore, the analysis highlighted the pressing need for integrated strategies addressing both teacher training and curriculum adaptation to effectively incorporate these technologies. In conclusion, this study underscores the vital importance of a balanced and well-planned approach to the integration of educational technologies in primary education. This entails recognizing and addressing the challenges that arise while capitalizing on the opportunities offered by these tools, with the ultimate goal of enhancing the quality of education and student development. Therefore, the research emphasizes the need for a strategic and collaborative approach to maximize the positive impact of technologies in education.


The role of digital information and communication technologies in modern education

Abstract: This research examined the role and impact of Information and Communication Technologies (ICTs) in modern education, addressing how these technologies transform teaching and learning. The central problem focused on the integration and effectiveness of ICTs in the educational environment. The main objective was to investigate the contributions, challenges, and future trends of ICTs in education. A literature review methodology was employed, examining academic sources and specialized publications. The findings indicated that ICTs offer significant opportunities to personalize learning, enhance interactivity, and facilitate educational access. However, challenges related to infrastructure, teacher training, and access equity were identified as significant barriers. The analysis underscored the need for inclusive educational policies and continuous training for educators. It is concluded that ICTs are fundamental in contemporary education, but their effective implementation requires attention to infrastructure issues, teacher training, and equity.


Design, Analysis, and Fabrication of A 3D Printed Violin for the Public

Abstract: Abstract The goal of this project was to develop a high-quality violin that can be 3D printed and assembled at a low cost. This low-cost alternative to the traditional wooden violin would allow financially underprivileged people to participate in, and experience music and music education. Given the increase in accessibility of 3D printers, we focused on modeling a violin that can be 3D printed and constructed with off-the-shelf parts and a non-commercial 3D printer. During the creation of the model, we performed simulations of the governing physics and physical analyses. These physical analyses involved testing of the constructed instruments, measuring the sound acoustics and quality compared to a traditional violin. The structural and frequency simulation analyses allowed us to modify our prototypes without the need for numerous physical models as we experimented with the violin's shape. Our target was to build the violin for less than $50, making it nearly a quarter of the cost of an in-expensive traditional violin. Our methodology started with measuring a traditional violin and using the measurements to develop 3D printed violins using different modeling methods. The first method was a mesh model created in Blender, which allowed for the complex curves and angles of a violin to be created and modified. The second modeling method used SolidWorks, which is a traditional CAD modeling environment, and involved creating and modifying solid geometries to create the violin. Once printed, we were able to use sensors to measure and collect data on the instrument, such as the strength of the glue bonds, as well as its waveform, sound, and frequency responses. Structural and frequency analyses of the virtual models were conducted and compared to those available from the literature for traditional wooden violins. An analysis was also conducted to see how the acoustic pressure, tension and frequency change over time for a given instrument and its material composition. We revised the designs through multiple iterations to accommodate issues we encountered during the design and testing process. These modeling adjustments included the placement and alignment of the strings, the body wall thickness and infill to better match that of a traditional violin as well as creating more support for the neck of the violin due to material differences. Additionally, the model was modified so that the 3D printed violin's waveforms were close to its wooden counterpart. The prototype models were tested and assessed by independent music professors.


Abstract: Abstract The Sweet Sounds of Coding: promoting digital inclusion via remote instruction of introductory Python in a musical context Sommer Anjum*, Maria Jantz*, James Churilla, Kirk Holbrook, Steven Abramowitch * These authors contributed equally to this work. Keywords: pre-College, race/ethnicity, Socio-Economic Status, computer science Students from underrepresented groups begin to drop out of the pathway to STEM careers at an early age due to factors such as limited community-based exposure to STEM programming, and the scarcity of representative role models in STEM careers. These issues are compounded further as many universities do not equitably share resources to support surrounding communities. Introducing coding to pre-college students enriches their exposure to STEM fields and equips them with programming skills that are in high demand for jobs on both a local and global scale. Therefore, to promote STEM engagement for local youth, our chapter of the Biomedical Engineering Society, in partnership with the University of Pittsburgh Community Engagement Center in the Hill District (CEC), introduced coding classes that allow students to produce their own musical compositions. Recent studies suggest that integrating topics that are traditionally decoupled from coding, such as music, into a coding course can improve attitudes toward computing for underrepresented students (Freeman et al, 2014). This attracts students who may not be interested otherwise and empowers them to express themselves in a fun way while learning practical skills. In our workshops, we teach 4th-12th grade students to code in Python using an adapted curriculum for the EarSketch platform developed by Georgia Tech. Due to the move to virtual learning in recent months, it is important to note that EarSketch is an online platform that requires no additional setup. This made our program more accessible because students and their families did not need to worry about transportation to and from a venue or equipment beyond a basic computer with internet access. If necessary, the CEC provided students with a wifi hotspot, a laptop, and a stipend as a way to provide both technological access and to incentivize participation. Our team of University of Pittsburgh students and faculty has experience in coding, music, and outreach in local schools, making us a multidisciplinary team capable of meeting the needs of students in our program. This program began with seed grant funding that was an outgrowth of the CEC's STEAM Studio planning grant, supported by the PNC Foundation, which invited collaborators from 24 university units and 20 community-based organizations to co-create a vision for addressing the digital divide in the Hill District. We ran two workshops, which both focused on underrepresented students in the Greater Hill District and Homewood, communities that have historically faced significant socioeconomic challenges. The median household income in the Hill District is significantly lower than the City of Pittsburgh's. In Bedford Dwellings, the Hill District's oldest and last remaining public housing community, the median household income is approximately $16,000, and over 70% of children live below the poverty line. The first workshop, in the Fall of 2020, was with two classes of 4th graders at Miller African Centered Academy (Miller ACA), an elementary school within walking distance of Bedford Dwellings. Miller ACA has a 99% minority population and 100% of families qualify for free and reduced lunch. In order to most effectively reach this young audience, we began by teaching algorithmic thinking, including "robot pictionary" with students giving us instructions to draw a specific object. In the second workshop, in the spring of 2021, we taught 9th-12th graders from the Greater Hill District. In both workshops, we introduced the students to variables, functions, how to structure their programs, and more computer science concepts, with pedagogy adapted to each level. As much as possible, we worked in small groups in virtual breakout rooms so that instructors had maximal interaction with students. This was critical in the virtual environment so students could share their screens and walk through errors or demonstrate their accomplishments. During these workshops, we covered examples of Black engineers and computer scientists through history to provide historical background and representative role models. Along the way, we provided constructive criticism and encouraged students to pursue careers that involve programming. For students who were particularly interested in programming, we challenged them during class, while uplifting those who were less interested. We encouraged students to explain concepts to each other to enhance their technological communication skills. We worked with a set of 32 students in the fall in partnership with Miller ACA's science teacher, Mr. Churilla, who incorporated these classes into his weekly instruction. He was eager to provide opportunities for his students that were innovative, engaging, and related to STEM fields. Each week, he detailed how his students would excitedly anticipate the coming lesson and observed their growth in math and computing concepts. Students shared in the chat that their beats were "fire" and were excited to share their screens with their classmates. At the high school level, more complicated coding concepts such as different variable types, conditional statements, and loops were introduced, Additionally, we discussed aspects of musical composition such as measures, time signatures, and navigating a digital audio workstation. In a post-course survey, the majority of students expressed interest in taking another class like this and all students said that they could see themselves using coding in the future. Instructors met after every class session in order to share feedback and find ways to adapt the course to student needs in the next session. We had many instructors who wanted to remain involved. Instructors felt that teaching this class was a good learning experience for them, and this experience invigorated their interest in being involved with supporting underserved communities. This mutually beneficial relationship between the university and the community is an excellent model to sustain this type of program moving forward. Due to the web-based nature of EarSketch, students can make music on their own time to share with friends and family using the skills they learned in the course. We plan to keep this program running in the future as part of a series of events supported by the CEC to promote digital literacy and equity in the Pittsburgh area.


Recommendation Engine using Adamic Adar Measure

Abstract: Abstract ABSTRACT In recent years, recommendation engines have gained a lot of success on many online giant commerce and entertainment platforms. Recommending similar products that users will like using the user's past behavior is a challenging problem especially because of the unpredictable nature of people's likes and dislikes. It also involves a guess about the future based on something that the user has never seen which makes it that much harder to predict mainly because people's tastes change all the time. What we can do is try to estimate those values as best as we can using the Adamic Adar measure by creating nodes and finding similarities between those nodes. Unlike most of the existing recommendation systems that use either collaborative filtering or content-based filtering to generate recommendations, this paper explores a slightly different approach by creating node pairs consisting of common neighbors but with a lower degree and calculating the Adamic Adar Coefficient of those two nodes. Adamic Adar Coefficient is a measure that is used to calculate the closeness of two nodes based on their common neighbor. This paper describes a recommendation engine built that can be used to predict similar items when a user is browsing an eCommerce, music or movie platform based on the user's behavior. It takes in the item's features such as description, price, title, ratings, etc., and creates nodes for each word to find commonalities between those nodes. It then generates nodes with the highest Adamic Adar Coefficient which will result in the items that are close in characteristics to the currently viewed item by the user. Adamic Adar Coefficient: If I and J are two nodes, the Adamic Adar Coefficient of I and J would be calculated as Whereas N(node) is a function that returns the set of neighboring nodes


Full Paper: Integrating the iPad Into the Engineering Classroom

Abstract: Abstract Beginning in 2016, the University embarked on the Digital Citizen Initiative with the goal of including an iPad as standard issue for all students and faculty. From 2016 to 2019, pilot programs were conducted to test the use of the iPad in both classroom and laboratory settings. In the Fall of 2019, all incoming first-year students at the University were issued an iPad along with an Apple pencil and protective cover. Laptop-, tablet-, or Chromebook-per-child programs are becoming more and more prevalent in P-12 environments. As today's students matriculate to college, they expect a technology-rich learning environment that is an improvement from what they experienced in their primary and secondary education. Mobile computing has become more commonplace in many industry and professional settings. As we work to educate future professionals, it is important that the on-campus experience is preparing students for life after the University. The file management improvements to the iPad combined with the creation of an active stylus, the Apple pencil, have created a powerful mobile productivity platform that can be useful in the traditional ECE classroom. The authors performed a pilot study on the use of the iPad in multiple ECE courses, including Circuits and Electronics. Initially, the iPads were used to perform homework digitally, allowing for easy integration with the University's learning management system (LMS). Based on the results of this pilot, the use of the iPad has been expanded to include the use of the iPad for circuit simulation, hand sketching, and digital lab notebooks. The authors have also used the iPad for assignment grading, again allowing for improved assignment feedback, modernized grading workflows, and efficient integration with the University's LMS. In this paper, insights into the use of the iPad in the ECE classroom will be discussed, including student perceptions from the pilot program. Example student work will be included to compare work completed on the iPad to work traditionally composed on paper. The authors' thoughts on further integration possibilities will also be discussed. Ultimately, the iPad has been seen as a value-additive augmentation to the ECE classroom by many students.


Abstract: Abstract Research for Undergraduates experiences from various academic institutions Abstract The opportunities for doing research by undergraduate students help them to pursue further career interests in Science, Technology, Engineering, and Mathematics (STEM) field. Involving undergraduate students in research will make them take more interest in studies and motivate them to pursue graduate degrees. The undergraduate students at the XYZ institution can get involved in research usually conducted in the summer period for an 8-week or 10-week duration. The name of the program is "Multi-campus Research Experience for Undergraduates" (MC-REU) and it is administered by the College of Engineering at the XYZ institution. There are 21 campuses functioning under the system of XYZ University in Pennsylvania. To enhance the research opportunities for the undergraduate students in most of the campuses, the program MC-REU was started. The MC-REU program supports University undergraduate students to conduct research with the University faculty. Selected students will complete their proposed research project in conjunction with two University faculty members — one from the student's home campus and one based at University Park (Main Campus). The goals of the MC-REU are (1) to promote undergraduate students participating in research early in their academic program to broaden their education and increase their chances of entering graduate studies, and (2) to promote mutual awareness and collaboration among faculty across the Commonwealth. In the same way, a few more educational institutions are also heavily involved with Undergraduate Research, particularly during summer semesters. Those undergraduate students that are involved with undergraduate research were paid scholarships to cover boarding and lodging. This paper will explain some of the REU experiences from different educational institutions and draw conclusions based on the students' feedback. Students will not take any courses towards their academic accomplishments during the summer in which they are engaged in research. They will work 40 hours per week for the 10-week or 8-week period. During the 8-week or 10-week time period, they are also compensated with financial help.


Abstract: Abstract Promoting quality STEM Education in the time of Social Distancing The pandemic produced by COVID-19 has forced a radical change in the strategies and methodologies used to share and transmit knowledge. With the closure of the Schools / Universities, the educational process has been radically transformed from one day to the next. STEM education, based on collaborative work, inquiry, experimentation, problem-solving, and project generation, encounters many obstacles in the present situation: students do not have access to laboratories, materials, and other essential supplies to implement an educational process of quality. The Institution has developed alternative ways to promote quality STEM education for our students when learning from their homes with the present limitations. These activities allow students to explore phenomena through "virtual" labs (not simulations), project generation using a supply of pre-existing materials (constraints that any project has at any time), and in particular, the development of projects based in easy to find at home materials. In this paper, the author will present three strategies to promote STEM education through remote learning: 1) Laboratory activities for college-level students 2) Hands-on activities for high-school students through informal education settings, and 3) Activities for the public at large through social media (Facebook live and YouTube) and sponsored by public institutions. The author implemented these strategies successfully in a university setting, in Out of School Time (OST) programs in the City for nonprivileged student groups, and with multiple public institutions partners in different countries.


Mixed Reality Game for Active Geotechnical Engineering Learning

Abstract: Abstract Our fully-integrated mixed reality game system, called multiphysics enriched mixed reality for integrated geotechnical education (MERGE), focuses on improving student education in the context of geotechnical engineering. This work allows students to participate in online learning in a more inclusive way while playing a game. The game is meant as an "integrated geotechnical learning experience". By providing students visualization, collaboration, and simulation tools, we hope to promote problem-solving and improved learning. A big part of that is leveraging mixed reality technology to improve student access to lab equipment. By gamifying the learning, we also focus on improving student interest in geotechnical engineering. Due to the virtual nature of the game, everyone will have access to the same lab equipment and the same opportunity to experience the material. They are also able to progress through the game at their own pace, which helps keep the interest of the students while they are learning. Learning by playing games is not a new concept, but the potential has not been reached yet. Our goal is to provide a fulfilling learning experience while keeping the students engaged in a fun game. The MERGE platform is inspired by popular mixed reality games, allowing students to see computer-generated models superimposed into the world through the camera on their mobile device. Building on recent advancements in cyberinfrastructure, MERGE is meant to provide an attractive and motivating environment to support students' geotechnical engineering skills. Throughout gameplay, students' strategic thinking will be tested through carefully designed games that relate to geotechnical problems. Additionally, the game uses their GPS to create a map of the real world around them, giving players the ability to explore their local environment to advance their game progress. Initial gameplay mainly focuses on a lab assignment involved with the study, construction, and implementation of geothermal piles, which are special foundations that utilize the earth's temperature to heat and cool the associated building. The main contribution of this work is a discussion of the educational technology and processes behind implementing a mixed reality educational game. We provide developmental insights and educational background as well as results from preliminary game testing to inform researchers who seek to develop similar games.


Neighbor list artifacts in molecular dynamics simulations

Abstract: Molecular dynamics (MD) simulations are widely used in biophysical research. To aid non-expert users, most simulation packages provide default values for key input parameters. In MD simulations using the GROMACS package with default parameters, we found large membranes to deform under the action of a semi-isotropically coupled barostat. As the primary cause, we identified overly short outer cutoffs and infrequent neighbor list updates that result in missed long-range non-bonded interactions. Small but systematic imbalances in the apparent pressure tensor then induce unphysical asymmetric box deformations that crumple the membrane. We also observed rapid oscillations in averages of the instantaneous pressure tensor components and traced these to the use of a dual pair list with dynamic pruning. We confirmed that similar effects are present in MD simulations of neat water in atomistic and coarse-grained representations. Whereas the slight pressure imbalances likely have minimal impact in most current atomistic MD simulations, we expect their impact to grow in studies of ever-larger systems with coarse-grained representation, in particular in combination with anisotropic pressure coupling. We present measures to diagnose problems with missed interactions and guidelines for practitioners to avoid them, including estimates for appropriate values for the outer cutoff r_l and the number of time steps nstlist between neighbor list updates.


Blockchain technology in modern accounting: A comprehensive review and its implementation challenges

Abstract: In an era where technological advancements redefine traditional paradigms, this paper delves into the transformative impact of blockchain technology on the accounting sector. The study embarks on a scholarly journey, exploring the intricate interplay between blockchain and accounting practices, with a focus on its evolution, adoption, and the challenges it presents. The aim is to unravel the potential of blockchain as a catalyst for change in accounting, examining its implications from a multi-dimensional perspective. Adopting a qualitative research methodology, the study employs an analytical framework to dissect the integration of blockchain in various accounting sectors. This approach facilitates a comprehensive exploration of the subject, ensuring a thorough understanding of blockchain's impact on accounting practices. The paper navigates through the realms of blockchain adoption, comparative analysis with traditional systems, implementation challenges, and emerging trends, culminating in a rich tapestry of insights. The findings reveal that blockchain technology is not merely an emerging trend but a paradigm shift in accounting. It offers enhanced transparency, efficiency, and security, surpassing traditional accounting systems. However, the journey towards a blockchain-dominated future is fraught with challenges, including technological integration, skill development, and regulatory compliance. Conclusively, the study posits that blockchain technology is poised to revolutionize accounting practices. It recommends a harmonious blend of technological innovation with ethical and regulatory frameworks, emphasizing the need for education and skill development in blockchain technology. The study serves as a clarion call to accounting professionals and educators to adapt to this technological evolution, envisioning a future where accounting transcends traditional boundaries and embodies trust, transparency, and transformative innovation.


Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: Not all droughts are created equal

Abstract: Abstract Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e., positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R 2 ~0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-meteorological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–Jul), causing below-average TWSA in dry season that was favourable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-meteorological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e., LST max ) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e., TWSA min ). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e., LST > LST max and TWSA < TWSA min ) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydrological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.


The pedagogical approach of grade 7 teachers in teaching the learning competency of integers

Abstract: This research addresses the intricate challenges of teaching integers in middle school mathematics. Focusing on grade 7 teachers, the study explores strategies to enhance integers' teaching-learning competency, revealing nuanced approaches employed by three teachers. Through qualitative methods, specifically in-depth interviews, the research uses an exploratory approach to unravel the underlying strategies shaping the teaching of integers. Key findings underscore integers as a formidable learning competency for grade 7 students, particularly in operations like addition and subtraction. The most effective pedagogical approach, encapsulated in the 3R — Reaching, Representing, Recommending — involves drill exercises for foundational concepts, integrating manipulatives like algebra tiles and coloured tiles, and advocating for continuous manipulative use with persistent drill exercises and integrating technology. These identified themes emphasize the need for a uniform understanding among teachers during discussions to ensure a cohesive and effective teaching strategy. Recognizing these strategies is crucial for addressing the existing learning gap and advancing grade 7 students' competency in dealing with integers. The research contributes valuable insights to the academic discourse, offering effective pedagogical practices and fostering a more robust foundation for mathematical understanding at the middle school level.


Maximum entropy inverse reinforcement learning-based trajectory planning for autonomous driving

Abstract: With the rapid advancement of autonomous driving technology, effective trajectory planning has become crucial for ensuring road safety and driving efficiency. Traditional trajectory planning methods often rely on preset rules and models, making them ill-suited for the complex and dynamic traffic environment. To address this, a Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) based trajectory planning method is proposed in this paper, aiming at learning from expert driving behaviors to infer an efficient reward function, which in turn guides decision-making and path planning for autonomous vehicles. This study begins by analyzing expert driving data to extract key state and action features. Then, the MaxEnt IRL algorithm is applied to learn the reward function underlying these features, reflecting the decision-making logic of expert drivers. The learned reward function is subsequently used to guide the trajectory planning of the autonomous driving system, generating safe and efficient driving paths. A series of experiments conducted in a simulated environment demonstrate that the MaxEnt IRL-based method proposed in this paper exhibits higher adaptability and efficiency in handling complex traffic scenarios compared to traditional trajectory planning methods.


Research on the Method of Artistic Image Restoration Based on Artificial Intelligence

Abstract: In order to improve the current inpainting algorithm for natural art images, an artistic image inpainting model is proposed by integrating artificial intelligence and embedding multi-scale attention expansion convolution. According to the uniqueness of artistic images, the network structure of DMFB is improved, and its repair model combines two plug-and-play optimization modules: extended convolution block and coordinate attention mechanism. The extended convolution module is used to capture multi-scale context information, and the coordinate attention mechanism is used to improve the remote migration ability of features of the repaired network. The combination of the two makes the repair results meet the visual visibility and semantic rationality. The experimental results show that ablation experiments are carried out to evaluate the effectiveness of each module in this model. In this paper, the hybrid extended convolution and coordinated attention mechanism are used to train the network, and the effectiveness of MADC is verified. When the expansion rate changes to 2, 4, 6 and 8, the method in this paper will lead to moderately distorted structure and fuzzy texture. On the contrary, there is no coordination attention. In the branch of mechanism, the output of this method shows texture defects and discontinuities. By using these two branches, the method in this paper has achieved good results in structure and texture. The quantitative evaluation of branches without coordinated attention mechanism is given when the expansion rate is fixed at 2, 4, 6 and 8. Conclusion: The results restored by this method are more consistent with the real image in human senses, and the objective evaluation of PSNR, SSIM and MSE has also been improved to some extent.


Research on Intelligent Assistant Teaching System for National Musical Instruments Based on PSS

Abstract: In order to improve the efficiency and quality of the teaching service of the national musical instruments, a design method of product service system (PSS) for national musical instruments is proposed to build a tool for integrating teaching resources, autonomous practice feedback, and social interaction. The basic characteristics of the national musical instruments of the balance between the improvement and the inheritance of traditional music is summarized, the shortcomings of digital transformation of national musical instruments under the condition of smart technology are compared and pointed out, there is still room for development in precision teaching services. Based on the design method of PSS, the musical instrument products and teaching services are integrated into an overall solution, a model of teaching service and value co-creation is constructed. A service process is formed with the song as the basic teaching unit and gesture skill training as the key. The human-smart system collaboration mode is analyzed. The smart product subsystem and teaching service subsystem are established. The relationship among the stakeholders is discussed. The prototype scheme of the smart assistant teaching system of Pipa based on this method is built up to verify.


The Design of Undergraduate Students’ Learning Product Service System Based on User Behavioural Persuasion

Abstract: The article investigates the application of user behavior and persuasive design theories in product design and develops a design model based on user behavioral persuasion. The research collects data on study habits observed from undergraduate students and subsequently examines the patterns and variations within this cohort. The investigation leads to the development of a product service system that integrates motivational education to encourage undergraduate students to cultivate positive study habits and enhance the overall quality of their learning experience. The study builds a logical and efficient design model and hits the pain points of undergraduate students learning behaviors, delivering effective design solutions and improving the match between product strategies and design requirements by combining with motivational education.


Research on Improving the Quality of Japanese Chinese Machine Translation Based on Deep Learning

Abstract: In order to improve the quality of Japanese-Chinese machine translation, the research on improving the quality of Japanese-Chinese machine translation based on deep learning is put forward. The maximum entropy model is used to effectively classify Japanese possible states and passive states, and then the features of Japanese possible states and passive states are effectively integrated into the log-linear model to improve the translation model, so as to improve the accuracy of selecting possible states and passive states in translation rules. The experimental results show that this method can effectively improve the translation quality of Japanese possible sentences and passive sentences. On large-scale Japanese Chinese materials, the maximum translation BLEU value can be increased from 41.50 to 42.01, and the overall intelligibility of translation results has been improved by 2.71% in manual evaluation. Conclusion: This method can improve the accuracy of rule selection.


Spillover Effect of Network Public Opinion on Market Prices of Small-Scale Agricultural Products

Abstract: Network public opinion plays a crucial role in the behavior and decision making of various stakeholders, including farmers, middlemen, and consumers. It also affects the price fluctuations of small-scale agricultural products. Understanding the transmission path and spillover effect of network public opinion on the price fluctuations of these products is essential for ensuring their sustainable development and price stability. This paper selects the monthly data of network public opinion and related market prices of small-scale agricultural products from January 2014 to December 2021, constructs a network public opinion value through the sentiment classification results of deep learning models, and uses the trivariate VAR-BEKK-GARCH(1,1) model and spillover index model to study the spillover effect and spillover index of network public opinion on the market prices of small-scale agricultural products (national average price and origin price). The results show that: (1) There is a bidirectional volatility spillover effect between public opinion sentiment and the market prices of small-scale agricultural products. Additionally, this two-way volatility spillover effect is also evident between the average market prices and the origin prices of these commodities. (2) The influence of network public opinion on the market prices of small-scale agricultural products is substantial, with the spillover index being more pronounced for origin prices than for national average prices and reaching its zenith earlier. Consequently, based on these results, recommendations are provided to adapt planting and inventory strategies, enhance vigilance towards price risk transmission amongst small-scale agricultural product markets, and improve the comprehensive information platform encompassing the entire industry chain.


Success Factors in Management of IT Service Projects: Regression, Confirmatory Factor Analysis, and Structural Equation Models

Abstract: Although there have been some studies on the success factors for IT software projects, there is still a lack of coherent research on the success factors for IT service projects. Therefore, this study aimed to identify and understand the factors and their relationships that contribute to the success of IT service projects. For this purpose, multivariate regressions and structural equation models (SEMs) were developed and analyzed. The regression models included six project management success criteria used as dependent variables (quality of the delivered product, scope realization and requirements, timeliness of delivery, delivery within budget, customer satisfaction, and provider satisfaction) and four independent variables (agile techniques and change management, organization and people, stakeholders and risk analysis, work environment), which had been identified through exploratory factor analysis. The results showed that not all success factors were relevant to all success criteria, and there were differences in their importance. An additional series of exploratory and confirmatory factor analyses along with appropriate statistical measures were employed to evaluate the quality of these four factors. The SEM approach was based on five latent constructs with a total of twenty components. The study suggests that investing in improving people’s knowledge and skills, using agile methodologies, creating a supportive work environment, and involving stakeholders in regular risk analysis are important for project management success. The results also suggest that the success factors for IT service projects depend on both traditional and agile approaches. The study extensively compared its findings with similar research and discussed common issues and differences in both the model structures and methodologies applied. The investigation utilized mathematical methods and techniques that are not commonly applied in the field of project management success modeling. The comprehensive methodology that was applied may be helpful to other researchers who are interested in this topic.


ESARS: A Situation-Aware Multi-Agent System for Real-Time Emergency Response Management

Abstract: This paper describes ESARS, a real-time situation-aware social media- enabled emergency situation alert and reporting system, as a decision support system built on multi-agent software design architecture for emergency situation management. The impact of an incident or disruption due to the incident could be minimized by implementing real-time intervention strategies that involve event monitoring, detection and situation identification via classification and prediction, notification, visualization and reporting that culminate in providing emergency support within time. The nature of agent behavior, which is autonomous, proactive and cooperative, makes them a suitable method for the design and deployment of a dynamic system of this nature. The system relies on historical and streamed real-time geolocation-enabled Twitter data stream for the target emergency events to provide decision-makers with dynamic, comprehensive, and timely information specific to the emergency situation.


A Syntactic Proof of the Decidability of First-Order Monadic Logic

Abstract: Decidability of monadic first-order classical logic was established by Löwenheim in 1915. The proof made use of a semantic argument, but a purely syntactic proof has never been provided. In the present paper we introduce a syntactic proof of decidability of monadic first-order logic in innex normal form which exploits G3-style sequent calculi. In particular, we introduce a cut- and contraction-free calculus having a (complexity optimal) terminating proof-search procedure. We also show that this logic can be faithfully embedded in the modal logic T.


Ant Colony Optimization for Efficient Distance and Time Optimization in Swarm Drone Formation

Abstract: One of the challenges in swarm drone formation is achieving fast and effective formation with optimal distances. In this paper, we propose a swarm drone formation approach utilizing Ant Colony Optimization (ACO) for achieving it. We conducted simulations involving the formation of three or more drones, aiming to identify the best formation based on distance, acceleration, and time criteria. Simulation results demonstrate that formation time is significantly reduced when employing ACO optimization compared to non-optimized methods. Additionally, the optimized formations exhibit shorter inter-drone distances compared to non-optimized formations. By implementing this approach, swarm drone formations can be rapidly established with minimized distances, resulting in substantial battery savings. The simulation encompassed various patterns formed by 3, 5, 10, 15, 20, and 25 drones. The findings indicate that the approach can reduce formation time by varying degrees, ranging from 12% to 51%, across 66% of the conducted experiments, notably for patterns created with a substantial drone count. The degree of diversity observed among the proposed solutions reached 60%, with minimal variances of less than 1% for each.


Analyzing Facebook Mobile Usage: Efficacy and ESL Learners’ Writing Proficiency

Abstract: Facebook has leveraged rapid technological and societal changes over the past decade and has emerged as the largest social networking platform. However, research on Facebook has been limited, particularly when examining its potential to improve English as a second language (ESL) writing in comparison to a control group. The study aims to explore the morphological, syntactic, and orthographical aspects of ESL writing through an experimental group [N = 30] using Facebook on mobile and a control group [N = 30] with a traditional teaching approach. The experimental and control groups undergo observation through twice-weekly, in-class-focused free writing sessions for 10 weeks. The progress of the learners’ writing was tracked using pre- and post-tests to assess its impact. The analysis of variance (ANOVA) test revealed that the experimental group made more significant advancements in writing by reducing morphological, syntactic, and orthographical errors compared to the control group. These results validate previous studies that support the use of Facebook on mobile devices in ESL courses and emphasize the need for further research comparing Facebook with other writing platforms.


Convolutional Neural Network Architectures for Gender, Emotional Detection from Speech and Speaker Diarization

Abstract: This paper introduces three system architectures for speaker identification that aim to overcome the limitations of diarization and voice-based biometric systems. Diarization systems utilize unsupervised algorithms to segment audio data based on the time boundaries of utterances, but they do not distinguish individual speakers. On the other hand, voice-based biometric systems can only identify individuals in recordings with a single speaker. Identifying speakers in recordings of natural conversations can be challenging, especially when emotional shifts can alter voice characteristics, making gender identification difficult. To address this issue, the proposed architectures include techniques for gender, emotion, and diarization at either the segment or group level. The evaluation of these architectures utilized two speech databases, namely VoxCeleb and RAVDESS (Ryerson audio-visual database of emotional speech and song) datasets. The findings reveal that the proposed approach outperforms the strategy level in terms of recognition results, despite the real-time processing advantage of the latter. The challenge of identifying multiple speakers engaging in a conversation while considering emotional changes that impact speech is effectively addressed by the proposed architectures. The data indicates that the gender and emotion classification of diarization achieves an accuracy of over 98 percent. These results suggest that the proposed speech-based approach can achieve highly accurate speaker identification.


The Effectiveness of a Program Based on Blended Learning in Developing National Identity and Concepts of Intellectual Security Among Kindergarten children in the Najran Region

Abstract: The goal of this study was to determine how well a blended learning program develops national identity and concepts of intellectual security in kindergarten students in the Najran region. Blended learning is an integrated system that combines e-learning with traditional methods to guide and help the child in acquiring concepts and skills. It is one of the modern approaches based on the use of educational technology in designing new educational situations, and it has become an urgent necessity, especially because of its characteristics that make it the most suitable alternative for developing the educational system. In order to do this, a one-group quasi-experimental design was employed along with a blended learning program that was suggested, along with measures of intellectual security and national identification for kindergarteners. Twenty-seven kids made up the research sample, and they were chosen at random. The outcomes demonstrated how well the suggested program worked to help the study sample's national identification and ideas of intellectual security grow. The study made a number of recommendations, the most significant of which are to use blended learning to help children develop their thoughts and abilities and to incorporate national identity and ideas of intellectual security into educational units and kindergarten activities.


Decentralised Storage Network by Using Block-Chain

Abstract: This paper explores the intersection of network security and blockchain technology in the context of today's digital era. It begins by acknowledging the vulnerabilities and threats contemporary networks face, including data breaches and denial-of-service attacks. The paper then delves into the foundational principles of blockchain, highlighting its decentralization, consensus mechanisms, cryptographic encryption, and immutability as key attributes that can enhance network security The central idea presented is the potential of blockchain to revolutionize network security. It discusses how blockchain can be integrated into network architecture to achieve secure and transparent identity management, combat identity fraud, automate network processes through smart contracts, and enhance real-time threat detection and response. However, the paper also recognizes challenges such as scalability, interoperability, and energy efficiency when implementing blockchain in secure networks and suggests ongoing research efforts and potential solutions to make this integration more practical.


Computer-Aided Putonghua Test: Problems and Strategies

Abstract: The implementation of mandarin test is one of the most important measures to popularize Putonghua in China. With the rapid development of modern information technology, especially the effective integration of computer, network and speech recognition technology, the model of computer-aided Putonghua proficiency test was born in a new form, which provides a good opportunity to promote the standardization and modernization of Putonghua proficiency test. Based on a brief introduction of the advantages of computer-aided Putonghua test, this paper mainly discusses the typical problems in the process of Putonghua test, and then puts forward the corresponding solutions in order to better promote the development of this test.


Power System Uncertainty Modeling Based on Gaussian Process

Abstract: In order to solve the problem of low accuracy of parameter estimation in expectation maximization algorithm, a modeling method based on Gaussian component number reduction is proposed. Taking the nonparametric kernel density estimation results as the base Gaussian mixture model, the Gaussian mixture model with any number of Gaussian components can be established by reducing the number of Gaussian components by using the density-preserving hierarchical expectation maximization algorithm, which overcomes the problem that the expectation maximization algorithm has low parameter estimation accuracy when there are many Gaussian components. In order to reduce the burden of modeling calculation under large samples, a hierarchical modeling method based on time scale is proposed. In order to solve the problem of Gaussian component number combination explosion of independent random variables, a hierarchical modeling method of “combination-reduction” is proposed. The proposed method is tested by using measured multidimensional wind speed data and load data with complex distribution characteristics. The experimental results show that the Pearson and Spearman correlation coefficients of GMM constructed based on this method are very close to the sample data. The absolute value of Pearson correlation coefficient error is 0.03739, and the root mean square value of error is 0.02388. The absolute value of Spearman correlation coefficient error is 0.11693, and the root mean square error is 0.05797. Conclusion: The accuracy of the proposed method is significantly better than that of Gaussian mixture model and Copula function method based on expectation maximization algorithm.


The Theoretical Merits and Practical Challenges of Adopting an ESAP Methodology in Teaching ESP, with an Emphasis on the Pragmatic Viability of an EGAP Approach

Abstract: This article vividly exhibits and distinguishes the distinctions between teaching ESAP and EGAP. It defines the two approaches and shows which teaching approach is the realistic option according to the author's point of view. The author hence proceeds with elucidations on why EGAP is the realistic option and elaborates on the five roles of an ESP teacher. In addition, the author mentions a study that was conducted in Vietnam. The study was based on a systematic literature review of current research on English for Specific Academic Purposes (ESAP) and English for General Academic Purposes (EGAP) teaching approaches, and 15 relevant studies were synthesized. Some of this research showed positive results, while others showed negative results of the approaches. The final results of this study show that ESAP learning was hard in practice. It shows how EGAP is considered a course to equip and prepare students for ESAP courses. It prompts EGAP through group work and suggests it will make students feel less intimidated. The weaker students will learn from the stronger students in an EGAP group working class. It shows one of the relevant study's responses and teachers who think EGAP is significant in teaching before students can enter ESAP courses. Scaffolding is considered a significant role teachers need to ensure learners have reached their needs in both approaches. In the latter part of the article, the author mentions her experience teaching ESAP at Kuwait University, specifically at the College of Engineering. The authors show what difficulties they have gone through and how they managed to teach at this college.


Effect of Knowledge Acquisition on Organizational Performance

Abstract: In today's ever-evolving and fiercely competitive business environment, the acquisition, assimilation, and effective utilization of knowledge have emerged as quintessential drivers of organizational success. Knowledge acquisition, which encompasses not just the accumulation of data but also the cultivation of tacit and explicit knowledge, plays a pivotal role in fuelling innovation, facilitating informed decision-making, and fostering adaptability. This study examines the impact of knowledge acquisition on organizational performance, with a null hypothesis guiding the inquiry. The research design adopted was survey research, A purposive technique was utilized. A sample size of 412 respondents from selected manufacturing companies in Oyo State, Nigeria, formed the study's subject group. Data collection was executed through a questionnaire, with a rigorous validation process. The findings of this study indicate a significant positive effect of knowledge acquisition on organizational performance. Enhanced knowledge acquisition is shown to empower organizations to achieve sustainability by developing new competencies that cascade across different organizational levels. This underscores the vital role of knowledge acquisition in positively influencing organizational performance. It was recommended that organizations invest in structured knowledge acquisition processes, mentorship programs, and knowledge management systems. Fostering a culture of continuous learning and knowledge sharing should also be a top priority, as these measures are poised to yield substantial enhancements in overall organizational performance and long-term sustainability.


Crossing the threshold of material and symbolic entry to school represents the possibility of suspending social inequality

Abstract: This paper presents a theoretical research into the role of school in the modern western society of the 21st century characterized by social inequality.It takes as a central reference of theoretical analysis the contributions of some contemporary educators of relevance such as Philippe Meirieu, Jan Masschelein, Maarten Simons and Gert Biesta.The conceptual categories selected are those linked to the theory of school culture, namely: material school culture in its space-time dimension, school culture symbolic, teaching, learning and educational research.In this sense, a conceptual review of the role of teachers (from their teaching and research) in this unequal social structure is realized in terms of the possibility that the school will again be the space-time recovery of the right to education as a public and national good.It is understood that school can restore its democratizing state function if it manages to restore free time as the one necessary for people to succeed in free, participative and cooperative education.


A Tracer Study of the 2019 Graduates of Bachelor of Science in Industrial Technology

Abstract: The success of any school in achieving its basic purposes is determined by the adequacy and quality, and relevance of the educational programs it provides. The objective of this study was to determine the status of employment of the respondents; the length of time in looking for a job; their reason/s for the delay of employment or unemployment; the factors that facilitated in getting a job; and the level of their assessment on the quality and relevance of the education they obtained at ISU Ilagan Campus. Most of the traced BS in Industrial Technology graduates batch 2019 started and completed their schooling within the prescribed period to finish the degree. However, some of them may have started out late or lagged behind their studies as indicated by the presence of significant number of respondents within the age range over the prescribed period of their course. Most of them were female and are still single. Most of them were took professional subjects for education and some of them were already LET passers.


THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DECISION MAKING: A COMPREHENSIVE REVIEW

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize decision making across various domains. This research article explores the impact of AI in decision making and its implications for individuals, organizations, and society. The article begins by providing an overview of AI and its key components, such as machine learning and natural language processing. It then discusses the role of AI in enhancing decision-making processes by automating tasks, augmenting human capabilities, and providing data-driven insights. The article highlights the benefits of AI in improving decision accuracy, efficiency, and scalability, while also acknowledging the challenges and risks associated with its implementation. These challenges include ethical considerations, biases in AI algorithms, and potential job displacement. The article further explores the importance of transparency, accountability, and interpretability in AI decision-making systems. Additionally, it discusses the role of human-AI collaboration and the need for interdisciplinary approaches to ensure the responsible and ethical deployment of AI in decision making. Drawing on case studies and empirical research, the article provides concrete examples of how AI is transforming decision making in various fields, such as finance, healthcare, and transportation. Finally, the article concludes by discussing future directions and recommendations for policymakers, organizations, and individuals to harness the full potential of AI in decision making while addressing its ethical, social, and economic implications. KEYWORDS: Artificial Intelligence, Decision Making, Machine Learning, Automation, Augmentation, Ethical Considerations, Bias, Transparency, Human-AI Collaboration, Responsible AI.


Meeting Summarization: A Survey

Abstract: Abstract: In an era characterized by an incessant influx of information, the demand for efficient knowledge extraction tools has become paramount. This research introduces a Meeting Sum- marizer, a cutting-edge system that amalgamates speech recog- nition and natural language processing (NLP) to autonomously distill salient information from recorded meetings. The primary objective of this research is to alleviate the cumbersome process of manual meeting summarization by harnessing the capabilities of advanced machine learning techniques tailored to audio data. The Meeting Summarizer leverages state-of-theart speech recognition algorithms to transcribe spoken content into textual form, laying the foundation for subsequent NLPbased analysis. Through the integration of deep learning methodologies, the system discerns key discussions, identifies critical components, and extracts context from meeting transcripts. The synergy of speech recognition and NLP empowers the system to comprehend linguistic nuances, enabling it to adapt to diverse meeting contexts.


Yoga Guide: Yoga Pose Estimation Using Machine Learning

Abstract: Abstract: A deep learning model is proposed which uses convolutional neural networks/LR algorithm for yoga pose identification along with a human joints localization model followed by a process for identification of errors in the pose for developing the system. After obtaining all the information about the pose of the user the system gives feedback to improve or correct the posture of the user. we propose an improved algorithm to calculate scores that can be applied to all poses. Our application is evaluated on different Yoga poses under different scenes, and its robustness is guaranteed.


The Long-Run Effect of E-commerce on Economic Growth in Saudi Arabia

Abstract: This study aims to examine the long-term impact of e-commerce on economic growth in Saudi Arabia using the Cointegration model. The study employs a theoretical and empirical approach by reviewing previous studies to identify some key variables which are the merchandise trade ratio, the number of mobile phone subscriptions, the percentage of internet users and mobile phone subscribers in the population, and the ratio of communication and computer service imports, using time series data from 1992 to 2022 collected from Saudi Central Bank and the World Bank's, and employing Cointegration model for analysis. Results of the study indicate a positive and statistically significant impact of the merchandise trade ratio and the number of mobile phone subscriptions on GDP in the long run. However, the study finds that a high statistical correlation between GDP and the percentage of internet users and mobile phone subscribers in the population does not positively impact economic growth. Additionally, the study found that the ratio of communication and computer service imports no longer affects GDP and economic growth in the long run. These findings first suggest that e-commerce can help diversify the national economy and increase economic growth rates. Second, highlight the need for technology and its use to be directed toward productive purposes. The government and regulators are encouraged to increase investment in e-commerce and facilitate the growth procedures for e-commerce companies.


Pengaruh Sistem Informasi Akuntansi Manajemen terhadap Kinerja Manajerial di Moderasi oleh Ketidakpastian Lingkungan pada PT Perkebunan Nusantara IV (Persero) Medan

Abstract: The aim of this research is to test and analyze the management accounting information system for managerial performance moderated by environmental uncertainty at PT Perkebunan Nusantara IV (Persero) Medan directly and indirectly. The approach used in this research is a causal approach. The population in this study was all employees of PT Perkebunan Nusantara IV (Persero) Medan. The sample in this study used the Slovin formula so that there were 80 employees of PT Perkebunan Nusantara IV (Persero) Medan. Data collection techniques in this research used documentation, observation and questionnaire techniques. The data analysis technique in this research uses a quantitative approach using statistical analysis using Auter Model Analysis, Inner Model Analysis and Hypothesis Testing. Data processing in this research uses the PLS (Partial Least Square) software program. The results of this research prove that the accounting information system directly influences managerial performance. Indirectly, environmental uncertainty cannot moderate the influence of management accounting information systems on managerial performance


Analysis Of High Job Satisfaction And Employee Engagement With High Employee Turnover: A Curious Case Of Edutech

Abstract: This stuldy elxplorels thel paradoxical phelnomelnon at edutech, whelrel thelrel is a simulltanelouls occulrrelncel of high elmployelel tulrnovelr alongsidel high lelvells of job satisfaction and elmployelel elngagelmelnt. Thel relselarch aims to ulncovelr thel delelpelr managelrial aspelcts contribulting to this discrelpancy, offelring insights and stratelgiels to managel elmployelel tulrnovelr whilel maintaining a positivel work elnvironmelnt. Thel stuldy elmploys a pulrelly qulalitativel melthodology, celntelring on thel sulbjelctivel elxpelrielncels and pelrcelptions of edutech elmployelels. Thel collelction of primary data involvels condulcting in-delpth intelrvielws with a pulrposivel samplel of approximatelly 50 elmployelels from diffelrelnt divisions. Thel objelctivel is to captulrel thel intricatel and mulltifacelteld aspelcts of job satisfaction, elmployelel elngagelmelnt, and tulrnovelr intelntions.Thel stuldy's concelptulal framelwork is celntelreld on threlel primary variablels: job satisfaction, elmployelel elngagelmelnt, and tulrnovelr intelntion. Thel stuldy of job satisfaction involvels elxamining elmployelels' pelrcelptions of thelir carelelr advancelmelnt, relmulnelration, pelrks, and thel ovelrall working conditions. Thel concelpt of elmployelel elngagelmelnt is analyzeld baseld on factors sulch as elmotional attachmelnt, elnelrgy, commitmelnt, and absorption in work. Thel invelstigation of tulrnovelr intelntion foculsels on elmployelels' elxprelsseld probability of lelaving thel company, with a particullar elmphasis on both intrinsic and elxtrinsic motivations.Thel qulalitativel melthodology elnablels a thoroulgh invelstigation of psychological, elconomic, and delmographic factors that impact thelsel variablels. Elmployelels' pelrsonal aspirations, alignmelnt with organizational objelctivels, pelrcelptions of compelnsation, profelssional growth opportulnitiels, and delmographic attribultels likel agel and lelngth of selrvicel arel scrultinizeld. Thel stuldy's robulstnelss liels in its capacity to captulrel thel intricatel and frelqulelntly ellulsivel aspelcts of thel work elxpelrielncel that qulantitativel melthods might fail to considelr.Thel stuldy's findings delmonstratel a complelx rellationship beltweleln job satisfaction, elmployelel elngagelmelnt, and tulrnovelr intelntions. Althoulgh elmployelels at edutech elxpelrielncel positivel job satisfaction and high lelvells of elngagelmelnt, thelrel arel ulndiscloseld factors that arel caulsing thel high tulrnovelr ratels. Notablel factors incluldel ulnrelalizeld profelssional ambitions, an imbalancel beltweleln work and pelrsonal lifel, appelaling elxtelrnal job prospelcts, and intelrnal problelms within thel organization. Thel relselarch elmphasizels thel significancel of comprelhelnding thel elxtelnt and rangel of thelsel factors, which, delspitel not beling immeldiatelly elvidelnt, grelatly impact elmployelels' choicels to delpart.Thel stuldy elmphasizels thel crulcial importancel of proficielnt commulnication, compassionatel managelrial assistancel, and a fostelring organizational culltulrel in influlelncing elmployelel attituldels and choicels. Thel statelmelnt impliels that edutech shoulld adopt stratelgiels that tacklel both concreltel factors, sulch as compelnsation, and intangiblel ellelmelnts, sulch as relcognition, carelelr advancelmelnt, and maintaining a helalthy work-lifel balancel.To sulmmarizel, this qulalitativel stuldy offelrs a deltaileld and thoroulgh comprelhelnsion of thel factors influlelncing elmployelel tulrnovelr in thel contelxt of high lelvells of job satisfaction and elngagelmelnt. This stuldy elnhancels thel elxisting litelratulrel on hulman relsoulrcels managelmelnt by providing a clelar ulndelrstanding of thel intricatel dynamics beltweleln thelsel factors within an organizational contelxt. Thel findings and sulggelstions obtaineld from this stuldy arel elxtrelmelly valulablel for HR profelssionals, elxelcultivels, and acadelmics, providing a morel profoulnd comprelhelnsion of elmployelel attrition and its complelx factors in a bulsinelss seltting.


Produktivitas Karyawan Ditinjau dari Lingkungan, Kepemimpinan dan Disiplin Karyawan Konveksi Nik’s Collection Surakarta

Abstract: The aim of this research is to determine the influence of environmental influences, leadership and employee discipline on the productivity of Nik's Collection Surakarta convection employees. The author uses a descriptive quantitative approach. This research will be conducted for 3 months, from October 2023 to December 2023. The population in this research is 58 employees of Konveksi NIK'S Collection Surakarta. The population was less than 100, so the researchers took all employees of Konveksi NIK'S Collection Surakarta, namely 58 employees. The sampling technique in this research used a census. Researchers used multiple linear regression analysis. The results of this research show that the environment, leadership and employee discipline simultaneously have a significant influence on the productivity of Nik's Collection Surakarta convection employees. Based on the t test, it was found that environmental results, leadership and employee discipline had a positive and significant influence on the productivity of Nik's Collection Surakarta convection employees.


Analisis Sistem Informasi Pendistribusian Alat Tulis Kantor di Fakultas Pendidikan Ilmu Pengetahuan Sosial Universitas Pendidikan Indonesia

Abstract: Office equipment, often referred to as Office Stationery (ATK), is a necessity that must be fulfilled and carefully considered in its use in an institution, such as in the Department of Computer Science. Currently, the Department of Computer Science still utilizes Microsoft Excel and handwritten notes in processing ATK data, ranging from the registration of incoming items to the distribution to professors and staff. Therefore, the Department of Computer Science requires a system that can support the process of distributing ATK data. The developed system is expected to assist in generating ATK distribution reports. The system development method chosen in this research is the Waterfall system development model. The system design in this research uses the Unified Modeling Language (UML). The program development process (coding) utilizes the PHP programming language with HTML, and MySQL for database creation. The testing approach in this research employs Black Box testing with the Equivalence Partitioning (EP) method. The results of the conducted testing indicate that all system functions operate according to their intended purposes.


Trustworthy Satellite Earth Observations for Science and Society

Abstract: Enhancing confidence in satellite observations of terrestrial properties like land surface temperature and soil moisture requires advances in validation and data quality assessment practices.


Workshop Manajemen Organisasi dan Penyusunan Modul Dolanan Anak Bagi Forum Taman Baca Masyarakat (FTBM) di Kabupaten Jepara

Abstract: This community service aims to enhance knowledge and expertise in organizational management and children's play module preparation for the Forum Taman Baca Masyarakat (FTBM) in Jepara Regency. The service used the Participatory Action Research (PAR) method, with the participants being representatives of TBM managers who are members of FTBM. A questionnaire was used as the instrument for evaluating the activity, and the results of which were analyzed descriptively. The service results indicate a significant increase in workshop participants' understanding. A total of 9 participants (30%) who were initially in the low understanding category succeeded in increasing to the high category, including 18 participants (60%). The workshop activity on preparing children's play modules also made a positive contribution to the participants, particularly in terms of module preparation. There was a significant increase in understanding, where 10 participants (33.33%) who were initially in the low understanding category succeeded in increasing to the high category, involving 20 participants (66.67%). This activity was successful in achieving positive results and supporting the development of participants’ skills and understanding of organizational management and module preparation.