Machine Learning Oceanographic Data for Prediction of the Potential of Marine Resources

Abstract: Marine data and information are very important for human survival, therefore this data and information is attractive to investors because of the potential economic value. This data and information has been difficult to obtain, the solution to overcome this is by analyzing oceanographic data for 2009-2019 collected from the marine database belonging to the Agency for the Study and Application of Technology (BPPT). The data is the result of a collaborative marine survey between Indonesian and foreign researchers from various countries who sailed in various Indonesian waters. Raw oceanographic data is converted and classified into Conductivity, Temperature, and Depth (CTD) data as oceanographic data parameters identified as predictor variables (X) that are correlated with each other. CTD data is processed into numeric data attributes that have been labeled for input and training. The data was modeled using the Machine Learning (ML) type Supervised Learning (SL) method with the Decision Tree (DT), Linear Regression (LR) and Random Forest (RF) algorithms which were interpreted according to the characteristics of the CTD data. ML will learn data models to understand and store. Next, the model is evaluated using accuracy metrics by measuring the difference between the predicted value and the actual value to obtain a good prediction model. The prediction results show a salinity level of 34.0 parts per thousand (ppt), meaning that in this area of marine waters salinity will affect the solubility of Oxygen (O2) and play a major role in the sustainability and growth of the fertility level of biological resources which is supported by sea surface temperature conditions 29.2°C. So the salinity values obtained using ML techniques and marine resource potential can be assumed to have a strong correlation. The research results show that the RF model has the lowest level of prediction error based on the values: Mean Square Error (MSE) = 0.007; Root Mean Squared Error (RMSE) = 0.082; Mean Absolute Error (MAE) = 0.007 compared to DT model: MSE = 0.008; RMSE = 0.088; MAE = 0.012 and LR model: MSE = 1.008; RMSE = 1.004; MAE = 0.281. The equivalent RF and DT models have a Determination Coefficient (R2) = 0.999, meaning that a model is created that is good at predicting, compared to the LR model with a value of R2 = 0.914. The correlation between variables shows that the LR model is very linear with a Correlation Coefficient (r) = 1.000 compared to the DT model (r) = 0.621 and the RF model (r) = 0.379. Therefore the algorithm that has a value of (r) +1 has the best level of accuracy. The use of ML to predict marine resource potential is a relatively new research field, so this research has the potential to contribute data and information as a reference for innovative studies and investment decision material for investors.


Forward Composition Propagation for Explainable Neural Reasoning

Abstract: This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently propagated through the whole network until reaching the output layer. The sign of each composition value indicates whether the corresponding feature excites or inhibits the neuron, while the absolute value quantifies its impact. The FCP algorithm is executed on a post-hoc basis, i.e., once the learning process is completed. Aiming to illustrate the FCP algorithm, this paper develops a case study concerning bias detection in a fairness problem in which the ground truth is known. The simulation results show that the composition values closely align with the expected behavior of protected features. The source code and supplementary material for this paper are available at https://github.com/igraugar/fcp.


Enhancing data publishing privacy: split-and-mould, an algorithm for equivalent specification

Abstract:
Data sharing and publication have been popular in recent years due to the abundance of options. Evaluating and extracting data from sizable valuable databases i.e., data mining has various challenges which include issues with security, privacy, and data integrity. Anonymized data is used in the majority of privacy preserving data publication approaches, depending on a few utilitarian measures. However, applications that have particular needs for the data they utilize might not be able to use the anonymized data. Practical data anonymization must work to accomplish two opposing objectives: to maintain the data’s usefulness and to satisfy a specific privacy need.
 The utility loss when data is anonymized is frequently measured using generic utility metrics, such as the specific values generalized in a specific ontology. As a need for an application, we suggest equivalent specification, a technique that enables a data user to characterize some properties of the anonymized data. We also introduce the “split-and-mould” algorithm, a heuristic anonymization algorithm that applies a generalization method to the user-provided parameters. Our preliminary results indicate that the specification format and procedure can improve significantly the utility of the anonymized data for data mining that develop predictive models, like decision trees (DTs) and Naïve Bayes.


Cybersecurity integration in distance learning: an analysis of student awareness and attitudes

Abstract: With the rapid growth of distance learning, especially since the COVID-19 pandemic, cybersecurity has become increasingly essential to protect students, instructors, and institutions from cyber threats. This paper examines the role of cybersecurity in enhancing students’ security awareness during distance learning. A literature review covers critical cyber threats in distance learning and strategies to mitigate risks through cybersecurity tools, policies, training, and promoting a culture of cybersecurity. Primary research was conducted by surveying 531 university students engaged in distance learning to assess their cybersecurity awareness, attitudes, and behaviors. Results indicate relatively low awareness and adoption of secure practices. Recommendations include implementing multi-layered cybersecurity defenses, student security awareness training, and nurturing a “human firewall” through a cyber-aware campus culture. Cyber risks can be reduced through proactive partnerships between students, faculty, information technology (IT) staff, and administrators to secure distance learning environments.


The effect of technological context on smart home adoption in Jordan

Abstract: This research examines the use of internet of things (IoT) smart home technologies in Jordan, using the technologies-organization-environment (TOE) framework and social exchange theory (SET). This study investigates the influence of security and privacy considerations on the behavioral intention to embrace IoT smart home solutions. The research examines the function of trust (TR) in service providers as a mediator and investigates how information technology (IT) knowledge acts as a moderator between technological elements and behavioral intention. Analyzed using smart partial least squares (PLS), data from 315 responses of executives in both listed and non-listed businesses were examined. The results highlight the need of giving priority to technological factors and building TR in service providers to ensure the effective implementation of IoT smart home technology in Jordan.


Class imbalance aware drift identification model for detecting diverse attack in streaming environment

Abstract: Detecting fraudulent transactions in a streaming environment presents several challenges including the large volume of data, the need for real-time detection, and the potential for data drift. To address these challenges a robust model is needed that utilizes machine learning techniques to classify transactions in real-time. Hence, this paper proposes a model for detecting fraudulent transactions in a streaming environment using xtream gradient boost (XGBoost), cross-validation and class imbalance aware drift identification (CIADI) model. The performance of the proposed method is evaluated using datasets named credit card and Network Security Laboratory (NSL-KDD) dataset. The results demonstrate that the model can effectively detect fraudulent transactions with high accuracy, recall, and F-measure. The results show that the proposed CIADI model attained 95.63% for the credit card dataset which is higher accuracy in comparison to the generative-adversarial networks (GAN), network-anomaly-detection scheme-based on feature-representation and data-augmentation (NADS-RA) and feature-aware XGBoost (FA-XGB). Further the proposed CIADI model attained 98.5% for the NSL-KDD dataset which is higher accuracy in comparison to the NADS-RA, stacked-nonsymmetric deep-autoencoder (sNDAE) and convolutional neural-network (CNN). This study suggests that the proposed method can be an effective model for detecting fraudulent transactions in streaming environments.


Method comparison of blood film differential performed by digital image and traditional microscope morphology using RCPAQAP datasets

Abstract: Introduction: Historically, the RCPAQAP has issued a glass slide case for the Blood Film Differential Program. However, in 2022, the program was moved to a fully virtual EQA program due to increased difficulty in acquiring donor cases. To ensure statistical acceptability and facilitate the transition to a digital program for manual blood film differential, three historical glass slide cases were re-issued as digital images. A study was conducted to determine differences between the manual differential on glass slides and the re-issued digital images.


Joint remote state preparation protocol for non-maximally entangled W-states and fidelity analysis in noisy environments

Abstract: In this paper, we discuss a protocol for preparing a non-maximally entangled W-state at a distant location where there are two parties intending such creation each of whom possesses only partial information about the state. These two parties along with a third party Controller, participate in a single protocol in order to create the intended state in the hands of a fourth party. The role of the controller is to fabricate an 8-qubit entangled state and distribute the respective qubits involved in the entangled state to the relevant parties and thereby creating a noisy quantum channel. The protocol is first ideally discussed in the noise-free case which is then a perfect joint remote state preparation protocol with fidelity 1. Then, we discuss the performance of the protocol in the presence of noise in which the fidelity is diminished. We consider three different types of noises and analyze the variation of fidelity of the process.


An extreme random tree model combining EDA and PCA for predicting PV module temperature method

Abstract: The variation in temperature of PV modules can affect the efficiency of power generation. Therefore, many studies have been conducted to predict the temperature of PV modules. In this paper, the extreme random tree algorithm is utilized with EDA and PCA analysis to accurately predict the temperature of PV modules in the next hour by filtering and structuring available features. EDA and PCA analysis enhance the model's understanding of the degree of addition, subtraction, and similarity between different features. They also help filter out and construct new key features. Compared to the random forest commonly used in previous studies, the extreme random tree algorithm is more random in the choice of bifurcation in tree building, enabling it to jump out of the vicious circle of local optima and learn the data adequately. After training, the model is tested using actual operating wind turbine data for validation, and the results indicate that the method is highly accurate, noise-resistant, targeted, and practical.


An investigation into emotional engagement of holography for museum display

Abstract: This study delves into the emotional engagement elicited by displaying holograms in museum settings, offering a comprehensive analysis of visitors’ emotional responses and the technological features that influence them. Utilising a qualitative approach complemented by a TF-IDF analysis, the research gathered data from diverse participants who experienced holographic exhibits. The study revealed a range of emotional responses, with ‘Curiosity’ and ‘Amazement’ being predominant, signifying the ability of the display of holograms to enhance visitor engagement and learning experiences significantly. Key technological aspects of holography, such as display method, colour, and three-dimensionality, were crucial in eliciting strong emotional responses and deepening understanding of the exhibit content. The study also highlighted the long-term impact of these experiences, suggesting that the display of holograms can leave a lasting impression and foster sustained interest in museum visits. This study enriches the field of museology by shedding light on the emotional dimensions of visitor interactions with holographic technology. The findings reveal the significant potential of holography to deepen visitor engagement, suggesting that, when effectively implemented, the display of holograms can significantly elevate the museum experience. This research highlights the importance of understanding and catering to visitors’ emotional and experiential needs in the evolving landscape of museum exhibit design.


INTEGRATION OF BLOCKCHAIN TECHNOLOGY IN DIGITAL LIBRARIES: A SOFTWARE ENGINEERING DESIGN

Abstract: This research aims to design software engineering that integrates blockchain technology in digital libraries to improve system security and reliability. This integration is expected to overcome challenges related to data security, service reliability, and efficiency in digital library management. The research methodology involves collecting data through literature, expert interviews, and observations, on the implementation of blockchain technology in digital libraries, then analyzing data to support data design such as etherum, smart contracts, address, node.js, solidity, metamask, and sublime text, then using the Agile Extreme Programming (XP) method for software development. The research results include the design of a decentralized blockchain architecture, the use of smart contracts, and the application of cryptographic techniques to enhance security. Immutability testing in the context of blockchain involves verifying data consistency, validating the process of adding data, testing the ability to delete data, testing against attacks, and activities on immutable data. These tests were conducted using the Truffle framework. The results show that the system is able to maintain data integrity well.


Analysis and Classification of Factors Contributing to Personalization in E-learning

Abstract: The transformation brought about by digital technologies has significantly reshaped the field of education, giving rise to innovative approaches like E-learning that extend beyond the conventional confines of traditional education. One of E-learnings’ defining features is the concept of personalization, wherein learning approaches and content dynamically adapt to cater to the unique needs and preferences of individual learners. This research systematically classifies the myriad possibilities of personalization within the E-learning process. The developed classification encompasses factors of creating and delivering adaptive learning objects, employing learner-centered teaching methods, and personalization grounded in individual learner data. This classification structure enables the development of practical models for personalized learning by leveraging the features of contemporary Learning Management Systems (LMS), offering tangible benefits to both learners and tutors. This research enhances the academic discourse by providing a well-organized structure that elucidates the diverse factors of personalization within the context of e-learning. By classifying and discussing various factors, the research shows the perspective of integrating modern technologies in enhancing the personalized educational experience.


Police and robber game on infinite chessboard

Abstract: It is considered two variants of the game "Policeman and a robber", on an infinite chessboard that is a graph giving a regular partition of the plane into squares. Heuristic and precise definitions of the concepts "the initial state is winning for the pursuer", and "the initial state is winning for the evader" are formulated. Then, criteria for determining if a given initial state is winning for the pursuer or for the evader is given.


Generation of Images from Text Using AI

Abstract: Reading the words can be confusing, and it may be hard to picture what is happening. There are some circumstances where words can be misunderstood. It's much simpler to recognize text if it's displayed as an image. The use of visuals is proven to increase viewership and retention.Synthesizing realistic images automatically is a challenging undertaking, and even the most advanced artificial intelligence and machine learning algorithm has trouble meeting this standard. GANs (Generative Adversarial Networks) are just one example of a powerful neural network architecture that has shown promising results in recent years. Existing text-to-image methods can generate examples that generally reflect the meaning of the provided descriptions, but they often lack the necessary details and colorful object elements.The primary objective of our research was to explore diverse architectural methodologies with the intention of facilitating the generation of visual representations from textual descriptions. By delving into this investigation, we aimed to discover and examine various approaches that could effectively support the creation of visuals that accurately depict the content and context provided within written narratives. Our aim was to unlock new possibilities in the realm of visual storytelling by establishing a strong connection between language and imagery through innovative architectural techniques.


Locus of Control as Moderator between Work Motivation and Job Satisfaction among Bank Employees

Abstract: Background: The global banking sector faces numerous challenges that necessitate a reevaluation of employee attitudes, behaviors, and the working environment. The critical roles of job satisfaction and work motivation in enhancing organizational efficiency and productivity are well-documented, with research consistently showing a positive correlation between these factors and organizational outcomes. The concept of locus of control, distinguishing between internal and external beliefs about control over life events, is a significant personality trait influencing job satisfaction and work motivation. Objective: This study aims to explore the moderating role of locus of control on the relationship between work motivation and job satisfaction among bank employees. It hypothesizes that an internal locus of control will enhance the positive correlation between work motivation and job satisfaction. Methods: A quantitative research methodology was employed, involving the survey of 212 bank employees. Data were collected on work motivation, locus of control (internal and external), and job satisfaction. Correlation analysis was used to examine the relationships between these variables. Further, multiple hierarchical regression analysis was conducted to assess the moderating effect of locus of control on the relationship between work motivation and job satisfaction. Cronbach’s alpha was used to evaluate the reliability of the constructs. Results: The study found a strong positive correlation between work motivation and job satisfaction (r = .53, p < .01), a positive correlation between work motivation and internal locus of control (r = .44, p < .01), and a negative correlation between work motivation and external locus of control (r = -.26, p < .01). Job satisfaction also correlated positively with internal locus of control (r = .46, p < .01) and negatively with external locus of control (r = -.34, p < .01). The regression analysis revealed that internal locus of control significantly predicted job satisfaction and moderated the relationship between work motivation and job satisfaction, with a total variance explained by the model of 68% (R² = .68, F = 86.83, p < .01). Conclusion: The findings confirm the hypothesis that locus of control moderates the relationship between work motivation and job satisfaction, with an internal locus of control enhancing this positive correlation. These insights highlight the importance of considering personality traits, such as locus of control, in developing strategies to enhance employee motivation and satisfaction, thereby contributing to organizational success and productivity.


Multi-scale and multi-patch feature fusion network for person re-identification

Abstract: Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches.


Test of Good Governance in Nigeria using World Bank indicators

Abstract: The concern for good governance and its role in development process has increased since 1980s. Nigeria has experienced uninterrupted democracy since 1999. This study carryout a test of good governance in Nigeria using the six indicators of good governance used by World Bank. Specifically, the study determine the effect of voice and accountability, political stability and absence of violence and terrorism, government effectiveness, regulatory quality, rule of law and control of corruption on economic development in Nigeria using per capita GDP as a proxy for economic development. Inflation rate and oil price were included in the model in order to produce a robust model. Annual data covering 1999 to 2022 were collected from World Bank website. Descriptive statistics were used to summarize data while Autoregressive Distributed Lag Model and Bound tests were used to estimate relationship. Findings revealed that the performance of governance in Nigeria in all the six indicators during the period of study was weak. This consequently affect per capita income negatively both in the short-run and long-run. It was concluded that governance has negative effect on economic development in Nigeria. It was recommended that the government should use worldwide indicators to conduct self-evaluation and carry out reforms that will help to improve governance in Nigeria for the purpose of achieving development or improving the wellbeing of Nigerians.


Efficient quantum digital signatures over long distances with likely bit strings

Abstract: Quantum digital signatures (QDSs) can provide information-theoretic security of messages against forgery and repudiation. Compared with previous QDS protocols that focus on signing one-bit messages, hash function--based QDS protocols can save quantum resources and are able to sign messages of arbitrary length. Using the idea of likely bit strings, we propose an efficient QDS protocol with hash functions over long distances. Our method of likely bit strings can be applied to any quantum key distribution--based QDS protocol to significantly improve the signature rate and dramatically increase the secure signature distance of QDS protocols. In order to save computing resources, we propose an improved method where Alice participates in the verification process of Bob and Charlie. This eliminates the computational complexity relating to the huge number of all likely strings. We demonstrate the advantages of our method and our improved method with the example of sending-or-not-sending QDS. Under typical parameters, both our method and our improved method can improve the signature rate by more than $100$ times and increase the signature distance by about $150\phantom{\rule{0.2em}{0ex}}\mathrm{km}$ compared with hash function--based QDS protocols without likely bit strings.


At the frontier of the metaverse: NFTs, artistic expression, and digital immersions

Abstract: This paper explores the multifaceted landscape of Non-Fungible Tokens beyond their role in digitizing artworks. It delves into the intersection of art, utility, and cultural preservation within the NFT realm. The discussion revolves around the duality of “art for art’s sake” and functional utility, sparking ongoing debates about the essence of art in both the analog and digital realms. The convergence of contemporary aesthetics with practical token utility challenges conventional definitions, offering an alternative to narrative retrieval and contributing to cultural preservation in the virtual sphere. As one enters the NFT ecosystem, a utopian quest for autonomy in healthy production becomes evident, albeit not without formidable obstacles. The paper examines the promise of NFTs to liberate creators from intermediary control and the entwined complexities accompanying their evolution. It sheds light on the cautious and ethical approach required for the mass adoption of these technologies, contrasting this with the current global context characterized by rampant consumerism, individualism, and the relentless pursuit of easy financial gains.


Dementia Research on Facebook and Twitter: Current Practice and Challenges

Abstract: Background: Social media is a powerful tool for engaging diverse audiences in dementia research. However, there is little data summarizing current content exchange in this context. Objective: To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public social media posts. Methods: We retrieved Facebook (2-year period, N = 7,896) and Twitter (1-year period, N = 9,323) posts containing dementia research-related keywords using manual and machine learning-based search strategies. We performed qualitative and quantitative content and sentiment analyses on random samples (10%) of the posts. Results: Top Facebook users were advocacy (45%) and health organizations (25%). On Twitter, academics/researchers were the largest user group. Prevention was the most frequently coded theme (Facebook 30%; Twitter 26%), followed by treatment (Facebook 15%; Twitter 18%). Diagnostics had the highest Facebook engagement. Sharing knowledge was the primary form of content exchange (Facebook 63%; Twitter 80%). Most shared journal articles were peer-reviewed and open access. Emotional tone was overall more positive on Facebook. Justice was a prominent ethics topic regarding inequalities related to identity and intersecting modes of marginalization in dementia research. Conclusion: The findings indicate the importance of social media as an engagement tool of current topics in health research and reveal areas of potential for increased engagement. These data can inform consensus-based best practices for ethical social media application in dementia research.


Habits, Motivation and Performance: An Integrative Model for Organisational Improvement Grounded in Brain Science

Abstract:
Purpose: Organizations aim to cultivate motivated and productive workforces, yet often struggle to understand the complex neurological factors shaping employee habits, engagement, and performance. This paper presents an integrative model linking psychological needs, motivation systems, and contextual supports to optimize collective habits and outcomes.
 Methodology: A mixed methods study of 400 employees in Zambia’s public health sector combined surveys, interviews, statistical analysis, and prior organizational research.
 Findings: Results indicated the limitations of existing habit frameworks in capturing neurological dynamics. Just 31% felt current models reliably predict long-term impacts on performance. Key findings revealed that the top motivators were financial rewards, career growth, and training. On-the-job skill-building is also rated highly. Clear expectations, collaboration, and resources further enabled productivity.
 Unique Contribution to Theory, Policy and Practice: Based on the data, a Motivation-Performance Model is proposed, encompassing elements like empowerment, open communication, and flexibility to fulfil psychological needs. Individual, team, and organizational factors intersect to form collective habits and effectiveness. Implementing diagnostic and enhancement strategies based on the model offers an actionable approach to leveraging habits for competitive advantage.


The digital economy brings new opportunities for arts and culture

Abstract: We are currently in the digital era of the 21st century, and the rapid advancement of artificial intelligence has brought new energy to the evolution of museums. Museums must inevitably advance towards digitisation. Therefore, the variety of applications for artificial intelligence and other related technologies should be broadened. At the same time, numerous arising issues need to be addressed. The focus of this paper is the objective of increasing young visitors’ satisfaction. Herein, we leverage an assortment of digital technology tools and continuously evaluate their commercial feasibility. It is hoped that a rise in satisfaction can be accomplished through the exemplar business plan provided.


Workshop Media Pembelajaran Berbasis Artificial Intelegence

Abstract: The aim of this community service is to enhance teachers' creativity in utilizing Artificial Intelligence (AI) technology in teaching activities. The service method was conducted in-person, utilizing LCD Projectors and sound systems to support workshop activities. The results of the implementation show that teachers have gained an understanding of creating instructional media using Canva and CapCup. This is evident as teachers can operate both AI applications and produce instructional media that are sufficiently good and diverse. In conclusion, the workshop proceeded smoothly as scheduled, providing a positive impact on teachers.


Board 90: Core Feature Extraction with Computer Vision

Abstract: Abstract In the oil and gas industry, exploration is largely dependent on the study of the subsurface hundreds or thousands of feet below. Most of the data used for this purpose is collected using borehole logging tools. Although sophisticated, these tools are limited as to how precisely they can measure the subsurface in terms of vertical resolution. There is one method of studying the subsurface that provides unlimited vertical resolution – core samples. Although core samples provide scientists the opportunity to generate a full, continuous data set, lab analysis work is normally done at one-foot intervals, as anything more would be prohibitively expensive. This means at best, a representative data set is generated. However, if the subsurface is not homogeneous, it is difficult to generate a representative data set with lab analysis done at one-foot intervals. This is a void that artificial intelligence can fill. More specifically, a properly trained neural network can analyze high-resolution core images continuously from top to bottom and generate a continuous analysis. It is also important to note that geologic interpretation tied to core analysis can introduce human error and subjectivity. Here too, a properly trained neural network can generate results with extreme levels of accuracy and precision. One core analysis expert believes that core analysis done manually is flawed about 70% of the time. This flawed analysis can result from lack of experience and or a lack of knowledge of the geologic formation. We are not the first to attempt to analyze core samples with vision algorithms. A group of Stanford researchers used micro-computed tomography (micro-CT) and Scanning Electron Microscopy (SEM) images of core samples to characterize the porous media. While promising, SEM and micro-CT imaging is expensive, and more importantly it is not a standard practice in the oil and gas industry to collect these types of images, making these images rare. One other work applied convolutional neural networks to a GIS based regional saturation system, but our work is significantly different. It is well known that training a neural network requires abundant data, thankfully with the method of core analysis we are proposing that will not be a problem. Through industrial partnerships we've obtained hundreds to thousands of core images sufficient to train a neural network, as well as core interpretations tied to those images coming from a core analysis expert with over 40 years of experience. We are the first to propose automatic hydrocarbon saturation as well as lithology prediction from core slab images. We propose the use of convolutional neural networks to analyze core samples at a single site. We plan to conduct experiments using a variety of neural networks to determine the best practices, and explore how such a service can be offered to the industry via the software-as-a-service paradigm. In the past, automated analysis through core slab images has not been possible simply because images of the required resolution were not common, but that has changed. If implemented successfully, this proposed method could become the new standard for core evaluation.


Benchmarking hybrid digitized-counterdiabatic quantum optimization

Abstract: Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of nearterm quantum computers, utilizing parameterized quantum circuits designed with counterdiabatic protocols. However, the classical aspect of this approach has received limited attention. In this study, we systematically analyze the convergence behavior and solution quality of various classical optimizers when used in conjunction with the digitized-counterdiabatic approach. We demonstrate the effectiveness of this hybrid algorithm by comparing its performance to the traditional QAOA on systems containing up to 28 qubits. Furthermore, we employ principal component analysis to investigate the cost landscape and explore the crucial influence of parametrization on the performance of the counterdiabatic ansatz. Our findings indicate that fewer iterations are required when local cost landscape minima are present, and the SPSA-based BFGS optimizer emerges as a standout choice for the hybrid DCQC paradigm.


Separability criteria based on the correlation tensor moments for arbitrary dimensional states

Abstract: As one of the most profound features of quantum mechanics, entanglement is a vital resource for quantum information processing. Inspired by the recent work on PT-moments and separability [Phys. Rev. Lett. 127, 060504 (2021)], we propose two sets of separability criteria using moments of the correlation tensor for bipartite and multipartite quantum states, which are shown to be stronger in some aspects in detecting entanglement.


AI-assisted classification of AVM endothelial cell phenotypes based on dynamic features

Abstract: Biological tissues frequently display cellular heterogeneity, characterized by the coexistence of diverse cellular phenotypes. These phenotypes encompass a range of physical traits, including shape, motion, and gene expression patterns that indicate distinct functions and behaviors. Heterogeneous cellular phenotypes are particularly prevalent in many diseases, including arteriovenous malformations (AVMs), a type of vascular anomaly. Recent research has emphasized the significance of angiogenic potential in the development and treatment of AVMs, as AVM endothelial cells exhibit considerable genetic mutations in angiogenesis signaling pathways. Since angiogenesis is closely associated with dynamic behaviors such as migration and proliferation, a quantitative approach is necessary for classifying heterogeneous cell phenotypes based on their dynamic features. In this study, we propose a dynamic feature-based approach to classify heterogeneous cell phenotypes, which accounts for time variability and the number of division cycles. We apply this approach to classify AVM endothelial cell phenotypes and group them into subcategories based on their angiogenic potential. In order to facilitate dealing with a vast volume of images, we employ cellpose, a deep convolutional neural network designed for cellular segmentation, to generate cell masks from phase contrast time-lapse images of AVM endothelial cells. We extract dynamic features that not only capture the periodic and progressive changes in morphology but also incorporate motility features. These features are then subjected to principal component analysis, followed by K-means clustering and hierarchical clustering. We have identified three distinct subgroups based on their angiogenic potential, and notably, these subgroups exhibit distinct temporal variations in the solidity and aspect ratio of the cell body.


Analyzing octocoral morphology using network analysis

Abstract: The effects of warming oceans due to climate change have had an immediate and devastating effect on coral ecosystems. Amid this crisis, the need to accurately identify and understand these organisms is more crucial than ever. In this work, we use methods from network science to analyze the transport distribution networks of octocorals, a group of more climate change-resistant corals. Octocorals are colonies of Cnidarian polyps connected by a network made of biological tissue that contains a through-gut that allows for nutrient sharing between individuals. We first develop a diverse database of over 350+ images of quasi-two-dimensional soft corals. We then quantify topological and geometric graph theory metrics that consider the spatial embedding and identify relevant functions of the transport network within the corals. These metrics include the number of nodes and edges, robustness, total length, and centrality of the nodes and edges. From this, we identify two main classes of coral morphologies: tree-like corals, whose branches do not reconnect, and mesh-like corals, whose branches reestablish connections throughout their network. We compare the metrics from these two types of coral to minimal and maximal models to discover that tree-like corals are expensive but in turn robust and transport efficient networks whereas mesh-like corals strike an ideal balance between the marginal cost of their network versus the returns in efficiency.


3D tracking using a multi-spectral 4th-generation phase plate

Abstract: 3D imaging and object tracking is critical for medical and biological research and can be achieved by multifocal imaging with diffractive optical elements (DOEs) converting depth (z) information into a modification of the 2D image. Recently, we introduced a simple system for achieving precise localization and tracking of point sources emitting fluorescent light at 525 nm using a spiral phase plate in a standard wide-field optical microscope with minor modification. We achieved a bead localization precision of <25 nm in the transverse dimensions and ≤70 nm along the axial dimension over an axial range of 6 μm. 3D diffusion constants of chromatin labelled with LacR-GFP arrays matched expected values. We are now pursuing chromatin tracking experiments to better understand how DNA damage affects chromatin motions and how chromatin dynamics impacts the DNA damage response. We are extending our chromatin tracking capabilities to simultaneously track chromatin and a DNA repair factor (53BP1) which has been fluorescently labelled at 610 nm with mCherry. To do this, we are testing a new 4th-generation waveplate that will enable 3D tracking of cellular objects labeled with two different fluorophores. Unlike 3rd-generation DOEs that are limited to tracking objects that fluoresce within a narrow wavelength range centered on their design wavelength, 4th-generation optics have the potential to perform 3D tracking with high efficiency over a broad wavelength range that can span the whole visible spectrum. We will present preliminary data on the performance of a microscope fitted with a multiwave vector vortex waveplate that can track particles fluorescing in a wide range of narrow wavelength channels, making this optical system ideal for single particle tracking in multispectral microscope systems.


Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network

Abstract: The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data-driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on-site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.


Open-ComBind: Harnessing unlabeled data for improved binding pose prediction

Abstract: Accurate determination of a ligand binding pose is crucial for in silico drug discovery. Molecular docking is a widely used method for the prediction of non-covalent binding of a protein and ligand system. However, most molecular docking pipelines consider only one ligand at a time, even though ligands may share binding interactions when bound to the same receptor. Open-ComBind, an open-source version of the ComBind molecular docking pipeline, addresses this issue by incorporating information from multiple ligands, including those without known bound structures, to improve pose selection. Poses are simultaneously predicted for a docking ligand of interest as well as a set of helper ligands. First, we generate distributions of feature similarities between ligand pose pairs, comparing near-native poses with all sampled docked poses. These distributions capture the likelihood of observing similar features, such as hydrogen bonds or hydrophobic contacts, in different pose configurations. We then combine these similarity distributions with a per-ligand docking score to improve overall pose selection by 8.5% and 4.5% for high-affinity and congeneric series helper ligands, respectively. Open-ComBind reduces the average RMSD of ligands in our benchmark dataset by 8.2%. We investigate the significance of the inter-molecular features as well as the number of helper ligands to achieving accurate pose selection. We provide Open-ComBind as an easy-to-use command line and Python API to increase pose prediction performance at www.github.com/drewnutt/open-combind.


TonB raider: Discovering the secrets of the Ton system's order

Abstract: Active nutrient uptake is fundamental for survival and pathogenicity of gram-negative bacteria, which operate a multi-protein Ton system to transport essential nutrients like metals and vitamins. This system harnesses the proton motive force at the inner membrane to energize the import through the outer membrane, but the mechanism of energy transfer remains enigmatic. Here, we study the periplasmic domain of ExbD, a crucial component of the proton channel of the Ton system. We show that this domain is a dynamic dimer switching between two conformations representing the proton channel’s open and closed states. By in vivo phenotypic assays we demonstrate that this conformational switch is essential for the nutrient uptake by bacteria. The open state of ExbD triggers a disorder to order transition of TonB, enabling TonB to supply energy to the nutrient transporter. We also reveal the anchoring role of the peptidoglycan layer in this mechanism. Herein, we propose a mechanistic model for the Ton system, emphasizing ExbD duality and the pivotal catalytic role of peptidoglycan. Sequence analysis suggests that this mechanism is conserved in other systems energizing gliding motility and membrane integrity. Our study fills important gaps in understanding bacterial motor mechanism and proposes novel antibacterial strategies.


Rational enzyme engineering via generative AI

Abstract: Rational enzyme engineering necessitates an in-depth understanding of various enzyme mutations, a pursuit that poses significant difficulties. However, this obstacle could potentially be overcome by learning from nature's own mechanisms of evolving efficient enzymes. We employed generative AI to analyze natural protein sequences, thereby establishing a connection between enzyme evolution and catalysis. We found that the sequence information could be used to predict enzyme activity and stability in the enzyme active center and scaffold, respectively. The discovery paves the way for a fresh understanding of both enzyme catalysis and evolution. Moreover, the enzyme evolution-catalysis relationships provide a rational way for enzyme engineering, with a ∼50% success rate in enhancing enzyme performance in the experiment. Collectively, our research highlights the immense potential of the abundant natural protein sequences combined with generative AI for advancing enzymology and enzyme engineering.


Cyberbullying Detection: A Comparative Study of Classification Algorithms

Abstract: In the realm of social media, cyberbullying's pervasive impact raises urgent concerns about its emotional and psychological toll on victims. This study addresses the imperative of effectively detecting cyberbullying. By leveraging ML and DL techniques, we aim to develop reliable methods that accurately identify instances of cyberbullying in social media data, enhancing detection efficiency and accuracy. This facilitates timely intervention and support for affected individuals. In this comprehensive analysis of existing systems, various ML and DL models are extensively texted for cyberbullying detection. The evaluated models include Random Forest, XgBoost, Naive Bayes, SVM, CNN, RNN, and BERT. Pre-processed datasets are utilized to train and evaluate the models. To evaluate the ability of each model to reliably identify cyberbullying in social media data, performance metrics such as F1 score, recall, precision, and accuracy are used. The findings of this study demonstrate the efficacy of different ML and DL models in monitoring cyberbullying in social media data. Among the models evaluated, the BERT model exhibits exceptional performance, achieving the highest accuracy rates of 88.8% for binary classification and 86.6% for multiclass classification.


Policy (In)Consistency and Sustainable Development Goals in Africa: A Systematic Literature Review

Abstract: The attainment of Sustainable Development Goals hinges on the alignment of SDGs policies. This systematic literature review delves into the intricate relationship between policy inconsistency and sustainable development goals in Africa. Through this study, we unveil the challenges, implications, and the potential pathways that emerge at the intersection of policies and the quest for sustainable development in Africa. Our investigation takes a close look at the diverse dimensions of policy coherence, encompassing economic, social, and environmental considerations, and how these dimensions impact the progress of SDGs. Employing a systematic review approach, we meticulously filtered through 1745 results from databases, selecting 353 articles for a comprehensive analysis. Our findings underscore the significant role that policy inconsistency plays in impeding the attainment of SDGs in Africa. We propose an approach anchored on the alignment of SDGs policies and each goal of SDGS for the attainment of the 2030 SDGs agenda in Africa.


Website Quality Analysis Using Modified Webqual Method and Importance Performance Analysis on SITU TAK Website

Abstract: Technological developments affect information services, such as website. Information services on the website make it easy to convey information widely. Therefore, the quality of the website can affect the information services. This research assessed one of Telkom University's academic service websites, namely SITU Student Activities Transcript (SITU TAK). The purpose of this study was to measure the quality of the website, user satisfaction, and the factors that can increase the user satisfaction. This study employed Webqual 4.0 method as the indicator and Importance-Performance Analysis (IPA) for grouping the factors based on the quadrant of IPA. Before grouping the data, the data first passed the validity test, reliability test, and gap analysis between user perceptions and expectations. Therefore, it can strengthen the conclusions and recommendations resulted from this study. After conducting this study, the final results were obtained, which stated that SITU TAK website still did not meet the expectations of its users. This can be seen in the results of the gap analysis calculation with a value of -0.63, which means that the level of importance or expectations of the users is still higher than the performance of the website.


Ring Oscillator Based True Random Number Generator

Abstract: Abstract: A true random number generator (TRNG), also known as a hardware random number generator (HRNG), does not use a computer algorithm. Instead, it uses an external unpredictable physical variable such as stochastic models to generate random numbers. Here it gathers data from random electronic signals. Then, the data is converted into digital form and any patterns registered are removed to make it random. This data is used to create random numbers. It is mainly used in Cryptographic Security, authentication, secure communications, e-commerce transactions, Digital Signatures etc. In Existing Method, LFSR based TRNGs generate pseudo random numbers. They produce sequences of bits that appear random but are deterministic and repeat after a certain number of cycles known as the period. They are easy to predict and are not suitable for high security applications. This project aim is to overcome such circumstances, we use TERO(Three Edge Ring Oscillator) based TRNG. Three edges are simultaneously injected by each inverting NAND stage with enable signal Run. These edges will have an identical mean period since they propagate through the same stages. The TERO generates an oscillating signal with a frequency determined by the delay of inverters. TDC (Time to Digital Converter) is used to precisely measure the time intervals between the rising edges of TEROs output signal. Then, it converts time intervals into digital values, effectively generating truly random numbers. Power consumption depends on factors like operating frequency and load capacitance. The Software used is Xilinx Vivado/Xilinx ISE Tools. Here the Proposed Method exquisitely balances Low design effort and resource consumption with high throughput and high randomness.


Witnessing edge modes in trimerized circuit quantum electrodyamic lattice

Abstract: Abstract We propose a scheme to investigate and witness edge modes of general one-dimensional (1D) photonic trimers in a circuit quantum electrodynamic (QED) lattice. These in-gap edge modes are strictly and analytically solved and the criteria for their emergence are indicated respectively. Moreover, the energy spectrum of the system shows two different regimes characterized by a discrepancy in the number of edge modes. Specifically, while there are always a couple of edge modes only present at one single boundary in both the regimes, one of the regimes also shows another pair of edge modes localized on the opposite boundary. Furthermore, these edge modes are witnessed with the aid of continuous-time quantum walks and average photon number measurements. Our scheme provides a comprehensive method for studying the edge modes of matter.


Implementasi Pengelolaan Bantuan Operasional Sekolah Reguler pada Peningkatan Sarana Prasarana dan Kualitas Pembelajaran di SD Yapis Kota Bogor

Abstract: The purpose of this study was to describe the management of Reguler BOS at SD Yapis Bogor City and its impact on improving infrastructure and the quality of learning at SD Yapis Bogor City.The approach used in this study is a qualitative approach with descriptive methods. The resource person consists of 6 people with positions, Head of school, Deputy Head of Curriculum, Deputy Head of Student Affairs, treasurer, operator and teacher at fifth grade. The results showed that the management of Reguler BOS increased the infrastructure at SD Yapis. In 2023 Yapis already has a Computer Lab with 12 sets of computers along with tables and chairs. While in the previous year there were only 6 sets. This also affects the increase in ANBK results. ANBK is an indicator of improving the quality of learning for elementary school level students which is implemented in grade 5 students. From the 2023 Education Report Card, the proportion of students with numeracy skills reaching a minimum competency increased by 42.86%. Where in 2022 it will be 23.33 and in 2023 it will be 33.33.


Analyzing credit card fraud cases with supervised machine learning methods: logistic regression and Naive Bayes

Abstract: Frauds involving credit cards are simple and effortless to target. With the rise of online payment credit cards have had a huge role in our daily life and economy for the past two decades and it is an important task for companies to identify fraud and non-fraud transactions. As the number of credit cards grows every day and the volume of transactions increases quickly in tandem, fraudsters who wish to exploit this market for illegitimate gains have come to light. Nowadays, it’s quite easy to access anyone’s credit card information, which makes it simpler for card fraudsters to do their crimes. Thanks to advances in technology, it is now possible to determine whether information gained with malicious intent has been used by looking at the costs and time involved in altering account transactions. The Credit Card Fraud analysis data set, which is obtained from the Kaggle database, is used in the modeling process together with The Logistic regression method and Naive Bayes algorithms. Using the Knime platform, we are going to apply machine learning techniques to practical data in this study. The goal of this study is to identify who performed the transaction by examining the periods when people use their credit cards. The Logistic regression approach and the Naive Bayes method both had success rates of 99.83%, which is the highest. The two methods’ results are based on Cohen’s kappa, accuracy, precision, recall, and other metrics.


Technologies for Designing Professional Educational Training Programs for the Digital Economy

Abstract: In this article, the authors consider the issues of training qualified personnel for the digital economy at the present stage of higher education development. The article describes the relationship between the emergence of new technologies and high-tech industries and the emergence of new directions and specialties in the field of higher education, requiring students to master new competencies. The concept of "competence" and the problems of implementing the competence approach in the system of higher professional education are investigated. The main definitions and principles of the competence approach to the design of educational outcomes in higher education are outlined. The analysis of published studies and presented software products devoted to the design of curricula is given. An important analytical conclusion is that one of the important points in the development of educational programs is to focus on the needs of production in young professionals with a certain set of competencies, but now most programs of higher and secondary vocational education do not have time to take into account the latest achievements in the technological and industrial spheres. The development of students’ competencies is the goal of educational programs, therefore they should be structured in such a way as to ensure the consistent and systematic development of the necessary skills and knowledge. The authors note that the competence approach in higher professional education is implemented in the form of an approximate basic educational program that forms a model of a specialist whose competencies must meet production standards to perform specific labor functions. The article sets the task to form a set of disciplines that is optimal in terms of labor intensity and levels of competence formation, allowing the most effective implementation of the competencies of a student in the bachelor’s degree direction, taking into account the level of formation and limitations of the curriculum set by the employer. The mathematical model of solving the problem and the technology of designing the optimal competence model of the graduate, taking into account the requirements of the employer, are presented.


Recursion Algorithm for Highlighting the Maximum Clique of the Graph

Abstract: The work considers the polynomial algorithm for calculating the maximum clique of the non orientable graph. The algorithm is based on the properties of the subspace of sections and the subspace of cycles belonging to the the space sugrapha of a non — unoriented graph. It is shown that the algorithm for the allocation of the set of maximum clicks of the graph has an exponential computational complexity. Examples of highlighting the maximum clique of the graph are considered.


Implementation of Archives Digitalization through Belajar.id Account in Improving Data Organization

Abstract: The purpose writing this article is to understand the process of structuring digitized data at SDN 8 Rambang, due to archive services conventional which is ineffective and we are trying to change to a digital archive service. Due to the preservation of archives in the current era of Society 5.0 leads to how the public can access archival information that is capable of implementing technology in it. Archive arrangement includes several types of archives including textual, audio and audio-visual archives. The process of moving current archive contents saving it in different media, takes place continuously from old media to new media. Both documents and archives that are in digital format or still analog. Document digitization/archives are a process of change from conventional ones in various styles and forms to electronic archives.


Developing the Contribution of Information Processing Activities in Forming Persuasion

Abstract:
Research on persuasion or attitude change has been approached through singleprocess models. The focus of this research is to explain more clearly that persuasion only occurs through a process that is directly caused by social influence. However, the use of this approach produced contradictory results. On the one hand, social influence increases persuasion, but other research finds the opposite. This article discusses the possibility of using a dual-process model approach, which is expected to address the inconsistency of social influence on persuasion in single-process models. Dual process models are proposed to be able to provide new explanations regarding the possibility of multiple effects of social influence variables on persuasion by including information processing activity variables. Through the dual process model approach, it is possible to explain that a certain effect can be caused by different processes and that a certain variable can work differently and in different situations. Social influence has different mechanisms for determining persuasion, which can act as a simple cue when the thinking level is low and can have more than one role (multiple roles) when information processing activities are at a high thinking level.
 Keywords: elaboration likelihood models, information processing, persuasion, social influence


The Importance of Studying Spontaneous Speech in Computational Linguistics

Abstract: This scientific work provides information on the importance of studying spontaneous speech in computational linguistics. Studying spontaneous speech has numerous practical implications. The ramifications of spontaneous speech analysis are extensive, ranging from improving voice assistants and speech-to-text systems to enhancing human-computer interaction. An examination of spontaneous speech in computational linguistics offers a more authentic depiction of language usage, poses difficulties for current models, and opens up fresh opportunities for enhancing the precision and adaptability of language processing systems. The integration of spontaneous speech analysis will be crucial in developing the discipline of computational linguistics as technology progresses.


SMOTE Variants and Random Forest Method: A Comprehensive Approach to Breast Cancer Classification

Abstract: This research focused on using machine learning methods for breast cancer diagnosis, considering that breast cancer is the scariest disease for women because it can cause mortality. Not only that, but there is also an increase in breast cancer death rates in women yearly. Early prediction is the right solution to increase life expectancy and reduce mortality rates caused by breast cancer. However, breast cancer data has a problem, namely that the data is imbalanced, which harms the performance of the machine learning method itself. In the data, breast cancer had a Benign class (357 instances) more than the Malignant class (212 instances). Therefore, this study aimed to solve the problem of imbalanced data using the Smote variants and Random Forest approaches in breast cancer classification. The results of this study showed that the Smote approach with Random Forest had the best performance compared to Borderline Smote and Random Forest in the case of breast cancer data classification, where Smote with Random Forest produced an accuracy of 97.3%, sensitivity of 96.9%, and specificity of 97.8%. In comparison, Borderline Smote with Random Forest produced an accuracy of 96.4%, sensitivity of 95.6%, and specificity of 96.9%. The results of this study can contribute to predicting breast cancer using the proposed method, because it has been proven to have high accuracy.


Understanding the Dynamics of Social Support, Self-efficacy, and Academic Stress in the Context of Online Learning: Evidence from Undergraduate Students

Abstract:
Engaging in remote learning presents substantial challenges, particularly in emergencies such as a Covid outbreak. The transition to online teaching can introduce additional stressors. This study investigated the association between social support, self-efficacy, and academic stress among undergraduate students. Furthermore, it sought to identify predictors of academic stress during online learning and examine the mediating role of social support in the relationship between online self-efficacy and academic stress. This study uses a convenience sample of 200 undergraduate students – 138 females (69%) and 62 males (31%), aged between 18 and 24 years. Data were collected using the social support scale, online self-efficacy scale, and academic stress scale. Bivariate correlation analysis revealed a negative correlation between family support and academic stress, while no significant correlations were observed with friends’ or significant others’ support. Moreover, no significant correlation was found between online self-efficacy and academic stress. Results also indicated a positive correlation between online self-efficacy and all dimensions of social support. Multiple regression analysis demonstrated that a combination of online self-efficacy and social support could predict academic stress. In conclusion, social support is pivotal in mitigating academic stress during emergency remote learning.
 Keywords: academic stress, online self-efficacy, social support


Strategi Mapping Classroom dengan model Quantum Teaching Untuk Meningkatkan Percaya Diri Tampil di depan Publik Untuk Generasi Kota Batam

Abstract: The urgency of this activity is to increase the courage of the younger generation to appear with good communication in front of the public. The purpose of this activity is to train the younger generation who are students to be able to communicate. The problem that occurs is the lack of confidence to appear in front of the public, the benefit of this activity is that the training participants are expected to be able to appear in front of the public individually or in groups. The target of this activity is that participants display artistic creativity uploaded on social media, namely tiktok. generation to run the wheels of Indonesia EMAS for that it is necessary to carry out activities that can improve their abilities. There were 350 participants, the training technique used the Mapping Classroom Strategy approach so that participants could communicate in small groups as the first step. Then the material is given using the Quantum Teaching model which involves speakers and participants to explore talents that can be presented and staged in publications on social media.


Job Satisfaction and Organizational Citizenship Behavior (OCB): The Moderating Role of Social Identity

Abstract:
Organizational citizenship behavior (OCB) is a contribution from individuals who go beyond the role in the workplace from what is assigned and rewarded. Employees who are satisfied with their jobs may be more able to bring out OCB behavior. The relationship can be strengthened by each individual’s social identity. The purpose of this study was to determine the moderating role of social identity on the relationship between job satisfaction and organizational citizenship behavior. The subjects of this study were 75 employees who worked at the Aster Cosmetic company in Malang. The total sampling method was used, wherein the entire population served as the subject. A quantitative research design was used. Data were analyzed using the Hayes PROCESS. The results show that job satisfaction significantly affects OCB behavior. It can be explained that the higher a person’s job satisfaction, the more able the employee will be to show OCB behavior. Meanwhile, social identity is not able to moderate the relationship between job satisfaction and OCB.
 Keywords: job satisfaction, OCB, social identity


Mineralogical Prospecting and Characterization of Auriferous Placers in the Elogo Region: Insights into Primary Gold Deposit Exploration Strategies

Abstract: Placer accounts for nearly 31% of global gold production between 1984 and 2006. The Elogo region has a historical legacy of artisanal and semi-industrial gold placer mining dating back to colonial times. Currently, the area is undergoing extensive exploration targeting a primary gold deposit. This study aims to contribute to understanding the primary gold deposit in the Elogo Region in the North-West of the Congo Republic. The methodological approach involved the comprehensive characterization of placer deposits, encompassing lithological and mineralogical aspects and the morphological features of gold grains. The objective was to delineate the origin of gold in relation to the local geological context. The obtained results reveal that the alluvial deposits are polygenic, comprising seventeen mineral species, namely zircon, olivine, magnetite, ilmenite, gold, garnet, rutile, coltan, cassiterite, monazite, apatite, amphibole, tourmaline, pyrite, limonite, chromite, and amphibole. Morphoscopic analysis of gold grains indicates the presence of single grains and grains with quartz inclusions, suggesting an association with quartz veins. Gold grains exhibit flattened, sub-flattened, and rounded morphologies. In the northern Elogo region, coarser quartz-included gold grains are prevalent, which implies a proximal source (within 50 meters). Conversely, the southern region displays finer and more evolved grains with choc marks, indicating a distal source (beyond 300 meters). Multivariate statistical analysis delineates seven sample classes corresponding to distinct mineralogical parageneses, pointing to varied sources, including high-grade metamorphic rocks, granitoids, pegmatites, basic and ultrabasic rocks, and banded iron formations (BIFs), which contributed minerals to the placer deposits. Gold predominantly originates from hydrothermal quartz veins, potentially undergoing peptization from the weathering of sulfides in the BIF. Therefore, in the Elogo region, exploration efforts for primary gold deposits should focus on identifying and tracing the hydrothermal system within the area. The integration of chemical analyses for both gold grains and associated minerals is imperative to enhance the precision of proposed geochemical prospecting strategies.


Evaluasi Pengembangan Pembelajaran Biologi Berbasis Media E- Learning pada Mahasiswa Tadris Biologi 3 Semester VI Universitas Islam Negeri Sumatera Utara

Abstract: The word learning is not far from a word, namely evaluation, along with the development of the times, and with the sophistication of existing technology, various kinds of media have emerged in the application of learning. E-Learning is the answer to all the sophistication that exists at this time. Where learning methods using technology are currently needed because of advances in technology in each place of learning, whether it's a school or college. This study uses the Research and Development method. The purpose of this study is to determine the development of E-Learning media-based learning. The results of the data that the researcher will obtain are the responses of Tadris Biology Students Semester 3 VI UINSU. Hopefully what is expected with this article can be an alternative choice or reference in conducting a study.


THE EFFECT OF THE STUDENT FACILITATOR AND EXPLAINING (SFAE) LEARNING MODEL REPORTING TECHNIQUES ON WRITING SKILLS

Abstract: Indonesian is one of the main subjects in the world of education in Indonesia. Language skills in these subjects include aspects of listening, reading, listening, speaking, listening and writing. The type of research used is quantitative. This research is called quantitative because the research data is in the form of numbers and the analysis uses statistics. Quantitative research is based on the philosophy of positivism which emphasizes objective phenomena that are studied quantitatively or using numbers, statistical processing, structure, and controlled experiments, (Sukmadinata, 2010: 53). In this subchapter two things are described. First, the skill of writing news texts before using the Student Facilitator And Explaining model assisted by reporting techniques for grade VIII students of SMP Negeri 15 Padang.


Analysis of the current situation and identification of problems in the evaluation of intellectual potential

Abstract: This paper discusses the theoretical and methodological foundations of the study of the intellectual potential of the population. In the presented research, intellectual potential is the subject of multidisciplinary studies, particularly philosophy, sociology, economics, technology, psychology and pedagogy. To this end, this paper analyzes intellectual potential as a multidisciplinary research field, and examines the main theories, concepts and approaches in this field. Relevant scientific literature on the evaluation of intellectual potential is studied, actual research fields are determined, and their current status is analyzed. The indicators for the evaluation of research activity and efficiency of science in research institutions and organizations are determined. This work explores the international experience in evaluating the activities of scientific organizations, and analyzes the purpose, organization and results of the evaluation in some countries. In the end, it examines the state-of-the-art of scientific potential in the scientific organizations and institutions of Azerbaijan, and presents the important results of the conducted fundamental and applied research and some indicators used in the evaluation of the activity of research organizations.


A medicolegal perspective on the issue of cyberbullying in Turkey: A retrospective descriptive study on medical specialization theses

Abstract: In recent years, the issue of cyberbullying has attracted the attention of researchers from many different fields as well as medical professionals. The aim of this study is to discuss the descriptive variables of medical specialty theses written on cyberbullying in Turkey in terms of forensic medicine. In this study, using the keywords "cyberbullying" and "cyber violence", theses registered in the Thesis Documentation Center of the Council of Higher Education were scanned. As a result of the screening, among 179 theses, 15 medical specialty theses related to the subject were examined within the scope of the study. The distribution of the 15 theses examined according to specialty areas was n:7 (46.6%) psychiatry, n:4 (26.6%) family medicine, n:1 (6.6%) forensic medicine, n:1 (6.6%) child and adolescent mental health and diseases, n:2 (13.3%) pediatrics. The peak in the writing of specialty theses was in 2019 (n:4). The population of the theses consisted of individuals aged 12-18 years in n:10 (66.6%) and 10-18 years in n:3 (20.0%) and university students in n:2 (13.3%), respectively. It was determined that questionnaires were used as the instrument and method in all of the theses analyzed. Most of the theses analyzed were conducted on adolescents between the ages of 12-18. There were no thesis studies among adults without or not a university degree. In future studies, these populations should also be examined. Due to its effects on people, it was thought that there may be an increase in the number of patients exposed to cyberbullying in the fields of psychiatry and forensic medicine. Therefore, it is necessary to increase medical awareness and conduct more studies on the cyberbullying issue.


Tutor Quality, Facilitating Conditions, and Perceived Usefulness as Predictors of Pre-service Teachers’ Satisfaction in an E-learning Environment

Abstract: Student satisfaction is one of the components used to measure the success and effectiveness of e-learning. This study examined the determinants resulting in pre-service teachers' satisfaction in an e-learning environment. Using structural equation modelling, student satisfaction was assigned as the endogenous variable, while tutor quality, perceived usefulness, and facilitating condition were the exogenous variables. Similarly, the construct was tested for its mediation effect towards student satisfaction from teacher quality and facilitating conditions, respectively. A total of (n=471) pre-service teachers enrolled in one of the colleges of education in Bhutan voluntarily responded to the survey questionnaire that followed a cross-sectional design. The results of the structural equation analyses indicated that student satisfaction was significantly determined by its antecedents, with tutor quality and facilitating conditions taken together resulting in an R2 of 0.82. Moreover, it was observed that tutor quality and facilitating conditions exerted a positive direct effect on the satisfaction dimension. The results also revealed that perceived usefulness has a statistically positive mediated effect on student satisfaction through tutor quality and facilitating conditions. The model proposed and tested in this study demonstrates that the tutor quality, facilitating conditions, and perceived usefulness can measure the e-learning satisfaction of students. The findings of this research may be helpful for administrators, policy planners and practitioners in the field of e-learning development to identify the elements that affect student satisfaction and further strengthen the existing e-learning programmes, particularly in developing Asian countries.


The equivalence of violence against women on social media: Acts constituting digital violence and their place in the Turkish Penal Code

Abstract: This study aims to explore women's perceptions of digital violence across different age groups, educational backgrounds, and marital statuses. It also investigates their awareness of digital violence on social media and their coping strategies when exposed to such acts. Additionally, their knowledge, attitudes, and behaviors regarding whether digital violence constitutes a crime were examined. Within the scope of the research, a survey form as a preliminary study was applied over the internet throughout Türkiye. According to the findings, 51.2% of the participants answered that they did not think they were exposed to acts of digital violence on social media. The findings revealed that 43.9% of the participants, who initially indicated that they had not experienced acts of digital violence on social media, stated that they were exposed to certain acts of violence such as being insulted and sworn at, falling victim to fraud, receiving threats and blatckmail, enduring harassing messages, discovering fake accounts created in their name with personal photos, etc., 47.7% of the participants stated that "Digital/Cyber Violence" is treated as a separate crime in the Turkish Penal Code. In response to the question ‘What would you do if you were exposed to the above action(s)?’ 83.3% of the participants marked the item 'I would block the related person from my social media accounts', 48.3% 'I would change the passwords of my social media accounts'. Consequently, looking at the answers of the women who participated in this survey, it was concluded that they were not aware that they were subjected to digital violence and that the actions they were exposed to were acts of digital violence. In this context, digital violence should be treated as a serious problem, and awareness, prevention and coping methods should be implemented.


A Literature Review of AI-Powered Systems for Monitoring Suspicious and Anomalous Activities

Abstract: This study of the literature focuses on the use of AI-powered systems in educational settings, examining the field of systems created to monitor suspicious and unusual activity. The paper explores the developments in data analytics, machine learning, and artificial intelligence that make advanced monitoring systems possible. It looks at the technology, approaches, and studies that have already been used to build these kinds of systems, highlighting how well they work to identify anomalous behavior in student environments. The assessment also identifies obstacles, moral issues, and prospective future paths in the creation and application of AI-driven solutions for boosting security and promoting a secure learning environment.


Fuzzy Conjoint Need Assessment Method for Model Development Justification

Abstract: This paper proposes a Fuzzy Conjoint Need Assessment Method (FCNAM) procedure for model development justification. The failure to select the analysis method and neglect the data when completing the needs assessment, before developing a model, leads to incorrect implementation of the justification. There is a misunderstanding in the requirements analysis, and certain elements that should be considered in the model's design and development may be overlooked. Therefore, as an alternative method in the model development justification procedure, the fuzzy conjoint analysis method together with the triangular fuzzy number is used as a data analysis medium in the need assessment survey. As an implementation and evaluation of the FCNAM procedure, a need assessment survey involving 53 mathematics teachers was conducted to justify the need for the development of a student's mathematics problem-solving ability (SMPSA) measurement model. This procedure is proven to comply with the guideline need assessment, which is to successfully confirm gaps related to mathematics problem-solving ability, describe the perspective of teachers as users and practitioners of the proposed model and synthesize the level of development needs of the model involved. This paper contributes knowledge about the application of the triangular fuzzy number-based conjoint method in perception surveys and introduces a more effective model development justification procedure.


Carbon-derived Black Soldier Fly (BSF) Larvae for Adsorption of Methylene Blue Dye: Characterization, Kinetic and Thermodynamic Studies

Abstract: The most effective substance for effectively adsorbing organic dyes is activated carbon (AC). We looked at the possibility of using Black Soldier Fly (BSF) larvae (Hermetia illucens Genome, MG) as a source for AC in this study. The two unique materials known as MG(300) and MG(500) were produced as a result of the larvae being heated for two hours at 300 and 500°C, respectively. These MG materials were further activated (MGO) using hydrogen peroxide (H2O2), and subsequently utilized for the adsorption of methylene blue (MB) dye. The objectives of this research include: (1) Investigation of the cabonization temperature and activation effects of the MG and MGO towards adsorption capacity; (2) Study of the physico-chemical properties of the adsorbents; (3) Exploration of the adsorptivity of MG and MGO in different parameters, including initial dye concentration, contact time, and temperature; (4) Examination of the kinetic and thermodynamic findings for the MG and MGO towards adsorption capacity. To analyze the obtained carbon, various analytical methods, including FTIR, WDXRF, SEM, and BET, were used. The equilibrium and kinetics of MB dye's adsorption behaviour on the AC were carefully investigated. It's interesting to note that within the first 10 minutes of contact time, all adsorbents demonstrated quick MB adsorption. The dye removal percentage (%) and adsorption capacities (mg×g-1) were measured at 65.7% and 6.33 mg×g⁻¹ for MG(300), 76.5% and 11.07 mg×g⁻¹ for MG(500), 91.1% and 16.41 mg×g⁻¹ for MGO(300), and 96.1% and 17.13 mg×g⁻¹ for MGO(500). Furthermore, the kinetics of dye adsorption using the pseudo-first-order (PFO) and pseudo-second-order (PSO) models were conducted, whereby PSO is the best fit with R2 in the range of 0.949 to 0.999.


Applying ADDIE Model to Develop Multimedia Lessons for Adolescent Reproductive Health Education in Uganda

Abstract: This study aimed to develop and evaluate a multimedia application intervention for adolescent sexual reproductive health education in secondary schools in Uganda. The lack of access to quality reproductive health education and services, exacerbated by the recent COVID-19 pandemic, contributes to unplanned pregnancies, health complications, and sexual and gender-based violence (SGBV) among adolescents in Uganda. Researchers and teachers collaborated to create multimedia lessons covering topics such as sexual reproductive health and rights (SRHR) and SGBV for adolescents in participating schools. The educational content and format were co-designed to better incorporate the local context and address priority issues by involving end users of the software (adolescents and their teachers) as content contributors during the development phase. The innovative multimedia application was developed using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. A student manual was also created as a teaching tool, providing educators with lesson introductions and guiding questions for post-lesson group discussions. The manual includes a simplified print version of the educational content, ensuring that students and teachers in schools without ICT resources can benefit from the intervention. Questionnaires were developed and administered to 14 Results indicate that all 14 trained teachers demonstrated improved knowledge about the use of multimedia applications to deliver adolescent-friendly sexual reproductive health (SRH) education. Additionally, all 14 teachers self-reported that the multimedia application is a feasible and acceptable method to deliver adolescent-friendly SRH education.


Characterizing non-Markovian off-resonant errors in quantum gates

Abstract: As quantum gates improve, it becomes increasingly difficult to characterize the remaining errors. Here we describe a class of coherent non-Markovian errors---excitations due to an off-resonant drive---that occur naturally in quantum devices that use time-dependent fields to generate gate operations. We show how these errors are mischaracterized using standard quantum computer verification and validation techniques that rely on Markovianity and are therefore often overlooked or assumed to be incoherent. We first demonstrate off-resonant errors within a simple toy model of $Z$ gates created by the ac Stark effect, then show how off-resonant errors manifest in all gates driven on a fixed-frequency transmon architecture, a prominent example being incidental cross-resonance interaction driven during single-qubit gates. Furthermore, the same methodology can access the errors caused by two-level systems, showing evidence of coherent, off-resonant interactions with subsystems that are not intentional qubits. While we explore these results and their impact on gate error for fixed-frequency devices, we note that off-resonant excitations potentially limit any architectures that use frequency selectivity.


Exploration of Unggan weaving in Minang culture: An ethnomathematics study

Abstract: Unggan weaving, an original cultural product of the Sijunjung people, especially in Unggan Village, is widely used as employee, traditional, and school uniforms once a week. However, the use of Unggan weaving in mathematics learning has yet to be widely explored. This research aims to reveal the mathematical elements of Unggan weaving as a learning vehicle more appropriate to students' culture. This research is qualitative research with an ethnographic approach. Data collection methods use in-depth interviews, documentation, and ethnographic notes. The interview subjects were Ernita, owner of Unggan Weaving (R-1), and Masniati, an employee of Unggan Weaving (R-2). Data analysis uses the results of interpretation and translation of the phenomena found, as well as based on the results of the informant's conception of the meaning and the informant's original language regarding the focus, which is combined verbally with the researcher's language after in-depth understanding through focus group discussions (FGD). The research results show that Unggan woven fabric contains mathematical elements of geometric transformation (translation, reflection, dilation, symmetry) and arithmetic number patterns. The results of this research can be recommended as material for developing school mathematics teaching materials based on local wisdom.


Computational thinking on concept pattern number: A study learning style Kolb

Abstract: This research aimed to determine how the number pattern concept's computational thinking characteristic picture was reviewed from the Kolb model's learning style. The research method used in this study is qualitative descriptive. The research was conducted at one of the state's small schools in Bandung. The research subjects consisted of 29 students in the ninth grade. One of the 29 study issues is selected with assimilator learning styles. The data-gathering techniques used are questionnaire tests, test instruments, and interviews. Angket is used to group subjects into four groups of learning style types. The test instrument was used to describe the computational thinking characteristics of high school students on the concept of number patterns, and the interview was used to strengthen the test summary results of the subject. The results of this study show that the characteristics of computational thinking that each type of learning style dominates are different. Computational thinking students with an assimilator learning style in solving mathematical problems of number patterns can solve issues by involving decomposition, pattern identification, abstraction and generalization, and algorithms. They can generalize patterns using accurate, thorough, complete, and systematic problem-solving strategies.


Deep Attributes: Innovative LSTM-based seismic attributes

Abstract: Summary Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long-Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as “Deep Attributes” because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic dataset to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute.


Examınatıon of famous use ın televısıon advertısements wıth electroencephalogram (eeg)

Abstract: Objective: The research aims to analyze the changes in brain activity of emotions caused by the use of celebrities in television (TV) commercials using Electroencephalography (EEG). It tries to determine the positive (liking, having fun) and negative (disliking, bored) emotions of the audience with Electroencephalography (EEG). Method: We tried to determine the emotional states of the volunteers who watched the commercials by examining the theta activities in different parts of their brains. A questionnaire was applied to 36 volunteers in our research before and after watching the advertisements. EEG recordings were made while the volunteers were watching the commercials. Theta activities obtained as a result of EEG recording were tabulated as duration. The hypotheses were tested by using the responses of the volunteers to the questionnaires and the EEG results comparatively with frequency tables and the Independent-Samples T test. Originality/Relevance: Both the preparation and broadcasting of TV advertisements, which have a very important place in marketing communication, can be very costly for businesses. Neuromarketing, which encourages interdisciplinary work, allows the use of methods, designs, theories and technologies used in neuroscience in areas such as marketing and advertising. In this study, with the help of EEG, one of the techniques used in neuromarketing, it has been tried to determine the emotions that the advertisements will create in the audience. It is among the few experimental studies conducted in Turkey. Results: Although EEG results shows that the activity intensity in the left hemisphere and left frontal lobe was higher than the activity intensity observed in the right hemisphere and right frontal lobe in the volunteers watching TV commercials, and the activity intensity in the left hemisphere and left frontal lobe in the volunteers where the advertisements created positive emotions, and the activity intensity in the left hemisphere and left frontal lobe in the volunteers, where the advertisements created negative emotions, statistically, as our hypotheses suggest, no significant activity difference was observed. Theoretical/methodological contributions: The research contributes to the scientific community by deepening the knowledge on neuromarketing, which is under development in Turkey as in many countries. It also provides clues about the use of neuromarketing in advertising research. This study will contribute to the preparation of effective advertisements by understanding the emotions of the audience. Managerial implications: Research findings can help businesses and advertisers prepare more effective commercials, thereby saving businesses costs.


MODERN MECHANISMS FOR THE DEVELOPMENT OF PUBLIC-PRIVATE PARTNERSHIPS IN VOCATIONAL (VOCATIONAL AND TECHNICAL) EDUCATION IN THE POST-WAR PERIOD

Abstract: The relevance of this article is defined by the need to substantiate the modern mechanisms for the development of public-private partnerships in vocational (vocational and technical) education to attract private investment and innovative technologies into professional training of skilled workers for various sectors of the economy in the post-war period. It will be based on mutually beneficial economic and organisational relations between state authorities, local governments, the private sector and vocational education institutions. The aim is to identify and justify the modern mechanisms for the development of public-private partnerships and to find out the possibilities of their use in improving the quality of vocational (vocational and technical) education in line with the demands of the labour market in the post-war period. Methods: studying of scientific sources, legislative and regulatory documents, empirical data to determine the state of public-private partnerships in the field of vocational (vocational and technical) education; theoretical analysis and synthesis, generalisation of scientific approaches to the problem under study in order to determine the mechanisms for the development of public-private partnerships in vocational (vocational and technical) education and drawing conclusions. Results: there are identified and substantiated the modern mechanisms for the development of public-private partnerships, including forms, means, and tools that are key to ensuring effective interaction between partnership participants to improve the quality of vocational (vocational and technical) education and meet the needs of the labour market in the post-war period. Conclusions: the modern mechanisms for the development of public-private partnerships in vocational (vocational and technical) education, which define the framework for cooperation between the state and the private sector and are implemented to improve the quality of vocational (vocational and technical) education, include: contract, institutional, investment, concession, leasing, franchise and project forms of partnership; means (contract for the provision of educational services, outsourcing, councils for vocational (vocational and technical) education and labour market, educational programmes, direct investment in infrastructure development, joint investment projects, leasing of equipment and technologies, leasing of educational premises; franchise of educational programmes, franchise of educational services, joint educational projects, joint research projects); instruments (service agreement, outsourcing agreement, agreement on the establishment of joint educational institutions, charter of a joint educational institution, concession agreement, agreement on the development and implementation of educational programmes, investment agreement, memorandum of understanding, leasing agreement, maintenance agreement, franchise agreement, educational services agreement, joint project implementation agreement, research cooperation agreement). Their use is key to achieving sustainable development and introducing innovations in vocational (vocational-technical) education.