Synthetic Hard Negative Samples for Contrastive Learning

Abstract: Abstract Contrastive learning has emerged as an essential approach in self-supervised visual representation learning. Its main goal is to maximize the similarities between augmented versions of the same image (positive pairs), while minimizing the similarities between different images (negative pairs). Recent studies have demonstrated that harder negative samples, i.e., those that are more challenging to differentiate from the anchor sample perform a more crucial function in contrastive learning. However, many existing contrastive learning methods ignore the role of hard negative samples. In order to provide harder negative samples for the network model more efficiently. This paper proposes a novel feature-level sample sampling method, namely sampling synthetic hard negative samples for contrastive learning (SSCL). Specifically, we generate more and harder negative samples by mixing them through linear combination and ensure their reliability by debiasing. Finally, we execute weighted sampling of these negative samples. Compared to state-of-the-art methods, our method can provide more high-quality negative samples. Experiments show that SSCL improves the classification performance on different image datasets and can be readily integrated into existing methods.


Utilizing Machine Learning in Legal Management to Enhance Supply Chain

Abstract: Innovative legal and supply chain complexity solutions include machine learning in legal management and intelligent supply chain governance. Complex laws and supply chains necessitate data-driven initiatives. Effective supply chain governance needs transparency, accountability, and risk management. Complex data-rich legal administration requires machine learning. Legal research, document analysis, and predictive analytics benefit from machine learning. Supply chain governance requires compliance and risk management. Machine learning principles demonstrate lawyers can switch careers. Innovation in legal tech comes from AI, blockchain, and cybersecurity. This novel machine learning strategy for legal and change and training management requires careful planning and implementation.


Worst-Case Analysis of Heapsort, Exactly

Abstract: Abstract This paper completes analysis of the worst-case running time of $ {\tt{Heapsort}}$ measured by the number of comparisons of keys performed while sorting. A derivation of an exact formula for the maximum number of comparisons of keys performed by $ {\tt{Heapsort}} $ on any array of size $ N \geq 2 $ is presented. It is equal to $$ \begin{align}& \nonumber 2(N-1)\lceil \lg N \rceil - 2 ^{\lceil \lg N \rceil +1} - 2 s_2(N) - e_2(N) + \min (\lceil \lg N \rceil, 3) + 5 + c, \end{align} $$ where $s_2(N)$ is the sum of digits of the binary representation of $N$, $e_2(N)$ is the exponent of $2$ in the $N$’s prime factorization and $ c $ is a binary function on the set of positive integers defined by $$ \begin{align}& \nonumber c = \left\{ \begin{array}{@{}ll} 1 \text{ if} \; N \leq 2 ^{\lceil \lg N \rceil} - 4 \\[4pt] 0 \text{ otherwise}. \end{array} \right. \end{align} $$ The above formula allows for deciding, in $ O(N \log N) $ time, if any given $ N $-element array is a worst-case array for $ {\tt{Heapsort}} $. Its proof yields an algorithm for construction, in $ O(N \log N) $ time, of worst-case arrays of arbitrary sizes $ N \geq 2 $ for $ {\tt{Heapsort}} $.


Quantifying the age peaks, age ranges and weights of detrital ages based on the EM algorithm

Abstract: Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age distribution. This study presents a streamlined approach, modeling the age distribution of detrital zircons using a normal mixture model, and employs the Expectation-Maximization (EM) algorithm for precise estimations. A method is introduced to automatically select appropriate initial mean values for EM algorithm, enhancing its efficacy in detrital geochronology. This process entails multiple trials with varying numbers of age components leading to diverse k-component models. The model with the lowest Bayesian Information Criterion (BIC) is identified as the most suitable. For accurate component number and weight determination, a substantial sample size (n > 200) is advisable. Our findings based on both synthetic and empirical datasets confirm that the normal mixture model, refined by the EM algorithm, reliably identifies key age parameters with minimal error. As a kind of probability density estimator, the normal mixture model offers a novel visualization tool for detrital data and an alternative foundation for KDE in calculating existing similarity metrics. Another focus of this study is the critical examination of quantitative metrics for comparing detrital zircon age patterns. Through a case study, this study demonstrates that metrics based on empirical cumulative probability distribution (such as K-S and Kuiper statistics) may lead to erroneous conclusions. The employment of the Kullback–Leibler (KL) divergence, a metric grounded in probability density estimation, is proposed. Reference critical values, simulated via the Monte Carlo method, provide more objective benchmarks for these quantitative metrics. All methodologies discussed are encapsulated in a series of MATLAB scripts, available as open-source code and a standalone application, facilitating wider adoption and application in the field.


"Virtual reality" as a tool for global manipulation of socio-cultural identity

Abstract: The subject of the article is the philosophical and cultural methodology of digital "virtual reality", comparing the declarations of developers with the practical possibilities and social consequences of using such technologies. The developers presented projects of online digital content services for all five senses using special equipment (glasses, headphones, interactive gloves, joysticks, costumes, printers of smells and tastes, etc.). It was assumed that virtual reality would surpass the reliability of previous multimedia content and interactive computer games, and the persuasiveness and attractiveness of traditional technical means of art and the press. Failure to fulfill these promises has led to significant losses since 2022 for leading companies developing digital virtual reality technologies Meta (recognized by the court as an extremist organization on 03/21/2022, activity is limited in the territory of the Russian Federation), Microsoft HapticLinks, Cave, AlloSphere, Teslasuit, VRealizer. There was a need to assess how and to what extent the methodology used and the predicted capabilities of digital virtual reality were correct. Philosophical and cultural analysis as a research method reveals: the developers of digital virtual reality promised the methodologically impossible, reducing human perception to the "sum" of the external "five senses" out of connection with the "self", inner sensitivity, bodily self-perceptions. A person is only partially amenable to algorithmized digital interaction; a critical barrier to digital virtuality is the socio-cultural identity of society and personality. The novelty of the results of the analysis lies in the conclusion: digital virtual reality in many respects is fundamentally inferior in effectiveness to traditional technical means of sensory and emotional impact and cognition, used both for creative and destructive purposes. In terms of creative potential, digital virtual reality is inferior to the technical means of art, science, education, upbringing, etc., and in terms of destructive capabilities, it is already used in the press and other mass communications technologies for manipulating public and individual consciousness, as well as psychotropic substances that cause addiction. National regulation of the development of digital "virtual reality" is recommended, aimed at protecting and developing the socio-cultural identity of Russian citizens and society, as well as for cognitive and educational purposes. It is recommended to limit the remote use of such developments to trusted domestic Internet services.


Technology for the formation of digital competencies of civil servants

Abstract: The subject of the research is the study of algorithms for the formation of competencies. The author examines various process structures of competence formation. The aim work is to develop a unified algorithm formation of digital competencies for civil servants. The specifics study will be focused on government civil servants who use digital technologies in their official activities. Special attention is paid to the issue of training and development of public civil servants. Author considers such an aspect of the topic as the impact of digital knowledge, skills and abilities on the digital economy of the Russian Federation. Author discusses various training technologies aimed at improving the qualification level of a civil servant. Author considers certain algorithmization of the formation of digital competencies in public administration. Features influence of educational activities on the creation of a special model of digital competencies of a civil servant. The following methods were used in the study: traditional analysis of documents (normative legal acts, scientific articles), modeling. As a result of the research author developed technology for the formation of digital competencies, aimed at developing the professional level of government civil servants and focused on the introduction of modern training models in public authorities. The area of possible implementation of the research results in such state bodies as the Government of the Russian Federation, the Administration of the President of the Russian Federation, the Ministry of Digital Development, Communications and Mass Media of the Russian Federation, etc. The novelty research is the development of completely new process of training and development of civil servants. The use of algorithmic technology for the formation of digital competencies will contribute to improving the qualification level of civil servant, improving public administration in a particular public authority, increasing measures to train and develop digital literacy of civil servant.


China Virus, Kung Flu, and MAGA: Countervalues and sociological fractionation on Twitter as evidenced by pro- and anti-Trump discourses in relation to Covid-19

Abstract: This paper seeks to investigate the indexical links (Silverstein, 2003) to social values activated by terms like "covid" and "virus" in tweets from users with opposing political leanings. Our data comes from a corpus of 12,607 tweets collected in both May and August 2020. We focus on tweets containing "Trump", as these occurred frequently throughout the corpus, to assess the ways in which Twitter users engage with discourses surrounding Covid-19 relative to the then-US President. Focusing on the local contexts of the tweets we, first, demonstrate the contrasting social values indexed by specific keywords and hashtags. We refer to these as countervalues (Bearth, 2005) that illustrate the multiple and competing valorisations of terms for Covid-19 and which lead to the reproduction of two main contrasting discourses. The first illustrates that Covid-19 is "fake", "a hoax", and is explicitly linked indexically with China and tends to appear in tweets by pro-Trump users. A second set of discourses emerges in opposition to the pro-Trump tweets where categorisations of the virus as fake or a hoax are described by users as moronic, links to China are described as racist, and users demonstrate an explicitly anti-Trump ideology. We conclude that the recirculation of these discourses is evidence of sociological fractionation (Agha, 2007), as we see the pro-Trump group resisting the scheme of values put forward by the anti-Trump group. To this end, we contribute to the body of research that sheds light on the participatory frameworks enabled by social media affordances.


Extension of the kinematics problem about the meeting of two bodies

Abstract: Abstract The well-known problem about the meeting of two bodies starting from two different points and moving along their line is extended by the general case when the bodies move in a plane at a non-zero angle to the given line. The task is solved graphically, analytically and using GeoGebra. This approach makes it possible to use IT technologies close to students in both standard and distance education.


Innovative technologies of physical and mathematical education implemented by using of fractal approach

Abstract: Abstract The implementation of the fractal approach to the teaching of physical and mathematical disciplines using computer modeling is substantiated.The formation of a fractal structure is established and iterations are defined that reflect the integrity and spontaneity of information presentation. The involvement of students of related specialties from two universities – University of Presov (Faculty of Humanities and Natural Sciences) and Uzhhorod National University (Faculty of Physics) – demonstrates the feasibility of using the fractal approach in training future teachers and scientists of natural sciences in higher educational institutions.


Mentoring as a way to the professional development of in-service physics teachers

Abstract: Abstract Nowadays education is facing new challenges based on the changes in the world and society. The generation of pupils has changed and their needs are different to what the teachers were used to. The change not only in teaching and learning methods but also in the way of thinking is necessary. To help teachers to find a different point of view on education and implement the changes, the Regional teachers’ support centres were established. The first two launched in 2021 and within three years there should be 40 centres across Slovakia. The main idea is to provide individual support for primary and lower secondary teachers as well as training in five main topics -i) school curriculum, ii) assessment and evaluation of pupils’ learning outcomes, iii) educational process and teaching style, iv) personalization and inclusive education, v) digitalization. Individual support is provided by a mentor in the form of class observations and mentoring conversations. The physics teachers can be involved and work on professional self-development in the topics they feel they want to improve. The paper reports on the mentors’ activities with the main focus held on the possibility of the in-service physics teachers’ professional development.


The entry profile of a pre-service physics teachers and the development of their professional competences

Abstract: Abstract In the paper, we reflect on various factors of motivation of pre-service physics teacher. Based on the data obtained from the entrance questionnaires of incoming students for the previous four years and a structured interview with current students studying in all grades, we create a professional entry profile of a future physics teacher student. Based on international standards, current trends, current and future needs of school practice, we characterize the professional competences of a physics teacher graduate. In the conditions of the Slovak educational space, we are looking for a breakthrough with the updated description of the field of study teaching and pedagogical sciences and standards for study programs. We present the goals and key elements of the university training of future physics teachers, educational methods, educational content and links to pedagogical practice.


Affordances of technology-supported trails in education of pre-service primary teachers in physics

Abstract: Abstract Outdoor activities focused on solving problems related to concrete objects, supported by technology, also known as trails, offer opportunities to develop not only content knowledge, but also the soft-skill of learners. The learner can be a pupil or a pre-service teacher, student of initial teacher education programme. Current students of initial teacher education programme are usually members of Generation Z and their education should be tailored to the needs of this generation. In this paper we designed an interdisciplinary trail connecting mathematics and science, particularly physics, knowledge and compared the solution process of pre-service teachers for primary schools and the primary pupils. The strategies almost did not differ, but pre-service teachers demonstrated deeper understanding of concept of measure.


Augmented reality as a tool for teaching physics to students with special needs

Abstract: Abstract Nowadays, teaching physics is becoming more and more challenging, particularly in regard to reduced number of physics lessons, lack of physics teachers and/or of the experimental equipment in the laboratories. This is becoming even more visible when students with special needs (SEN) are concerned. Consequently, physics rates among the most unpopular subjects in schools. We present a project ARphymedes plus („Augmented Reality made for student with special needs“), specifically designed to improve teaching of physics for students with special needs by applying Augmented Reality (AR). Within this project, we connect physics content with stories related to distinguished physicists, as well as to supporting experiments. For each phenomenon or physical law, examples in the form of videos, animations or 3D animated models are presented using AR. AR can be visualised in the book using tablets or smartphones, which are a daily part of students’ lives.


Q‐learning based adaptive Kalman filtering for partial model‐free dynamic systems

Abstract: Summary In this article, we propose an adaptive Kalman filtering based on Q‐learning for partial model‐free dynamic systems. First, a cost function is defined to iteratively update the prior state value when the model parameters are unknown. Then, the observations in a period of time are utilized to improve the accuracy and updating speed of the prior state estimation by means of the multi‐innovation least squares. Next, considering that the weight matrix in the cost function will change due to external noise noise and model mismatch, the innovation‐based adaptive estimation algorithm is presented to adjust the weight matrix by using the covariance of the information sequence. Finally, the proposed algorithms are applied to estimate the water level of a quadruple water tank system.


Paraconsistent logics as a Way of Expressing Objective Contradictions in Science

Abstract: The article focuses on the problems associated with the need to express in the language of science in a consistent way the movement and various kinds of changes taking place in the objective world by developing logics that would be tolerant of logical contradictions. The paper presents a brief history of the creation of paraconsistent (paraconsistent) logics, in which logical contradictions turned out to be quite permissible. It is noted that the priority in their development belongs to Russian scientists N. A. Vasiliev and I. E. Orlov, as well as Polish philosophers and logicians Y. Lukasevich and S. Yaskovsky; that since the seventies of the last century, the development of paraconsistent logics has assumed an international character; that interest in these logics is not abating at the present time. The possibilities of using paraconsistent logics presented in the works of foreign and domestic philosophers and logicians for the formalization of dialectics are discussed. The positive role of these logics is shown in solving many paradoxes in the foundations of classical propositional calculation and predicate logic, as well as in order to limit undesirable consequences when using IT technologies related to the processing of inconsistent or contradictory information. It is concluded that the use of paranoherent logics in the construction of models of individual fragments of dialectics contributes to its development as logic and as ontology, and indicates that the apparatus of non-classical logics as a whole is a very powerful means of studying and explaining many problems of theoretical cognition.


Digital Safeguards

Abstract: The rapid integration of internet and technological advancements over the past two decades has reshaped the global landscape, leading to transformative changes in various sectors.The emergence of Industry 4.0, conceptualized in 2011, has particularly revolutionized manufacturing and logistics by advocating for systematic digitalization technology integration to enhance efficiency and reduce costs. However, this technological evolution, while fostering efficiency and process optimization, also introduces vulnerabilities to cyber threats. The logistics sector, heavily reliant on interconnected systems like internet of things, faces potential risks from cyberattacks that target sensitive data across the interconnected supply chain. This chapter aims to address the intricate relationship between digitalization and the logistics sector, emphasizing the crucial role of digital safeguards in navigating cyber dangers. Furthermore, the chapter delves into the significance of visibility in supply-chain operations and explores technologies and practices for enhancing supply-chain visibility.


Logistics Industry in the Context of the Blockchain Technology

Abstract: Blockchain technology has garnered considerable interest from academics and businesses. One of the primary reasons to keep track of customer satisfaction and adoption is that customers don't use the product or service enough. Identifying the factors that influence the use and adoption of blockchain technologies will help solve the adoption problems effectively. Many significant firms, like Google, Amazon, and others, will embrace blockchain technology. It will indeed be found in the future and revolutionise logistics and transportation. Even though the advantages of blockchain technology have been well studied in the financial sector, using blockchain technology can help decrease significant logistical challenges such as border delays, product damage, errors, and multiple data entry.


Use of High-Resolution Multispectral Imagery to Assess the Role of Sand and Gravel Shoals for Flood-Infrastructure Interaction in the Yellowstone River, Montana

Abstract: Gravel and sand shoals may play a significant role for infrastructure impacts during flood events. This study uses high-resolution (30-cm panchromatic resolution at nadir) pan sharpened multispectral satellite images to map sand and gravel shoals near the Cinnabar Basin Bridge in Gardiner, Montana. While the bridge was not damaged during the flood event, erosion of the riverbanks and scour were observed with significant differences noticeable between both riverbanks, which may be related to the presence or evolution of shoals during the flooding. Toward analyzing the role of the shoals on local erosion and scour patterns, the goal of this study is to classify sediments based on soil type (sand, gravel, and cobbles) and color, using unsupervised K-means clustering and spectral information divergence (SID) with images taken in October 2022 and January 2023. The soil classifications from the K-means clustering and SID analysis are then compared to the pan sharped RGB images, drone images, and on-site observations and photos to assess the results from the classifications. The resulting classifications are compared for changes in the composition of the shoals over time from the October image to the January image, but also take advantage of receding water levels over the winter. Both the K-means clustering and SID analysis yield sediment classifications matching the site photos and drone images. Results show that unsupervised K-means clustering and SID can be used to classify sediments into different groups based on soil type and color, which may enable a deeper investigation of the impacts of sand and gravel shoals on infrastructure.


A Proper Methodology to Characterize the Associated Variability of UCS Data for the Metamorphic Rocks Based on Outlier Detection Methods

Abstract: This paper presents a methodology for analyzing the uncertainties of uniaxial compressive strength (UCS) test data for metamorphic rocks based on statistical and mineralogical analyses. The methodology consists of three steps: data treatment process, schistosity angle, and mineralogy. The treatment process involved different data treatment techniques to assess the variabilities of UCS data, and to determine appropriate outlier detection methods based on multiple decision-making criteria and engineering judgment. The results showed that adjusted boxplot and 2SD method provided more accurate outcomes in detecting outliers. The findings reveal that traditional boxplot may not be the best for treating UCS data. Moreover, the study examined the impact of schistosity angle and suggests removing samples within a specific angle range to decrease UCS variabilities. The mineralogical analysis examined the impact of various minerals on UCS variations, revealing a correlation between the dispersion and the variabilities of both hard and soft minerals.


ShopSculpt : Crafting Your Cart with Intelligent Ingenuity

Abstract: In the present world from small companies to giant company’s product recommendation system plays a very major role, also people are very much interested in online shopping these days, so Recommendations are utilized to make the customer's job easier and faster. The majority of these recommendations are made based on their previous transaction history and association rules generated from it. Apriori Algorithm is the most widely used method for generating association rules based on frequent product sets. But it has a drawback, while generating association rules it uses the whole transactions list and frequent product sets, ignoring the seasonal transactions for generating rules. Hence seasonal transactions are not considered for mining rules, if a person is buying a cake for Christmas, it’s very likely that recommender system recommends birthday balloons, birthday caps as recommendations. But actually, it should recommend Christmas related products for recommendation. To solve this problem, we proposed a model using chi square test with improvised Apriori algorithm. This research paper introduces ShopSculpt, an innovative automated shopping cart system designed to enhance the user shopping experience by leveraging intelligent algorithms. ShopSculpt employs a multifaceted approach, considering user shopping interests, patterns, seasonal trends, and current weather conditions to provide personalized and context-aware product recommendations. The aim is to create a dynamic and responsive shopping environment that adapts to individual preferences and external factors, thereby optimizing the user's shopping journey.


A Codebook Compression Method for Vector Quantization Algorithm

Abstract: This study introduces a novel approach to enhance the compression ratio of the vector quantization (VQ) algorithm by specifically targeting the compression of its codebook. The VQ algorithm typically generates an index matrix and a codebook to represent compressed images. The proposed method focuses on reducing the size of the codebook, which comprises N codewords, each with elements quantized into four levels. Each 8-bit element in a codeword is compressed to 2-bits, and the encoded codeword is accompanied by the minimum value and a threshold value in the codebook. Experimental results on benchmark color images, such as baboon, airplane, Lena, and others, demonstrate a significant reduction of 62.50% in the size of the VQ codebook.


The Information Fold

Abstract: This chapter brings the folded cosmos into dialogue with contemporary theories of surveillant information capitalism. Marks suggests an “only moderately paranoid” rejoinder to the darker theorizations of information's grip, especially where information-images are concerned. This relatively cheerful view is, however, moderated in turn by an analysis of the unsustainable energy consumption of information and communication technologies. The chapter then turns to media arts of the fold and presents a protagonist for the media of our coming collapse informatics: small-file movies. Other artworks include Jonathan Monaghan's digital animation Den of Wolves and Jawa El Khash's virtual-reality work The Upper Side of the Sky.


How Do Human Resources Practices Affect the Performance of the Employees in Syria?

Abstract: The purpose of this research is to analyses how HRM practices affect worker output in a high-pressure setting like the International Committee of the Red Cross (ICRC) in Syria. It contributes to literature by expanding the understanding of HRM. It provides useful insights and ideas for improving the efficiency of HRM procedures in businesses. This study highlighted the need for the ICRC to prioritize and invest in strong human resource management practices. Primary data was collected from ICRC employees through survey questionnaire. Findings show that HRM practices have a significant direct impact on the motivation and performance of ICRC employees. Meanwhile, psychological safety mediates the relationship between employee's motivation and employee's performance. On the other hand, employee's motivation partially mediates the relationship between HR Practices and employee's performance at ICRC.


The Internet of Things (IoT) Is Revolutionizing Inventory Management

Abstract: Every part of the supply chain has been affected by the rise of Industry 4.0 technologies. Many businesses have been trying out this modern technology to see if it can help them make more money. IoT devices make it simple for companies to keep track of an item's specific location, which helps inventory management (IM). The time it takes to find inventory is shortened as a result. The abundance of real-time data makes it possible to offer insightful information that supports strategic and tactical business decisions. Developing a supply chain into a fully integrated supply chain has several benefits. The differences between fourth-generation technology and earlier generations make it seem like traditional ways of restocking inventory don't adapt well enough to new technologies and can't handle IoT systems. Thanks to technological advancements, supply chains are undergoing rapid transformation.


Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

Abstract: Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification.


Optimized design of smart toilet spray bar flow channel based on CFD

Abstract: Abstract Currently, there are significant issues with the intelligent toilets available on the market, such as a large swing of the spray bar when spraying water column and eccentricity. This paper presents four optimization schemes for the intelligent toilet spray bar channel by analysing and redesigning a specific model. Computational Fluid Dynamics (CFD) technology was employed to analyse the water flow conditions inside the spray bar of the intelligent toilet. The calculation results indicate that for the same water inlet flow rate, the optimal structure in optimization scheme 1 is the block with a length (L) of 1.5 mm. In optimization scheme 2, the optimal structure is the block located at position (3). In optimization scheme 3, the optimal structure is a 15° rotation, which exhibits the least swirls and pressure loss while maintaining the prototype structure. When the prototype structure is modified, the optimal structure in optimization scheme 4 is the excision bend pipe. This structure exhibits the lowest swirl number and pressure loss among all the optimization schemes. The determined structure offers valuable insights for enhancing the flow characteristics of the improved product.


Adolescent Reading Literacy in View of Family Support and Reading Duration in Middle School Students

Abstract: Literacy is an important skill and relates to the ability to listen, speak, write, and count. This study aims to analyze the influence of family support and duration of reading on adolescent reading literacy. This study uses a quantitative survey approach research design. The number of samples in this study were 127 students through cluster random sampling method. Data analysis was carried out in several stages, namely data coding, SPSS analysis, namely inference test, normality test, correlation test, and multiple linear regression test. The results showed that the dominant (36.2%) subjects were 14 years old. As many as (30.7%) are 15 years old. The results showed that half (50%) of the research subjects read books for entertainment several times a year. The results showed that the majority (68%) of the study subjects had moderate reading duration and the family support given to the sample was very high (93%). The results of the study also show that family support and duration of reading affect adolescent reading literacy. The result of this study have implications for parent mindset to support literacy from an early age and adolescent to read more frequently.


The Effect of the Director's Authentic Leadership that MZ Generation Early Childhood teachers acknowledge on the Constructive Voice Behavior of MZ Generation Early Childhood Teachers: Examining the Parallel Mediating Effect of Organizational commitment

Abstract: The aim of this study was to examine how the MZ generation (born between 1980 and 2005) early childhood teachers perceive preschool directors' authentic leadership and how their perceptions influenced their organizational commitment and constructive voice behaviors. To this end, a survey was conducted on 242 MZ generation early childhood teachers currently employed in kindergartens and daycare centers across South Korea. Data analysis, performed using SPSS version 26.0, involved calculating frequencies, percentages, means, and standard deviations. Additionally, the PROCESS macro for SPSS version 3.5, specifically model 4, was utilized to confirm parallel mediation effects. Bootstrap analysis was employed to validate the statistical significance of the mediating effects. The findings of this study were as follows. First, the level of the director's authentic leadership recognized by MZ generation early childhood teachers, constructive voice behavior, and organizational commitment of MZ generation early childhood teachers were all reported as ranging from moderate to high. Second, parallel partial mediating effects were found through emotional and normative commitment in verifying the mediating effect of organizational commitment in the relationship between the director's authentic leadership recognized by MZ generation early childhood teachers and the constructive voice behavior of MZ generation early childhood teachers. These results suggest that the director's authentic leadership influences the constructive voice behavior of MZ generation early childhood teachers, and emotional and normative immersion can play a mediating role in this process to promote constructive voice behavior.


The Novel Mathematical Model and Methodology for Computer Simulation of Magnetic Field in a Nonlinear Medium

Abstract: Introduction. Magnetic devices of various types are used in production equipment. Designing and modernizing such equipment requires a signifi cant bulk of calculations of magnetic fi elds and parameters of magnetic devices. This task is diffi cult due to large dimensionality of the system of equations and nonlinear properties of magnetic materials.Problem Statement. Due to the nonlinearity of diff erential and integral equations on which these calculations are based, they need to be solved numerically by iterative methods, the convergence of which is often uncertain. This requires powerful computing tools and considerable time. Therefore, the problem of improving mathematical mo dels and increasing the computational effi ciency of the corresponding algorithms is relevant.Purpose. To develop a mathematical model of a magnetic fi eld in a nonlinear medium in the form of a surface integral equation for a quasilinear space and a computer modeling technique with increased computational effi ciency.Material and Methods. The material of the study is the mathematical models of the magnetic fi eld in a nonlinear medium of magnetic materials and the computational properties of the corresponding algorithms. The methods of vector analysis of diff erential operators and synthesis of modifi ed formulas in the magnetic fi eld equations have been used in the work.Results. The newest mathematical model of the magnetic fi eld in which the volumetric equation for a nonlinear medium is reduced to a surface equation in quasi-linear space, which reduces the dimensionality of data arrays by one order of magnitude and the number of computational operations by two orders of magnitude, has been substantiated. On this basis, a methodology for computer modeling of fi elds with the use of a unifi ed magnetization curve has been developed.Conclusions. The applicability of this methodology to various magnetic materials and its effi ciency have been confi rmed by the example of a model problem of practical importance for improving the algorithms for calculating and analyzing magnetic fi elds in magnetic systems with nonlinear elements.


Determination of Elongated Aggregates through Computer Vision-Based Technique

Abstract: The traditional measurement technique for finding elongated aggregates involves sieve analysis followed by passing each particle through an elongation gauge. In this study, machine learning has been used as a computer vision-based methodology to determine the elongation index of coarse aggregate particles. Images of aggregates were captured, and an algorithm was developed for contour detection of each particle. The particle size and the number of elongated particles were then computed in an automated form. The laboratory test results were compared with the results obtained from a machine learning-based algorithm. The results so obtained on the segregation of particles in various sizes give significant accuracy with a deviation of ±1 number of particles. The method so developed can be used by researchers and field engineers for the determination of the physical parameters of aggregates.


Simplicity versus Complexity in Machine Learning Models—Focusing on Soil Resilient Modulus Prediction

Abstract: The resilient modulus (MR) of subgrade soil is an important factor in pavement design, which can be determined through repeated load laboratory testing. However, due to its high cost, time-intensive nature, and intricate process, geotechnical engineers and researchers have consistently expressed interest in creating an MR prediction model using alternative soil test data. With advancements in prediction tools using machine learning (ML) algorithms, during the past decade, much research has been focused on implementing ML algorithms for the development of MR prediction models. Many recent studies have been focused on using what is referred to as "black box" models, such as artificial neural networks (ANN). While these black box models provide high prediction accuracy, they are not explainable. In other words, there is no closed-form solution in which the relationship between the predicted label and the input features is explicitly stated. Moreover, these black box models are computationally complex and require advanced coding knowledge, making them inaccessible to many design professionals. In this paper, for illustration purposes, a dataset of 279 MR test results that have been extensively used for ML-based model development of MR is adopted from the literature. Different regression approaches, from simple regression models to advanced ML algorithms, are applied to this dataset, and the developed models are compared in terms of accuracy, simplicity, and explainability. Additionally, the bias and variance in error are investigated for different algorithms. It is concluded that the improved accuracy of the developed models is not always adequate to justify the effort, time, and complexity of adopting complex advanced algorithms instead of simple regression models. Moreover, it is shown that overly complex algorithms could lead to high variance, causing the algorithm to model random noise in the training data.


Natural Language Processing and Deep Learning Approaches for Multiclass Document Classifier

Abstract: With the recent growth of the internet, the volume of data has also increased. A large section of the internet is full of documents, which may contain data, big data, formatted and unformatted data, structured data, and unstructured data. The increase in the amount of this unstructured data results in making it difficult to manage data. As it is difficult to classify the increasing volume of data for various purposes manually, automated classification is required. This paper overviews different approaches to Natural Language Processing and Deep Learning for content-based classification.


Measuring the Influence of Artificial Intelligence (AI) on Online Purchase Decisions-In Case of Indian Consumers

Abstract: The industry's ostensible technological sophistication contributes to the highly dynamic ecommerce environment. When new technology is made available, many of these companies openly adopt it to stay competitive market. Internet shop owners have embraced a variety of technologies, including artificial intelligence. Technology is rapidly evolving. Artificial intelligence significantly facilitates the conversion of interest into purchase intentions. The majority of the information gathered by e-commerce companies is about prospective customers or prospects. AI can be used to interact with warm leads or cold leads who have indicated interest in a brand or product. Furthermore, AI has been demonstrated to be a highly constructive technique of retargeting customers. Artificial intelligence advancements have increased consumer satisfaction even further, making it even more critical in today's climate. This paper will investigate the factors that influence artificial intelligence's practical implacability in order to better understand how it affects consumers' online purchase plans. This paper explores the various variables influencing consumers' purchase intentions for e-retailing using a technology-based model as the foundation. This study has developed a model that shows how business organisations can incorporate artificial intelligence into retailing in order to comprehend consumer requirements and encourage technology adoption. This research has looked more closely at consciousness, subjective norms, and faith as constructs that heighten the tenacity of artificial intelligence.


Formation of scientific-analytical and communication skills among students of humanities at the international university (Generation Z Humanities Students at an International University)

Abstract: Today, recruiting organizations, when selecting potential candidates for vacant positions, emphasize the importance of university graduates having developed communicative and scientific-analytical competencies, which are the main components of "soft skills" for students of the humanities cycle, more conducive to the formation of professionalism than "hard skills", due to the shift in society's priorities towards democratization and humanization socio-psychological principles of performance of official duties within the framework of professions. The purpose of this study is to identify methods and techniques for the formation of scientific, analytical and communicative skills at the international University among students of humanities. Intercultural communication in the context of the educational process, on the one hand, complicates the development of these competencies, on the other hand, requires improving the principles of teaching in the context of cultural dialogues, which determines the formation of the studied skills, taking into account the national and cultural specifics of the studied language and communication of the university where the foreign student is studying. These aspects are a prerequisite for successful integration into a potential professional environment. Research methodology: theoretical analysis of scientific literature on the issue under consideration, content analysis, descriptive and comparative methods. The novelty of the research is due to modern trends in improving the "soft competencies" of representatives of all professions in harmony with the global scientific space through the prism of effective intercultural communication. The results of the study indicate a mutual correlation of scientific, analytical and communicative skills, the synthesis of the main components of which contributes to the formation of metacognitive characteristics of students. It is advisable to form the studied competencies through role-playing games, memes, situational tasks, communicative professionally oriented trainings, the method of active problem-situational analysis ("case-study")


Leakage detection based on variational mode decomposition and long short-term memory neural network

Abstract: Abstract In the process of long-term continuous operation, fluid transportation pipelines are prone to leakage accidents. Therefore, this study investigates the detection of small-sized leaks with a leakage aperture of 13 mm in pipes with a diameter of 100 mm. The experimental investigation is conducted under the following operating conditions: volume flow of 25-80 m 3 /h, pressure of 100-200 kPa. The variations in volume flow and pressure signals during leak occurrences are analysed. To mitigate the interference caused by noise, the variational mode decomposition (VMD) method is introduced. The VMD effectively reduces noise interference in the signals. Furthermore, the denoised signals are utilized to establish a long short-term memory neural network (LSTM). The LSTM model achieves a high accuracy rate of 91.67% for the entire dataset.


Selection among site-dependent structurally constrained substitution models of protein evolution by approximate bayesian computation

Abstract: The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection.We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models.ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.


ON THE TITLE AND LEXICAL FEATURES OF THE WINTER OLYMPICS NEWS REPORT IN CHINA DAILY

Abstract: With the successful hosting of the 2022 Winter Olympics, people all over the world have turned more attention to Chinese winter sports. China Daily is major media to know the news about it. This article made an analysis of the title and lexical features of the Winter Olympics through the news from China Daily.


Adaptive novelty detection with false discovery rate guarantee

Abstract: This paper studies the semisupervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method that is able to wrap around any probabilistic classification algorithm and control the false discovery rate (FDR) on detected novelties in finite samples without any distributional assumption other than exchangeability. In contrast to classical FDR-controlling procedures that are often committed to a pre-specified p-value function, AdaDetect learns the transformation in a data-adaptive manner to focus the power on the directions that distinguish between inliers and outliers. Inspired by the multiple testing literature, we further propose variants of AdaDetect that are adaptive to the proportion of nulls while maintaining the finite-sample FDR control. The methods are illustrated on synthetic datasets and real-world datasets, including an application in astrophysics.


StarTrek: Combinatorial variable selection with false discovery rate control

Abstract: Variable selection on the large-scale networks has been extensively studied in the literature. While most of the existing methods are limited to the local functionals especially the graph edges, this paper focuses on selecting the discrete hub structures of the networks. Specifically, we propose an inferential method, called StarTrek filter, to select the hub nodes with degrees larger than a certain thresholding level in the high-dimensional graphical models and control the false discovery rate (FDR). Discovering hub nodes in the networks is challenging: there is no straightforward statistic for testing the degree of a node due to the combinatorial structures; complicated dependence in the multiple testing problem is hard to characterize and control. In methodology, the StarTrek filter overcomes this by constructing p-values based on the maximum test statistics via the Gaussian multiplier bootstrap. In theory, we show that the StarTrek filter can control the FDR by providing accurate bounds on the approximation errors of the quantile estimation and addressing the dependence structures among the maximal statistics. To this end, we establish novel Cramér-type comparison bounds for the high-dimensional Gaussian random vectors. Compared to the Gaussian comparison bound via the Kolmogorov distance established by Chernozhukov, Chetverikov and Kato (Ann. Statist. 42 (2014) 1787–1818), our Cramér-type comparison bounds establish the relative difference between the distribution functions of two high-dimensional Gaussian random vectors, which is essential in the theoretical analysis of FDR control. Moreover, the StarTrek filter can be applied to general statistical models for FDR control of discovering discrete structures such as simultaneously testing the sparsity levels of multiple high-dimensional linear models. We illustrate the validity of the StarTrek filter in a series of numerical experiments and apply it to the genotype-tissue expression dataset to discover central regulator genes.


Adaptive variational Bayes: Optimality, computation and applications

Abstract: In this paper, we explore adaptive inference based on variational Bayes. Although several studies have been conducted to analyze the contraction properties of variational posteriors, there is still a lack of a general and computationally tractable variational Bayes method that performs adaptive inference. To fill this gap, we propose a novel adaptive variational Bayes framework, which can operate on a collection of models. The proposed framework first computes a variational posterior over each individual model separately and then combines them with certain weights to produce a variational posterior over the entire model. It turns out that this combined variational posterior is the closest member to the posterior over the entire model in a predefined family of approximating distributions. We show that the adaptive variational Bayes attains optimal contraction rates adaptively under very general conditions. We also provide a methodology to maintain the tractability and adaptive optimality of the adaptive variational Bayes even in the presence of an enormous number of individual models, such as sparse models. We apply the general results to several examples, including deep learning and sparse factor models, and derive new and adaptive inference results. In addition, we characterize an implicit regularization effect of variational Bayes and show that the adaptive variational posterior can utilize this.


A Moderating Influence of Organisational Capabilities on the Relationship between Teacher Competency and Techer Performance within Private Higher Education in Sichuan

Abstract: In the dynamic landscape of private higher education in Sichuan, China, the relationship between teacher competency and performance stands as a critical factor shaping academic excellence and institutional success. This study investigates the nuanced interplay between teacher competency, organizational capabilities, and teacher performance within the context of private higher education institutions (PHEIs) in Sichuan. Drawing upon resource-based theory and organizational capability frameworks, this research aims to uncover how organizational capabilities moderate the relationship between teacher competency and performance. The study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data from faculty members and administrators. The quantitative phase involves surveying a sample of teachers from various disciplines across multiple private higher education institutions in Sichuan. Measures of teacher competency, organizational capabilities, and teacher performance will be assessed using validated scales. Statistical techniques such as regression analysis and moderation analysis will be employed to analyze the quantitative data. In the qualitative phase, semi-structured interviews with faculty members and administrators will provide rich insights into the mechanisms through which organizational capabilities influence the relationship between teacher competency and performance. Thematic analysis will be utilized to identify patterns and themes emerging from the qualitative data. This research contributes to both theoretical understanding and practical implications within the realm of private higher education management. By elucidating the moderating role of organizational capabilities, this study sheds light on strategies that PHEIs in Sichuan can employ to optimize teacher performance through enhancing teacher competency within the organizational context. The findings of this research hold significant implications for policymakers, administrators, and educators aiming to foster a conducive environment for effective teaching and learning in private higher education institutions in Sichuan, China, and beyond.


LIVE-STREAMING PERFORMANCE IN INQUIRY-BASED SCIENCE LEARNING WITH ACTION: TEACHERS’ PERSPECTIVES

Abstract: Online teaching has become an imperative approach in today’s society. However, as an essential approach, using live streaming to teach students in small groups, particularly rural primary school students, has not been extensively studied. To address this gap, an inquiry-based model, predict-do/observe-quiz/discuss-explain-transfer (P-D/O-Q/D-E-T), was adopted for live streaming with action, and its performance was analysed. Reflection by teachers can lead them to a deeper understanding to capture the profound impact of an educational program. In the present study, eight teachers who had experience assisting rural students in this experiment were invited to rate their points of view on immersion, social interactivity, humanness, and value perception. Examining the consistency of teachers’ viewpoints using the hermeneutic method, the results showed that they highly supported viewing these four constructs using live streaming to conduct inquiry-based science learning with action. As expected, using live streaming to deliver teaching with the P-D/O-Q/D-E-T approach can enrich other online science teaching. Keywords: educational program, inquiry-based science learning, live-streaming, teachers’ perspective


THE IMPACT OF THE OK4R MOBILE PLATFORM ON VOCATIONAL STUDENTS’ IMPROVEMENT OF SCIENCE READING COMPREHENSION AND LOGICAL THINKING

Abstract: In Taiwan, the education system places a strong emphasis on higher education. Students who perform well in the National High School Entrance Examination are given priority admission to public high schools, while those with lower scores tend to enroll in vocational schools. It's worth noting that students with low scores in this examination often struggle with reading and natural literacy, typically scoring below level 2 in PISA assessments. This study examined the impact of implementing the mobile platform OK4R reading strategy on students' comprehension of popular science reading and their logical thinking abilities. The research was conducted with two classes of first-year vocational school students. The findings indicate that when students used the OK4R mobile platform for popular science reading, it had a positive effect on their popular science reading comprehension and their logical thinking skills. Notably, female students outperformed their male counterparts. The study also revealed that several factors, including gender, basic abilities, and other variables, influenced the effectiveness of the OK4R mobile platform for vocational students. Those with lower science scores appeared to benefit the most from this approach. Keywords: OK4R, popular science reading, logical thinking, vocational high school students, reading comprehension


Geology and Geochemical Characterization of Basement Rocks Around Burumburum Area North Central Basement Complex Nigeria

Abstract: The geology and the geochemical characterization of rocks around Burumburum area north central basement complex of Nigeria was studied. Twelve (12) rocks samples were prepared for petrographic studies through a standard procedure while thirty (30) whole rocks samples were analyzed using X-ray Fluorescence (XRF) and Inductively Coupled Plasma Mass Spectrometry(ICP-MS). The result of the field investigation revealed that the study area is underlain by migmatites, gneisses, biotite microgranites, fine grained biotite granites, medium grained biotite granites, porphyritic biotite-hornblende granites, granodiorites, syenites, diorites, dolerites, quartzite, pegmatites and aplites. The geochemical characterizations of the granites, granodiorites, syenites based on Na&lt;sub&gt;2&lt;/sub&gt;O/K&lt;sub&gt;2&lt;/sub&gt;O versus SiO&lt;sub&gt;2&lt;/sub&gt; showed acidic compositions while diorites and dolerites are intermediate to basic compositions. The granites, granodiorites, syenites, diorites and dolerites are generally peraluminous (ASI&gt;1.1) to metaluminous (ASI≤1). The granitic rocks, diorites, dolerites and syenites plotted mainly in shoshonite series while the granodiorites occur in High-K calc-alkaline fields. The granitic rocks, syenites, diorite and dolerites based on A/CNK versus SiO&lt;sub&gt;2&lt;/sub&gt; are of I-type while granodiorites are of S-type. The multi-elements discrimination trends for granites, granodiorites, syenites, diorites and dolerites revealed relative enrichment in Large Ion Lithophile Elements (LILE: Ba, La, Rb, and Th and depletion in High Field Strength Elements (HFSE: Nb, P, Ti and Sr) which indicates crustal contamination of magma. The enrichment in the light rare earth element (LREE) relative to heavy rare earth element (HREE) for granodiorites, fine grained biotite granites, medium grained biotite granites (except medium grained biotite granites S12 and S14 with positive Eu), porphyritic biotite-hornblende granites and syenites with negative Eu anomaly suggest moderate to high degree of fractionation. The enrichment in light rare earth element (LREE) for diorites and dolerites relative to moderate to flat HREE with weak negative Eu anomaly indicates low degree of fractionation. The Y+Nb vs Rb, Y vs Nb, Ta + Yb vs Rb and Yb vs Ta tectonic discrimination diagram revealed that granites and syenites plotted mainly within volcanic arc granite, syn-collisional granite and in the within plate granites fields, the granodiorites clearly plotted in the within the plate granites field. The tectonic ternary molecular proportions MgO – FeOt – Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3 &lt;/sub&gt;revealed that dolerites and diorites plotted mainly in the spreading center island field.


Characterization of causal ancestral graphs for time series with latent confounders

Abstract: In this paper, we introduce a novel class of graphical models for representing time-lag specific causal relationships and independencies of multivariate time series with unobserved confounders. We completely characterize these graphs and show that they constitute proper subsets of the currently employed model classes. As we show, from the novel graphs one can thus draw stronger causal inferences—without additional assumptions. We further introduce a graphical representation of Markov equivalence classes of the novel graphs. This graphical representation contains more causal knowledge than what current state-of-the-art causal discovery algorithms learn.


Achieving High Efficiency: Resource sharing techniques in artificial neural networks for resource-constrained devices

Abstract: Abstract Artificial neural networks emerged in the early 1940s as a connectionist approach to modeling the behavior of interconnected living nerve cells within the intricate cognitive framework of the brain. Over the years, this idea has undergone unprecedented proliferation of ever-expanding application areas, accompanied by an exponential surge in the mathematical formalism employed. This prodigious growth poses severe demands on computational hardware infrastructure. The current research suggests a novel approach for optimizing artificial neural network architectures using resource-sharing techniques. It is discussed the concept of merging context-switching and time-division multiplexing in building a single-neuron artificial neural network with the aid of an FPGA device. This would benefit the significant reduction of hardware resources and the possibility of creating small yet very efficient artificial neural networks without deteriorating the dynamical system performance.


Neural network approximation in the Lipkin-Meshkov-Glick model

Abstract: Abstract In this paper we approximate the wave function of the Lipkin-Meshkov-Glick (LMG) model by a simple feed-forward neural network. By using the NetKet toolkit we obtain the ground state energies of LMG systems with different interaction strengths and find out that they are very close to the exact values.


Artificial intelligence in international business

Abstract: Abstract Artificial intelligence is poised to transform international business through the adoption of new digital technology practices as part of the fourth industrial revolution. The how, when, and why of artificial intelligence implementation in international business is still somewhat unknown but will likely significantly influence performance outcomes, market interpretation, risk prediction, consumer interactions, and technology‐based metrics. With greater emphasis on artificial intelligence‐based tools, the future practice of international business will require digital literacy. This means business managers need to improve the way they analyze artificial intelligence in terms of appreciating its usefulness while maintaining privacy and mitigation strategies. In this editorial, we discuss the current developments regarding artificial intelligence in international business with the goal of identifying future research areas of importance.


Code-Switching and Slang: An Analysis of Language Dynamics in the Everyday Lives of Generation Z

Abstract: This study explores the dynamics of code-switching used in Generation Z’s (Gen-Z) communication practices, revealing it as a dynamic and adaptive strategy employed in diverse social contexts. Gen-Z exhibits context-specific types of code-switching, utilizing informal and casual forms, such as slang and non-formal language, in peer interactions. Within familial settings, a seamless transition between formal and informal language aligns with established family norms, while professional environments witness the use of formal and professional language. The strategic use of code-switching and slang by Gen-Z serves as a nuanced communication approach, catering to specific social scenarios. In informal peer contexts, the incorporation of slang fosters a sense of camaraderie, while code-switching acts as a tool for expressing emotions and personal identity, reflecting individuals’ comfort in conveying specific feelings or ideas in a chosen language. This research contributes to a comprehensive understanding of Gen-Z’s language practices, showcasing their adeptness in navigating diverse linguistic landscapes, expressing identity, and adapting to social expectations. Such insights are pivotal for fostering effective intergenerational and cross-cultural communication, bridging understanding between different linguistic and cultural groups.


Switching the Script: Unveiling the Linguistic Dance of Code-Switching in Nigerian Online Skits

Abstract: This journal article presents an in-depth investigation into the intricate phenomenon of code-switching in Nigerian online comic skits, a burgeoning genre of digital entertainment. Within Nigeria, a nation characterized by linguistic and cultural diversity, online comic skits have emerged as a potent medium for creative expression, social commentary, and cultural representation. This research delves into the analysis of the different roles and types of code-switching employed by content creators in these skits, elucidating their linguistic, sociocultural, and comedic dimensions. Conducting a descriptive qualitative analysis, Poplack's (1980) Model of Types of Code-Switching and a functional model adapted from Hoffmann (1991) and Appel and Muysken (2005) were applied. Five skits, selected based on substantial viewership and the popularity of content creators, formed the core of this study. Our findings indicate that all three types of code-switching are prevalent in the skits, with inter-sentential switching being the most frequent. Additionally, the seven functions of code-switching manifest in the skits, with the referential function emerging as the most frequent. The primary use of code-switching in these skits revolves around fostering humor, expressing strong emotions, and portraying the educational status of the characters.


Design, Fabrication, and Experimental Analysis of a PV Panel for a Smart Sunflower System

Abstract: The Smartflower, an innovative compact energy-generation system inspired by sunflowers, stands out in energy innovation. Unlike traditional photovoltaic (PV) panels, it integrates foldable solar cells within a foundational structure for solar tracking aligned with the sun's path. The present paper focuses on designing, fabricating, and analyzing a proposed Smartflower-PV panel solar system. The study aims to comprehensively evaluate the performance of the proposed PV panel under different atmospheric conditions. The significant impact of insolation and temperature on the panel's efficiency was revealed by comparing empirical results from the PV sunflower panel with analytical calculations using MATLAB (m. file code). Enhanced solar radiation improved the system’s performance and efficiency, resulting in higher power output. Analytical insights showed a direct correlation between a 104% increase in solar radiation and parallel increases of 115% in peak power production and 100% in output current. Conversely, higher temperatures reduced power output, with a 400% temperature rise causing an 11.11% power reduction. Empirical observations align with analytical analyses under equivalent conditions, validating the model’s accuracy. This study serves as a catalyst and guide for completing and advancing the Smartflower system's manufacturing, including control, tracking, and the entire energy-generation framework.


Flood Modelling of Premulung River, Bengawan Solo

Abstract: Premulung River or commonly known as Kali Premulung is one of many branches of Bengawan Solo River in its upstream area. This river pass through one of the most historical cities in Central Java, Surakarta. The overcapacity of this river leads to flood event that has a negative impact on humans. The purpose of this research is to analyze the Premulung River capacity and simulate the flood caused by rainfall design. The hydrological matter was analyzed using Hydrognomon and HEC-HMS while flood modelling was analyzed using HEC-RAS software one- and two-dimension (1D & 2D) simulation. Model calibrations were carried out based on historical flood events (depth, duration, and area of inundation) and local interview due to data limitation. Based on the simulation, the flood modelling shows that the current capacity of Premulung River cannot accommodate its peak discharge for two (Q2) and twenty years (Q20) return period flood. There are two main spots identified flooded due to Q2 flood with depth varies from 40 to 80 cm and duration from 4 hour to 7 hour. For Q20 flood, there are also two same spots identified flooded with depth varies from 1.2 m to 1.8 m and duration from 6 hour to 9 hour. The result of this study can be a reference for flood dike design in the future which still require further detailed investigation.


USE OF SELECTED STRATEGIES IN LEARNING AND THEIR IMPACT ON LIFELONG LEARNING

Abstract: The article focuses on selected learning strategies that we acquire in school and extracurricular environments. The degree of their use in learning is an important determinant of lifelong learning. A learner who has mastered these strategies has a higher motivation for further education, higher self-confidence and achieves better results. In the empirical part of the work we describe work procedures, research objectives, research set and research methods. The quantitative design of the work is aimed at identifying the rate of use of individual strategies of critical thinking among secondary school students using a questionnaire with Likert-type questions. The results of the research were processed by mathematical and statistical methods. At the end of the work recommendations for pedagogical practice are proposed based on the theoretical analysis and specific research results.


POUZDANOST PRILAGOĐENE INAČICE SKALE SAMOPROCJENE PREDMETNO SPECIFIČNIH KOMPETENCIJA ZA RAD S UČENICIMA S TEŠKOĆAMA U NASTAVI TJELESNE I ZDRAVSTVENE KULTURE

Abstract: Teachers in primary education have both generic-specific competencies and subject-specific competencies. Generic competencies refer to general competencies and cover theoretical and practical knowledge, while subject-specific competencies refer to the application of knowledge of the substrate science of the subject area in order to realize the set tasks. The goal of the research is to examine the reliability of the Self-Assessment Scale of competencies subject-specific to the physical and health education area needed to work with students with disabilities. The research was conducted on a suitable sample of final year students of the teacher and preschool teacher education study programmes, and the total sample of respondents (N = 84) included 29 female students of the teacher education study programme and 55 female students of the preschool teacher education study programme. The adapted version of the Self-Assessment Scale of teacher competencies working in inclusive teaching (Skočić Mihić et al., 2014) was used for the self-assessment of competencies. The scale consists of ten items, and the agreement with the mentioned statements was assessed on a Likert-type scale. (1-5degrees). Six scale items refer to the cognitive dimension of competencies, while the remaining four refer to the emotional dimension of feeling competency. The results of the research indicate that students perceive both their cognitive and emotional competence to work in inclusive teaching in a physical education class as moderate. In addition, the results of Cronbach’s alpha (α = 0.76-0.88) determine both scales as reliable, and the obtained statistically significant correlation coefficients between the same variable of the first and second measurements (r = 0.57-0.88) define the good stability of the measuring instrument. From all above-mentioned factors, it was determined that the scale has satisfactory metric characteristics and is reliable for use in future research on the population of primary school teachers. The application of the scale for research purposes will provide insight into the current state of the level of competencies of future teachers for inclusive practice in physical education and provide guidelines for the best possible development of teacher education and kinesiology education programmes that will target all students, including students with disabilities


Discussion of the role of perception in visualization: From Cleveland to Heer, we understand how much and why it is important

Abstract: This paper starts with the literature review from Cleveland to Heer, and deeply discusses the role of perception in the visualization process, and the importance of understanding this role. The study points out that visualization is not only a process of conveying data, but also a process of interacting with the audience's perception. Effective visualization leads viewers to better understand and interpret the data, which also relies on a deep understanding of the perceptual process. By exploring the role of perception in the visualization, this paper highlights the importance of perception in the design process, and provides designers with suggestions on how to better utilize perceptual principles to improve the effectiveness and influence of data visualization.


Detection of Phishing Sites Using Machine Learning Techniques

Abstract: Phishing is a very commonly occurring attack in which the attacker attempt to get the private information of the user like card details, their passwords their transaction details etc. using fake copy websites. Attackers uses the websites very similar to the original websites and not possible for common people to identify. Phishing is the biggest loop-hole in the cyber world. Phishing became a successful business for phishers. Other than fake websites phishers uses different methods to do this job using messaging, spoofed links to make money and to counter Phishing various method are proposed some anti-phishing techniques are blacklist, whitelist but due to exponential growth on new innovations and technologies these techniques falls a little below as new websites contains dynamically work in it due to that these techniques cannot outperform todays websites.


A Study on the Effectiveness of the iTEST Paperless Testing System in English Listening Course

Abstract: In the intelligent teaching environment empowered by information technology, the methods and approaches for course examinations have become increasingly diverse, with a greater emphasis on assessing the learning process and linking it to final exams. Computer-assisted testing, as a new examination system, has gained attention and recognition from educators. It has reformed traditional teaching models to a certain extent, liberating teachers from repetitive and labor-intensive teaching activities while improving the overall teaching quality. Taking the application of the iTEST paperless testing system in English Listening course as an example, this study establishes a testing template and evaluates the role of the iTEST system in teaching. The research methods used in this study include quantitative and qualitative research methods, such as interviews and statistical analysis. By collecting feedback from students and teachers who have used the iTEST system, the study aims to understand the advantages and limitations of the iTEST system as perceived by teachers. Through statistical analysis, the study evaluates the examination results of the iTEST system and compares them with those of traditional paper-based tests. The results indicate that the iTEST paperless testing system can effectively enhance the teaching outcomes of English Listening courses, providing students and teachers with a more flexible, efficient, and accurate testing experience.


Mammogram Images Using Noise Removal of Filtering Techniques

Abstract: Historically, image denoising methods were proposed for reducing salt and pepper noise in mammography images through enhanced filtering techniques. The Mammogram Image Analysis Society (MIAS) benchmark database for DICOM visuals is used to detect benign and malignant breast cancer. Thus, mean and median filtering techniques, as well as spatial filtering methods, are used to implement medical image analysis with the modalities of low and high radiation for medical images. Similar to these experimental and suggested techniques for removing salt and pepper noise, these statistical techniques assess MSE, SNR, and PSNR data for enhanced quality of signal in the image.


Research on Experiences of Teachers and Children through Childcare Curriculum Consulting

Abstract: The purpose of this research is to explore changes and progress observed in both teachers and children during customized childcare curriculum consulting sessions developed based implementation of self-examination. The research was carried out among 6 teachers of childcare centers. This research was conducted in conjunction with the childcare curriculum consulting project of “A” Childcare Support Center as part of the 4th Standardized Childcare Curriculum implementation. The following were collected: in-depth qualitative interviews, journals kept by research participants, pictures of playtime, video clips, and researcher notes. The collected materials were transcribed and coded with coded responses grouped based on themes as needed. The results were the followings. First, teachers observed with care when children were playing and supported children to find purpose and lead their play time. Second, children became more independent during their playtime and proactively created their own plays. The childcare curriculum consulting supported teachers to implement goals of play-centered and child centered childcare curriculum through implementation of selfexamination.


The Flow of University Writing Courses and Reorganization Aspects of University Writing Textbooks: Focusing on Gyeongsang National University's writing textbooks

Abstract: The purpose of this article is to summarize the flow and textbook reorganization of Gyeongsang National University's writing curriculum and provide a reference point for the direction of future university writing textbooks by examining the characteristics of the recently published curriculum and the newly published textbooks. Put it. It was in 2009 that writing as a subject was designated as a required liberal arts subject at Gyeongsang National University. Since this period, the first common textbook for writing education was developed and published, textbooks have been reorganized and published every 4 to 5 years. Gyeongsang National University's writing curriculum and textbook composition are moving in a direction that takes into account the goals of liberal arts education and the needs and practical needs of learners. The direction of curriculum revision, which divides writing subjects into four categories and allows learners to selectively complete writing that they need or are interested in, along with the types of writing that are deemed fundamentally necessary for university education, can be understood in the same context. there is. In the case of textbook composition, it can be seen that the educational model and learning areas are subdivided according to the characteristics of the subject, and performance is emphasized through a process-centered approach.


Exploration of Faculty Community Experience to Consolidate the Digital Competence of Preservice Early Childhood Education Teachers

Abstract: This study explores faculty community experience to consolidate the digital competence of S University students in G City. The participants were six professors of the Department of Early Childhood Education, who formed a program to strengthen digital capabilities and conducted qualitative research on program learning and application processes to reinforce digital competence. As a result of learning process exploration of the professors for digital competence consolidation, they tried to get out of fear deriving from the lack of digital-friendliness, implemented digital devices handling methods, made creative ideas each other, and felt joy of learning. As a community with a goal of reinforcing digital competence, they demonstrated teamwork and built up knowledge. According to the professors’ onsite application exploration results to reinforce digital competence, they had child-like curiosities as they encountered digital education and tried to make good use of digital education’s positive function so that digital education can become practical knowledge in connection with early childhood education. They decided to apply digital education to the curriculum of each subject in order to enhance the preservice early childhood education teachers’ competence. Based on the study results, various communities and training for teachers are expected to be invigorated for consolidation of digital competence of the preservice early childhood education teachers.


Decentralized Energy Trading for Grids Using Blockchain for Sustainable Smart Cities

Abstract: Blockchain technology can be used to create a decentralized energy trading system for grids that rely on renewable energy sources. This system would allow individuals and organizations that generate renewable energy, such as solar or wind power, to sell excess energy to others on the grid through a peer-to-peer network. Blockchain has smart contracts that facilitate the transactions and ensure that the energy is exchanged securely and transparently. This type of system can increase the efficiency and flexibility of the grid, and help to improve the acquisition of sustainable energy resources. The system allows organizations and smart homes that generate sustainable energy to trade excess energy to others on the grid through a peer-to-peer network, facilitated by smart contracts. The use of blockchain in this manner can increase the efficiency and flexibility of grids and promote the adoption of renewable energy sources. Blockchain technology possesses the potential to transform the energy trading system by creating a decentralized, secure, and transparent P2P energy trading facility for grids that rely on renewable energy sources. This facility allows the trading of energy that is produced in excess by any entity through smart contracts. By using blockchain technology, the energy trading process is more efficient, secure, and transparent, and it can facilitate the adoption of sustainable energy sources. The implementation of such a system, however, requires collaboration between various stakeholders and compliance with existing regulations and standards of the energy sector. Despite the challenges, the potential benefits of a blockchain-based smart decentralized energy trading system make it a promising solution for the future of the energy sector.


A Comparative Study on Machine Learning Approaches for Sentiment Analysis

Abstract: Sentiment analysis plays a pivotal role in the operations of online product companies. User reviews are taken into account by others when they search for products, forming the cornerstone for delivering the right product based on user sentiments through sentiment analysis. Sentiment analysis involves the process of collecting, analyzing, and recommending reviews, which are often extensive and contain multiple paragraphs of content. This paper presents a comparative analysis of various machine learning models used to conduct sentiment analysis on customer reviews of Amazon products within the Electronics category. The initial models under scrutiny for our analysis include Logistic Regression, Decision Tree, Naive Bayes Classifier, Random Forest, Support Vector Machines, and BERT Model. The experimental result show that BERT classifier achieves higher accuracy when compare with other machine learning models.


Development of ISBAR-Based Anesthesia Unit Handover Instrument with Android Apps Against Completeness Pre and Post Anesthesia Documentation in the Installation Central Surgery

Abstract: The completeness of anesthesia records both electronically and handwritten is an important measure of information during perioperatives, as well as a legal document against anesthesia providers.The development of a handover instrument model with an android application with Kopelman model theory is needed in order to create an effective handover in the policy of supporting infrastructure and the implementation of anesthesia as an evaluation of the completeness of anesthesia documentation. This study aims to develop an ISBAR instrument with an android application as a guide for the handover of the anesthesia unit at the Central Surgical Installation of RSUD Haji Surabaya. Method: Research design using Research and Development (R&D). The independent variable is the development of ISBAR-based handover instruments, and the dependent variable taken is the completeness of pre- and post-anesthesia documentation. The population was taken as many as 34 nurse anesthetists and 100 pre and post anesthesia documentation, sampling using purposive sampling. The instruments used are questionnaires and observation sheetsResults: The trial of the anesthesia handover using the ISBAR instrument on the android application using the SUS questionnaire obtained 85 results meaning acceptable. Analysis: In stage 1 using descriptive analysis to determine the frequency of data, while in stage 2 using descriptive analysis presented in percentage to show the presentation of application test feasibility results. Conclusions: The development of ISBAR handover instruments with andorrid applications is suitable for use during operational services with Acceptability Ranges conditions that are acceptable. Grade Scale includes B. Adjective Rating: Worst Imaginable results are Excellent and Percentiles results are good.


Relationship between Major SatisfactionㆍStressㆍResilience of College Students

Abstract: The purpose of this study is to examine the relationship between college students' major satisfaction, stress, and resilience, and to determine whether college students' major satisfaction and stress affect their resilience. For this purpose, major satisfaction, stress, and resilience tests were conducted on 221 students at K University and the results were analyzed. Data analysis included descriptive statistics, frequency analysis, correlation analysis, multiple regression analysis, and reliability analysis using the SPSS program. As a result of the study, college students' major satisfaction and stress showed a negative correlation, and the negative correlation was especially high with academic stress and future problem stress. In terms of stress and resilience of college students, the correlation between positive resilience and stress was particularly high. College students' major satisfaction and resilience were found to be positively correlated, and resilience's interpersonal skills were found to be highly positively correlated with major satisfaction. Factors that affect the resilience of college students are both major satisfaction and stress. It was found that the higher the major satisfaction and the lower the stress, the higher the resilience. The relative influence of major satisfaction and stress showed that major satisfaction had a greater impact on resilience than stress. In future research, qualitative research such as FGI (Focus Group Interview) will be needed in addition to research to improve college students' stress management ability and resilience and quantitative research on major satisfaction, stress, and resilience targeting more diverse groups.


The meaning of picture book reading activities for infants and toddlers to pre-service early childhood teachers

Abstract: This study sought to examine the meaning that the experience of reading picture books in the infant class gave to pre-service early childhood teachers. The subjects of this study are two public daycare centers located in Nam-gu, G Metropolitan City, and classes for 1-2 year olds. The study participants were 10 first to third grade students attending the Department of Early Childhood Education at G University. Data analysis was conducted through the process of data integration, domain analysis, data classification, and interpretation of classified content based on the collected journals of pre-service early childhood teachers. As a result of the study, the meaning of the experience of pre-service early childhood teachers' picture book reading activities for infants was categorized into ‘the joy of novelty and challenge in the role of teacher’, ‘gift-like interaction time with infants’, ‘growth and development’, and ‘confidence through self-reflection’. In this way, activities linked to the early childhood education field provide prospective early childhood teachers with the ability to understand infants and recognize their value. In the process of preparing and executing the activity, infant development and developmental characteristics related to the activity are connected and analyzed after the activity. This can be an experience that supports the improvement of one's teaching abilities and allows one to develop into a better teacher. In this context, field-linked activities can be said to be an effective means that can have an effective and positive impact on the teacher training process and foster teachers with practical capabilities.


Learning Effects of Aviation Cabin Service Practice by Applying the PBL Teaching Model: For Aviation Service Departments

Abstract: This study applied the problem-based learning (PBL) teaching model to the aviation cabin service practical course offered in the Department of Aviation Service to train airline cabin crew and analyzed its learning effects. For this purpose, the PBL teaching model was applied to 61 students in the Department of Aviation Service in Jeollanam-do for a total of 12 weeks out of 15 weeks of class for 3 credits in the aviation cabin service practical course. The application of the PBL teaching model was conducted in a total of two modules based on the National Competency Standards (NCS). In the self-evaluation of participation in airline cabin service practical classes using the PBL teaching model, learners faithfully presented the learning outcomes of problem-solving activities. In the self-evaluation of the training results, the learners said that they learned a lot about communication problems during team activities and that the overall class activities were helpful in terms of training satisfaction. Through the application of the PBL teaching model, students faithfully presented the learning results of problem-solving activities, became aware of many communication problem situations during team activities, and found that overall class activities were helpful.


Factors Influencing the Improvement of Elementary Students' Basic Reading Skills: A Mixed-Methods Approach from the Teachers' Perspective

Abstract: This study analyzed factors influencing elementary students' basic reading skills enhancement from teachers' perspectives, focusing on the impact of digital environment changes and diverse learning methods. Employing a mixed-methods approach, the research unfolded in two phases: analysis of students' Korean language scores and a teacher survey on reading skill improvement factors, followed by teacher interviews based on survey outcomes. Teachers identified three main factors contributing to the improvement of students' basic reading skills: strengthening teamwork through regular learning, lesson planning, and mutual support among teachers; sharing students' incremental achievement assessment results within the grade to identify areas of growth; and setting continuous improvement of reading skills and literacy enhancement as a primary goal at the school level. These findings indicate that teachers prioritize team-based learning and collaboration. Enhancing basic reading skills requires strengthening teacher collaboration, sharing achievement insights to identify growth areas, and establishing literacy improvement as a school-wide goal, necessitating specific educational strategies and policies.


The Influence of Chinese Students' Motivation to Study Abroad on Educational Service Quality and Adaptation to University Life: The Moderated Mediating Effect of Korean Language Proficiency

Abstract: The purpose of this study was to investigate the mediating effect of educational service quality and the moderated mediating effect of Korean language level in the relationship between motivation to study abroad and adaptation to college life of Chinese students majoring in dance. Reliability analysis, descriptive statistical analysis, frequency analysis, correlation analysis, and structural model analysis were conducted using SPSS and Process Macro program. As a result of analyzing the mediating effect of education service quality in the relationship between motivation to study abroad and adaptation to college life, education service quality had a significant effect. It was confirmed that there was a moderating effect of Korean language level and a moderated mediating effect in the relationship between education service quality and college life adaptation.


A Qualitative Study on the Difficulties of Student Teachers in Health Education During Their Teaching Practicum

Abstract: The purpose of this study was to explore the difficulties and challenges student teachers of health education in the process of their teaching practicum for one month. The author interviewed nine student teachers of health education in which they practicum in the middle school and high school. The main results were as follows: First, the prospective health education teachers felt gaps between what they learned in the university and teaching practice in their practicum schools; Second, the prospective health education teachers felt limitations in their role as student teachers, both in conducting lessons of health education and providing student guidance; Third, the participants faced challenges in reconciling different educational philosophies between teachers at their practicum schools and fellow prospective health education teachers; Last, while conducting classes at the practicum schools, the participants were engaged in ongoing reflection and inquiry into the issues that teachers inevitably dealt with. The findings suggested several implications to improve school practicum for the professional development of pre-service teachers of health education.


The Role of Person-Major Fit and Autonomy Needs Satisfaction in Class on College Life Adaptation

Abstract: Students' perceived major fit plays an important role in adaptation to college. The present study examined whether person-major fit, which can become fixed when students start college, predicts college adaptation through its interaction with autonomy need satisfaction, which can be environmentally supported. Correlation and hierarchical regression analyses were conducted based on survey responses from college students (n = 251, 74% female) enrolled in a four-year university in Seoul and Gyeonggi Province. The correlation analysis showed that both person-major fit and autonomy need satisfaction were positively related to college life adaptation, especially to attachment to the university. Regression analysis was performed for each of the five domains of college adaptation, including academic adaptation, social adaptation, emotional adaptation, physical adaptation, and attachment to the university, with person-major fit and autonomy need satisfaction as predictors, along with their interaction term. The regression analyses revealed that person-major fit significantly predicted all aspects of college adaptation, except for physical adaptation. Autonomy need satisfaction significantly predicted all aspects of college adaptation, except for social adaptation. The interaction between person-major fit and autonomy need satisfaction was significant in the domains of emotional adaptation and physical adaptation. In other words, even when person-major fit was low, high autonomy need satisfaction in the class played a complementary role in enhancing emotional and physical adaptation. These research findings suggest the need for efforts to create an educational environment that provides opportunities for students with low person-major fit to choose learning methods and types of assignments in the class and to express their opinions freely to enhance their college adaptation.