The Comparative Socio-Educational Effect of the Use of E-Learning Vs Face-To-Face Education as a Proposal within the Teaching-Learning Processes in Higher Education

Abstract: In the contextual framework of Information and Communication Technologies (ICT) in the educational field, this research was carried out on E-learning education and face-to-face education. Education is currently developed within a framework of innovation and transformation, so ICTs have been incorporated to promote access and quality of teaching-learning processes. Face-to-face education E-learning is a modality that supports flexible teaching and learning methods. Even so, at the same time, they present advantages and disadvantages that facilitate or hinder the tools for educational tasks. This research article’s main objective is to know the opinion of the Delfín 2019 Centro Universitario de la Costa program students about e-learning and face-to-face education to compare both modalities later. To develop this work, an instrument was created as a questionnaire from which the quantitative analysis and conclusions were obtained. An answer was given to each of the research questions, and the main question presented qualitative moments; due to the above, it was mixed.


Bitcoin halving and the integration of cryptocurrency and forex markets: An analysis of the higher-order moment spillovers

Abstract: This paper introduces a flexible semi-nonparametric model, the so-called SNP-VSK model, to analyze the higher-order moment interactions among cryptocurrencies and the forex markets, i.e., accounting for conditional volatility, skewness, and kurtosis spillovers. These transmissions among cryptos are identified considering Bitcoin halving as a factor of price uncertainty. To study the degree of integration of cryptocurrency markets in the economy, spillover effects with the forex market are considered, showing that Bitcoin and Ethereum are the prime receivers of positive volatility transmission, a sign of integration with the currency markets, but also potential sources of financial instability.


Influence of digital competencies of personnel on the development of social capital of organizations as a management resource

Abstract: In this article there is analyzed the current state of the digitalization process of Russian society, identified the conceptual characteristics of social capital as a management resource, and revealed its substantive characteristics and structural components. It has been established that social capital is an important resource for management activities, determined by the nature and content of social relations. Within the framework of the resource approach, a classification analysis of digital competencies of personnel is carried out, the characteristic features of their formation are considered, the directions of influence of digital competencies on the development of the social capital of organizations are determined, and a model of resource interaction of digital competencies of personnel within the social capital of the organization is substantiated.


Classification Analysis Of The Eligibility Of Recipients Of Non-Cash Food Assistance And Family Hope Programs In The City Of Sukabumi Using The Naïve Bayes Classifier Algorithm

Abstract: Providing social assistance is the government's effort to improve the welfare of the underprivileged. Non-Cash Food Assistance (BPNT) and the Family Hope Program (PKH) are two social assistance programs provided by the Indonesian government. BPNT is a food assistance program that is provided non-cash through electronic cards, while PKH is a cash social assistance program provided to poor families with certain criteria. Both programs aim to help the poor meet their food and education needs. To evaluate the effectiveness and efficiency of social assistance programs, a method is needed that can process and analyze data quickly and accurately. One method that can be used is the Naïve Bayes Classifier, which is a probabilistic classification method based on Bayes' theorem. This method can be used to classify data into certain categories based on its probability. In this study, researchers used the Naïve Bayes Classifier method to analyze social assistance data obtained from the BPNT and PKH programs. Data from the Sukabumi City Social Service was used to classify the eligibility of beneficiaries using the Naïve Bayes Classifier algorithm. Out of 5,183 data, 31.2% were classified as "Eligible" and 68.8% as "Ineligible". The algorithm showed 98.77% accuracy in eligibility classification. These results indicate the effectiveness of the Naïve Bayes Classifier algorithm in analyzing social data, providing new insights for better decision-making by relevant agencies in the development of more targeted and efficient social assistance policies


The influence of COVID-19 on the learning and developing processes of practical skills in military educational institutions

Abstract: During the COVID-19 pandemic, the educational process in military higher education institutions underwent changes, especially in terms of carrying out practical activities. The purpose of the research was to analyze how the COVID-19 pandemic influenced the educational process and the leadership activities carried out in a Romanian military academy, in the context in which the practical activities had to be carried out in the online environment. Practical skills play an important role in the life of military students and they can help them within the process of becoming efficient leaders. In the context of the research, practical skills refer to decision-making abilities, conflict resolution skills, communication skills, the ability to delegate, or the capacity to motivate and inspire others. In order to conduct the research, we used a quantitative approach. A questionnaire was applied to 288 students of “Nicolae Bălcescu” Land Forces Academy from Sibiu, Romania. The results of the research revealed that students pointed out the difficulty of interacting with their teachers, and of developing practical activities. While the opinions of the students were not influenced by their gender, they were influenced by the type of studies followed. Compared to bachelor students, master students were more affected by the changes which took place, and they believed to a higher extent that they possess leadership skills and that they have the necessary knowledge to lead a specific military group.


Vectorial Image Representation for Image Classification

Abstract: This paper proposes the transformation S→C→, where S is a digital gray-level image and C→ is a vector expressed through the textural space. The proposed transformation is denominated Vectorial Image Representation on the Texture Space (VIR-TS), given that the digital image S is represented by the textural vector C→. This vector C→ contains all of the local texture characteristics in the image of interest, and the texture unit T→ entertains a vectorial character, since it is defined through the resolution of a homogeneous equation system. For the application of this transformation, a new classifier for multiple classes is proposed in the texture space, where the vector C→ is employed as a characteristics vector. To verify its efficiency, it was experimentally deployed for the recognition of digital images of tree barks, obtaining an effective performance. In these experiments, the parametric value λ employed to solve the homogeneous equation system does not affect the results of the image classification. The VIR-TS transform possesses potential applications in specific tasks, such as locating missing persons, and the analysis and classification of diagnostic and medical images.


Evaluation of Synthetic-Temporal Imagery as an Environmental Covariate for Digital Soil Mapping: A Case Study in Soils under Tropical Pastures

Abstract: Digital soil maps are paramount for supporting environmental process analysis, planning for the conservation of ecosystems, and sustainable agriculture. The availability of dense time series of surface reflectance data provides valuable information for digital soil mapping (DSM). A detailed soil survey, along with a stack of Landsat 8 SR data and a rainfall time series, were analyzed to evaluate the influence of soil on the temporal patterns of vegetation greenness, assessed using the normalized difference vegetation index (NDVI). Based on these relationships, imagery depicting land surface phenology (LSP) metrics and other soil-forming factors proxies were evaluated as environmental covariates for DSM. The random forest algorithm was applied as a predictive model to relate soils and environmental covariates. The study focused on four soils typical of tropical conditions under pasture cover. Soil parent material and topography covariates were found to be similarly important to LSP metrics, especially those LSP images related to the seasonal availability of water to plants, registering significant contributions to the random forest model. Stronger effects of rainfall seasonality on LSP were observed for the Red Latosol (Ferralsol). The results of this study demonstrate that the addition of temporal variability of vegetation greenness can be used to assess soil subsurface processes and assist in DSM.


Deep Learning Techniques for Alzheimer's Disease Detection

Abstract: Alzheimer's disease (AD) is a common chronic disorder with a high incidence rate that disproportionately affects elderly people. Deep learning (DL) has been increasingly popular in recent years, resulting in notable developments and innovations in medical imaging. Consequently, deep learning has emerged as the preferred approach for analyzing medical visuals, particularly in the realm of Alzheimer's disease detection. In this chapter, the authors performed a comparative analysis of various DL models such as convolutional neural networks, DenseNet, ResNet, EfficientNet, etc., which have shown some groundbreaking results for AD disease detection. Also, they focused on investigating data collection and feature extraction techniques pertinent to AD. In addition, they further discussed briefly the deep learning models for AD detection. This not only increases hope for the advancement of AD research and therapy but also highlights how deep learning can revolutionize the field of medical image analysis and illness identification.


KM3NeT real-time analysis framework

Abstract: KM3NeT is a deep-sea neutrino observatory under construction at two sites in the Mediterranean Sea. The ARCA telescope (Italy), aims at identifying and studying TeV-PeV astrophysical neutrino sources, while the ORCA telescope (France), aims at studying the intrinsic properties of neutrinos in the few-GeV range. Since they are optimised in complementary energy ranges, both telescopes can be used to do neutrino astronomy from a few MeV to a few PeV, despite of their different primary goals. The KM3NeT observatory takes active part to the real-time multi-messenger searches, which allow to study transient phenomena by combining information from the simultaneous observation of complementary cosmic messengers with different observatories. In this respect, a key component is the real-time distribution of alerts when potentially interesting detections occur, in order to increase the discovery potential of transient sources and refine the localization of poorly localized triggers, such as gravitational waves. The KM3NeT real-time analysis framework is currently reconstructing all ARCA and ORCA events, searching for spatial and temporal coincidences with alerts received from other operating multi-messenger instruments and performing core-collapse supernova analyses. The selection of a sample of interesting events to send alerts to the external multi-messenger community is presently under definition. This contribution deals with the status of the KM3NeT real-time analysis framework and its first results.


Optimizing Multidimensional Pooling for Variational Quantum Algorithms

Abstract: Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role in extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data to 2-D image data to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.


MSEConv: A Unified Warping Framework for Video Frame Interpolation

Abstract: Within the context of video frame interpolation, complex motion modeling is the task of capturing, in a video sequence, where the moving objects are located in the interpolated frame, and how to maintain the temporal consistency of motion. Existing video frame interpolation methods typically assign either a fixed size of the motion kernel or a refined optical flow to model complex motions. However, they have the limitation of data redundancy and inaccuracy representation of motion. This paper introduces a unified warping framework, named multi-scale expandable deformable convolution (MSEConv), for simultaneously performing complex motion modeling and frame interpolation. In the proposed framework, a deep fully convolutional neural network with global attention is proposed to estimate multiple small-scale kernel weights with different expansion degrees and adaptive weight allocation for each pixel synthesis. Moreover, most of the kernel-based interpolation methods can be treated as the special case of the proposed MSEConv, thus, MSEConv can be easily transferred to other kernel-based frame interpolation methods for performance improvement. To further improve the robustness of motion occlusions, an operation of mask occlusion is introduced. As a consequence, our proposed MSEConv shows strong performance on par or even better than the state-of-the-art kernel-based frame interpolation works on public datasets. Our source code and visual comparable results are available at https://github.com/Pumpkin123709/MSEConv.


Parent - Young Adult Relationship with Self-esteem among Undergraduate Students

Abstract: Worldwide, there are over 1.8 billion young people living today and 90 % of who live in developing countries1. Parent–child relationships are central to psychological development and several studies have shown that suboptimal parenting is an important risk factor for psychological disorder in clinical and representative community samples.2 This study is an insight to know the Parent - Young Adult Relationship in the current generation of young adult and its impact on Self - esteem. Research results carried out on this topic suggest & promote Parents and Young Adult to have a better relationship. The aim of the study was to correlate relationship between Parent - Young Adult with Self-esteem among undergraduate students in selected college at Puducherry. Methods: A Quantitative Descriptive Research Design was used to assess the correlation between Parent - Young Adult Relationship with Self-esteem among undergraduate students in selected college at Puducherry. After taking a written consent, both male and female (117) undergraduate students (18 - 21 years) were selected using Simple Random Sampling Technique. The Parent - Young Adult Relationship among undergraduate students was assessed by Parent Adult Child Relationship Questionnaire (PACQ) and level of Self esteem was assessed by Rosenberg's Self - esteem Scale. The data was analyzed by using frequency, percentage, mean and Standard Deviation. Correlate the relationship between Parent - Young Adult Relationship and Self – esteem was analyzed by using Pearson’s correlation coefficient test. The association between the demographic variables between Parent - Young Adult Relationship and Self – esteem was analyzed by using Fisher’s exact test Result: The results show the correlation between Parent - Young Adult Relationship with Self-esteem among 117 undergraduate students. The mean self esteem score was 19.26 ± 3.791 and it ranged from 6 to 29. The level of self - esteem among nursing college students was normal, high and low, for 88.0%, 5.1% and 6.9%, respectively. There was a weak negative correlation observed between Parent - Young Adult relationship (Father) with Self - esteem in control aspect which implies greater the control by father lower the Self - esteem of the students. There was no statistically significant correlation was observed between Parent-Young Adult Relationship (Mother) with self -esteem at the significance level of p>0.05. Statistically significant association was found between Parent-Young Adult Relationship (Father - Regard) with gender, (Father -control) with Occupation, (Father - Responsibility) with feels more attached. Statistically significant association was noted between Parent-Young Adult Relationship (Mother - Responsibility) with year of studying implying B.Sc (N) - II Year and with area of residence, (Mother - Regard) with time spent every day (1-2 hours). Borderline statistically significant association was found in (Father - Control) with feels more attached & area of residence and (Mother - Responsibility) with Occupation. Conclusion: The findings of the study reflect that the Undergraduate relationship with both of their parents and majority Undergraduate students had normal self - esteem. The way parents raise their children influences the development of their self-esteem. Stronger relationship with parents boosts the self-esteem level in an individual. The study participants get benefited by participating in the study. Conducting this research in another society and with a greater sample size and in different setting may generate different results.


Avoiding barren plateaus in the variational determination of geometric entanglement

Abstract: Abstract The barren plateau phenomenon is one of the main obstacles to implementing variational quantum algorithms in the current generation of quantum processors. Here, we introduce a method capable of avoiding the barren plateau phenomenon in the variational determination of the geometric measure of entanglement for a large number of qubits. The method is based on measuring compatible two-qubit local functions whose optimization allows for achieving a well-suited initial condition, from which a global function can be further optimized without encountering a barren plateau. We analytically demonstrate that the local functions can be efficiently estimated and optimized. Numerical simulations up to 18-qubit GHZ and W states demonstrate that the method converges to the exact value. In particular, the method allows for escaping from barren plateaus induced by hardware noise or global functions defined on high-dimensional systems. Numerical simulations with noise are in agreement with experiments carried out on IBM’s quantum processors for 7 qubits.


Real-time continuous gesture recognition system based on PSO-PNN

Abstract: Abstract In the field of natural human–machine interaction, wearable gesture interaction technologies have received considerable attention, particularly continuous gesture (CG) recognition. However, CG faces several challenges, including the impact of motion characteristics on gesture recognition and performance that is not sufficiently robust. Traditional algorithms are highly dependent on samples, thus meeting the requirements of low sample volume and high accuracy simultaneously is challenging. To address these challenges, we propose a real-time CG recognition system based on particle swarm optimization (PSO) and probabilistic neural network (PNN). This system employs principal component analysis for signal dimensionality reduction to alleviate computational burden and uses K-means clustering and Pearson correlation coefficient to extract optimal features for gesture classification. In offline gesture recognition experiments involving six CGs, the algorithm achieved an accuracy rate of 97% with a training set of 300 samples and a runtime of just 31.25 ms. Compared to other five algorithms, the proposed algorithm improved accuracy by at least 9% and reduced the runtime by 40.475 ms. Moreover, gesture recognition experiments were conducted using different datasets, with the PSO-PNN algorithm achieving an average recognition rate of 90.17%, at least 9.84% higher than other algorithms. Finally, in experiments on online CG control for robots in complex environments, the PSO-PNN demonstrated real-time performance of 28.56 ms and a task completion rate of 90.67%, validating the feasibility of PSO-PNN. This research provides a substantial theoretical and technical foundation for the ongoing enhancement and application of CG recognition technology.


A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking

Abstract: Abstract Performance of modern trackers degrades substantially on transparent objects compared to opaque objects. This is largely due to two distinct reasons. Transparent objects are unique in that their appearance is directly affected by the background. Furthermore, transparent object scenes often contain many visually similar objects (distractors), which often lead to tracking failure. However, development of modern tracking architectures requires large training sets, which do not exist in transparent object tracking. We present two contributions addressing the aforementioned issues. We propose the first transparent object tracking training dataset Trans2k that consists of over 2k sequences with 104,343 images overall, annotated by bounding boxes and segmentation masks. Standard trackers trained on this dataset consistently improve by up to 16%. Our second contribution is a new distractor-aware transparent object tracker (DiTra) that treats localization accuracy and target identification as separate tasks and implements them by a novel architecture. DiTra sets a new state-of-the-art in transparent object tracking and generalizes well to opaque objects.


The development and evaluation of an educational board game on basic geotechnical soil characterization

Abstract: This paper discusses the potential of gamification as a tool for teaching and learning in geotechnical engineering. Gamification involves incorporating elements of gameplay such as challenges, rewards, competition, and cooperation into teaching and learning environments to make the process more interactive and engaging. Although gamification is widely used in many fields, it is still relatively new in geotechnical engineering. This paper presents the ‘Soil Character’ board game developed by the GeoFUN group as an example of successful gamification in geotechnical engineering education. The game focuses on basic soil characterization, including soil classification systems, index properties, and geotechnical characterization tests such as sieving, sedimentation, and Atterberg limits. The paper provides background information on the development of the game, and a description of the game components. The online Portuguese version of the game was tested with eight civil engineering undergraduate students who had successfully undertaken the introductory soil mechanics module. Student’s satisfaction in terms of game design, rules, and gameplay was measured via a questionnaire. Results of the questionnaires showed that the game was well evaluated in all aspects. Student volunteers reported that they felt very motivated, and that they wished they had been able to play the game when they were learning the topic. Thus, results presented in this paper suggest that gamification has the potential to make geotechnical engineering education more interactive and engaging. Exploring the effectiveness of the game in various contexts and with diverse student populations constitutes a key direction for our future research.


DefenseFea: An Input Transformation Feature Searching Algorithm Based Latent Space for Adversarial Defense

Abstract: Abstract Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool systems and further threaten their security. In this paper, we propose to use latent space protect image classification. Specifically, we train a feature searching network to make up the difference between adversarial examples and clean examples with label guided loss function. We name it DefenseFea(input transformation based defense with label guided loss function), experimental result shows that DefenseFea can improve the rate of adversarial examples that achieved a success rate of about 99% on a specific set of 5000 images from ILSVRC 2012. This study plays a positive role in the further investigation of the relationship between adversarial examples and clean examples.


Building Client Networks and Maximising Your Resources

Abstract: A report on how an Irish professional dog behavior consultant created a social media based network of past clients who were able to provide opportunities to learn and practice behavior modification skills without the direct supervision of the consultant. The structure of “The Alumni”’ is discussed alongside observations of the beneficial role such a network plays in a particularly challenging semi-rural Irish environment.


Improving Mass Gathering Management Using Process Mining Techniques

Abstract: Crowd management is a significant topic especially for countries that support gathering events frequently. The Kingdom of Saudi Arabia hosts and manages one of the world class annual religious gatherings known as “pilgrimage”. Several challenges are raised for managing and controlling such mass gathering event. In this paper we propose a comprehensive framework for event processes modelling and management. The framework consists of four main stages starts with acquiring temporal data and ends by modelling different processes of the event. The main contribution of this work is to demonstrate how process mining techniques can be used innovatively to model the movement flow of crowd. Synthetic data is used to show a proof-of-concept of the proposed framework and the applicability of using it in modelling and monitoring real crowd movement scenarios.


Abstract: The "Android Application for Online Fertilizer Selling and Accounting" aims to streamline the process of buying and selling fertilizers through a user-friendly mobile application. This comprehensive platform facilitates a secure and convenient marketplace for users, integrating essential features for both buyers and sellers. The application encompasses user authentication, a detailed product catalog, efficient shopping cart functionality, secure checkout and payment options, order tracking, and dedicated interfaces for sellers to manage their products and orders. For buyers, the application provides an intuitive interface to explore a diverse catalog of fertilizers, categorized for easy navigation. Users can securely authenticate, add products to their cart, and complete transactions using various payment methods. The order tracking feature ensures transparency and keeps users informed about the status of their purchases. The application also maintains an order history for reference. Sellers benefit from a dedicated management interface, allowing them to efficiently update and manage their fertilizer inventory. The platform ensures accurate accounting through the integration of invoicing features, generating detailed invoices for each transaction. This streamlines the selling process and helps sellers keep track of their sales and revenue. By combining the convenience of online shopping with robust accounting functionalities, the Android application for online fertilizer selling and accounting seeks to enhance the overall experience for users in the agricultural sector. The goal is to foster a reliable and efficient marketplace that meets the needs of both buyers and sellers in the fertilizer industry.


Wearable IoT System for Hand Function Assessment Based on EMG Signals

Abstract: Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of hand function, with a focus on a wearable wristband. The system, enhanced by cloud technology, offers comprehensive solutions for remote health management and therapeutic services. Firstly, it uses electromyography (EMG) signals from the arm to assess hand function. By employing sophisticated classification and regression models, this system can automatically identify user gestures and accurately measure grip strength. Additionally, the integration of additional sensor data ensures that the system fulfills essential criteria for hand function assessment. Leaving conventional grip strength classification methods, this study explored four distinct regression models to accurately represent the grip strength curve. The findings reveal that the Random Forest Regression (RFR) model is the most effective, achieving an R2 score of 0.9563 on the test data. This significant outcome not only confirms the practicality of the wearable wristband, which relies on EMG signals, but also underscores the potential of the IoT system in assessing hand function.


Reimagining STEM Education in South Africa: Leveraging Indigenous Knowledge Systems Through the M-Know Model for Curriculum Enhancement

Abstract: This conceptual paper investigates the integration of indigenous knowledge systems (IKS) into South African Science, Technology, Engineering, and Mathematics (STEM) education, exploring its conceptions, historical roots, significance, and the challenges and benefits associated with such integration. The objective was to understand the role of IKS in enriching STEM education and its potential contributions to global scientific knowledge. A culturally sensitive research approach was employed, with criteria set for selecting relevant literature on IKS and STEM. The results highlighted the profound historical significance of IKS in South Africa and its potential in bridging cultural gaps in education. Pedagogical innovations rooted in indigenous practices were identified as key to revitalising the STEM learning experience. However, challenges such as educational barriers, policy constraints, and institutional norms were also noted. Thus, the Multiple-ways of knowing (M-Know) model is proposed to demonstrate the possibility of using an integrated approach for the inclusion of IKS and enhancement of the STEM higher education school curriculum in South Africa. The model negotiates for a synergic or integrated arrangement between formal STEM curriculum and IKS in terms of using IKS to interrogate, complement, and consolidate the contents and pedagogy of Westernised STEM education in higher education.


Hate speech and environmental activist discourse

Abstract: On 14 October 2022, two activists from the environmental group Just Stop Oil threw a can of tomato soup at Van Gogh’s famous Sunflowers at the National Gallery in London, gluing themselves to the wall beneath the painting. The action, covered in a tweet by the organisation, received a backlash of negative comments by social media users. The present paper focuses on hate speech in response to the controversial tweet by Just Stop Oil in a dataset of about 2700 user comments. Moving from critical discourse studies of hate speech in digital contexts, the manual and software-assisted qualitative analysis employs the appraisal framework and discourse-historical strategies to observe the discursive construction of hate themes against environmental activism. In line with ecolinguistics, findings suggest that, when something valued as extremely positive and important such as art is under attack, people may fail to recognise the motivations behind activist action, appraising it negatively through hate speech, and even distancing themselves from environmental values.


Evaluating The Effectiveness of Information and Communication Technologies (Icts) in Fire Response in Disaster Management in Batam, Indonesia

Abstract: This research aims to evaluate the utilization of Information and Communication Technologies (ICTs) in the context of fire response for disaster management in Batam, Indonesia, spanning the years 2018 to 2023. Employing a mixed-methods approach involving surveys and case studies, the study investigates strategies and provides recommendations for health adaptation actions during climate-related disasters. The research assesses the effectiveness of ICTs in disaster risk management, with a specific focus on evaluating the role of the Disaster Management Act in Indonesia. The findings reveal that ICT-based early warning systems, enhanced communication networks, and efficient data management significantly contribute to improving Batam's preparedness and response to disasters. Despite these positive outcomes, the study identifies challenges, including issues related to equitable technology access, infrastructure limitations in remote areas, and privacy concerns that warrant attention. This study presents valuable quantitative insights into the effectiveness of ICTs in disaster management in Batam, Indonesia. It emphasizes the importance of addressing the identified challenges for a more robust disaster mitigation framework. The results have significant implications for policymakers and practitioners, underscoring the need for targeted interventions to overcome barriers and ensure the effective integration of ICTs in disaster response and management practices in Indonesia.


I ASKED CHATGPT WHAT THE NEXT TREND WOULD BE

Abstract: This critical review explores AI’s impact on trend research within fashion, scrutinising its capacity for ethical and nuanced analysis amidst industry reliance on its speed and perceived neutrality. Highlighting the risks of oversimplification and ethical oversight, it questions AI’s role in shaping a future that should reflect diverse cultural and professional integrity. It argues for a responsible AI utilisation that respects cultural nuances and social equities, urging for co-designing with diverse voices to ensure equitable and inclusive futures. Through a conversation with ChatGPT, this study underscores the need for a deeper understanding of AI’s application in creative processes, advocating for a thoughtful approach beyond mere replicating trends.


Classification Of Electronic Crimes According To The Nature Of The Right Assaulted (A Comparative Study Between The Palestinian And Emirati Legislations)

Abstract: This research investigates the issue of the different classifications of electronic crimes, and the importance of classifying them in the criminal law, with a focus on the specificity and the importance of the right assaulted in classifying them, as well as the importance of classifying them according to this criterion at all stages of the legal process to Combatting phenomenon of electronic crime. In this study we also investigates, the approach of Palestinian and Emirati legislators in classifying and regulating the provisions electronic crimes and Communications Crimes & information technology, pursuant to Decree-Law No. 10 of 2018, and Federal Law Decree-Law No. 34 of 2021 on combating phenomenon of electronic crime. At the end of the study, the researcher reached a set of conclusions and recommendations, One of the most important of conclusions is that The classification of electronic crimes based on the criterion of the right assaulted constitutes a vital issue in confronting these crimes, just as the Palestinian legislator did not follow a specific approach in his classification of electronic crimes and their substantive and procedural provisions, as the UAE legislator did in organizing and classifying crimes according to a clear approach. One of the most important recommendations is the necessity of the legislator the need for the Palestinian legislator to make amendments to the electronic crimes Law, classify these crimes, and separate the substantive and procedural provisions within this law, as well as amend the texts of Articles (21, 28, 48) of this law.


Arsenic geochemical species modeling, prediction, and bioavailability in groundwaters of the Oban Massif, southeastern Nigeria

Abstract: To ascertain the distribution patterns, source identification, chemical speciation, and bioavailability of arsenic in basement aquifers, 64 groundwater samples were analyzed using hydrochemical and geochemical modeling. It was determined that important ion geochemistry, hydrogeochemical facies, and basic hydrogeochemical parameters are interdependent. Arsenic (As) concentrations varied from 0.001 mg/l to 0.03 mg/l, with a mean of 0.007 mg/l. The results of 4.68% of samples were higher than the allowable level of 0.01 mg/l, i.e., groundwater arsenic levels are not dangerous. Significant ion concentrations decreased from dry to rainy seasons suggesting that ionic concentrations generated by silicate weathering in aquifers became diluted. Abundance trends in metal concentration during the dry and rainy seasons were Fe > Mn > Zn > Ni > Cu > As > Pb > Cd and Fe > Zn > Mn > Ni > Pb > Cu > As > Cd, respectively. Of the two hydrochemical facies discovered, the predominant one was the main alkaline-earth-bicarbonate facies [Ca-(Mg)-HCO3] typical of basement terrains, suggesting the fundamental stage of groundwater evolution. Plots of ionic ratios, metal loads, and principal component analyses showed that ionic concentrations are controlled by geology. Geochemical modeling revealed the presence of aqueous arsenic species, which are the less dangerous arsenates at unsaturated levels and do not currently pose a concern.


Design of an Efficient & Secure Steganographic Model using Modified LSB & Encryption Process

Abstract: This paper introduces a novel steganographic model for robust multimodal data security, seamlessly integrating a modified Least Significant Bit (LSB) technique with encryption, making it applicable to diverse data types such as images, audio, video, and text. Overcoming challenges posed by existing complex models and communication delays, our approach employs a modified LSB technique to encode similar sized data samples, followed by dynamic bioinspired elliptic curve cryptography (BECC) utilizing a Mayfly Optimization (MO) Model. This adaptive strategy optimizes curve types and prime key sets, significantly enhancing data security while minimizing delays and complexities across diverse data sizes. The proposed model achieves an 8.3% reduction in encryption and steganographic process delays, while simultaneously maintaining superior Peak Signal to Noise Ratio (PSNR) and lower Mean Squared Error (MSE) levels compared to existing methods when applied to the same data samples. This highlights its effectiveness in securing dynamic datasets without compromising efficiency.


Analysis and Comparison of 3D Printed Soft Pneumatic Actuator Configurations for Swaying Lateral Motion of Planar Objects

Abstract: Soft pneumatic actuators (SPAs) offer unique possibilities in robotics and automation due to their compliance and versatility. Unlike conventional actuators that can move either revolute or prismatic, SPAs can produce complex motions, especially when configured in combinations. This paper systematically investigated the performance of four distinct SPA configurations (Types 1-4) concerning the task of moving a planar square object and swaying it laterally to the right and left. These configurations are analyzed for their ability to achieve desired movements and orientations. Type 4, involving two SPAs, emerges as the most effective configuration, successfully navigating the challenges posed by the desired task. In contrast, Type 2, consisting of four SPAs, encounters difficulties in executing the task attributed to increased inertia, which was similar as type 3. Types 1, while not precisely meeting the primary task, reveal distinctive movements - left-right motion without orientation change. This study contributes valuable insights into the complex dynamics of SPAs, offering considerations for optimal design and performance. The outcomes could be developed in the future in soft actuator applications, especially those involving configurations based on specific needs.


Analisis kemampuan literasi numerasi mahasiswa pada implementasi program kampus mengajar

Abstract: The aim of this research is obtain a profile of the numeracy literacy abilities of students who have participated in the Teaching Campus program through analysis of the problem solving. This type of research is a case study. The subjects of this research were alumni of the Class 3 Teaching Campus at Widya Dharma University. Research data collection uses test methods and interview methods. The research instruments are the main instrument (researcher) and auxiliary instrument (numeracy literacy test). This research uses triangulation techniques to obtain valid data. The results of this research are (1) in text type questions students mention important information, do not formulate the problem, do not write down strategies, solve the problem correctly, check again without alternative solutions; (2) on text and image type questions, students mention important information and formulate problems, convey strategies, implement strategies with wrong image patterns, recheck answers; and (3) on text and table type questions, students mention important information and formulate the problem briefly, do not convey strategies, solve problems by paying attention to tables, and are less careful in analyzing text and table.


Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets

Abstract: Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%.


Enhancing Fixation and Pursuit: Optimizing Field of View and Number of Targets for Selection Performance in Virtual Reality

Abstract: Gaze interaction in virtual reality (VR) offers promising advantages in speed and hands-free operation. However, selecting a suitable selection mechanism in VR interfaces with different visual encodings remains a challenge. This research compares two visual input techniques and explores specific visual elements, including field of view (FOV), design style, and number of targets, with the goal of alleviating these problems. The pilot study identifies two search stages, and it also demonstrates a significant effect of FOV on search time. In the formal experiment, a gaze-based trigger experiment is employed to examine the selection performance of fixation and smooth pursuit. The results reveal that optimizing FOV and number of targets improves the triggering efficiency. Furthermore, fixation outperforms pursuit in terms of triggering time and accuracy under the same conditions, while cognitive load during triggering process remains relatively similar. These findings are expected to propose design recommendations to enhance fixation and pursuit within VR interfaces.


Comparative Analysis of Sustainable Banking in Public and Private Sector Banks in Sri Lanka

Abstract:
Sustainable banking is still an emerging concept in the world. While different World Bank organizations interpret sustainable banking differently using different dimensions, it will contribute for the sustainability and longevity of the earth by reducing the indirect negative impacts of the banking sector generated through customer activities. In Sri Lanka, attention on sustainable banking is still emerging. Thus, the objectives of this study are to analyze the strategies used in sustainable banking worldwide, to compare sustainable practices of selected public and private sector banks in Sri Lanka, to determine the advantages of sustainable banking practices to Sri Lankan banks and customers, to determine the customer perception on sustainable banking in Sri Lanka and to provide recommendations for future development of sustainable banking concept in the country. This study was commenced from June 2021 until May 2022. World-recognized standards related to sustainable banking were reviewed and a comparative analysis in the public and private banks operating in Sri Lanka was carried out using the primary data collected by interviews with sustainability officers of banks and secondary data collected from the annual reports of banks. Questionnaire surveys were conducted among 302 retail customers and 82 corporate sector customers. Information was gathered on customer perception on sustainable banking and the benefits of sustainable banking on banks and customers through these studies. The results revealed that out of 2 public sector banks and 11 private sector banks in Sri Lanka, 9 private sector banks and 2 public sector banks showed a satisfactory level of implementation. Questionnaire surveys of customers have shown that there is low customer perception towards the concept. The awareness of the concept among retail customers and corporate customers on sustainable banking is 32.12% and 9.8% respectively. 92.1% of retail customers have responded that there are benefits from sustainable banking for customers, while 78% of corporate customers have responded that there are benefits for them through sustainable banking initiatives. As per the findings, banks could gain benefits such as a good corporate image, enhanced reputation, business growth, moving towards new eras of financing, cost reduction, managing risk using the thorough process in risk management and awards. As Sri Lanka is slowly implementing sustainable banking practices it is recommended to have a regulatory approach. Under this regulatory approach, the banks will have action plans within a specific time period to develop and implement sustainable banking practices more effectively. The requirement of a rating system/ assessing tool is highlighted in this study's recommendations and this would direct Sri Lankan banking sector for better future developments in sustainable banking.
 Keywords: Green banking, Environmental sustainability, Triple bottom line, Customer satisfaction, Commercial banks


Energy and Performance Centric Resource Allocation Framework in Virtual Machine Consolidation Using Reinforcement Learning Approach

Abstract: Abstract:: Virtual machines are used to reduce cloud platform application performance, management costs, and access irregularities. Virtual machines are frequently vulnerable to delays, overburdening workloads, and other obstacles while consolidating and migrating servers. To significantly disperse loads among virtual machines, dynamic consolidation techniques are implemented to control energy dissipation, monitor overloading, and address underloading problems. Background:: The process of consolidation involves more calculations and resources in order to transfer services between virtual machines, provided that Service Level Agreements are observed. Methods:: The suggested approach promotes the use of cutting-edge architecture to combine virtual machines, and, therefore, strike a balance between performance and energy requirements. The main design considerations for the suggested Dynamic Weightage algorithm, which includes the clustering approach in relation to reinforcement learning approaches, are overall resource needs and Performance to Power Ratio (PPR). A cluster of ideal virtual machines is created, and resources are distributed according to performance and energy requirements. Virtual machine resource requests are converted into a matching relationship factor, which represents the individual hosts while taking PPR into account. The overall workload associated with virtual machine consolidation is also provided by these estimations. It is noted that there is little energy trade-off and that performance is maintained at a nominal level across the cluster. The architecture is put into practice throughout offline platforms, which are dispersed ecosystems that allow for increased system performance and scaling. Results:: The CloudSim simulator is used to validate the system using datasets that are obtained from PlanetLab. According to the data, energy saving has produced yields of up to 47% and promising quality of service attributes. Conclusion:: The validation of the system is performed using the CloudSim simulator with datasets from PlanetLab. The results indicate significant energy conservation, up to 47%, along with promising quality of service parameters. The proposed architecture is compared with other state-of-the-art algorithms for distributed architectures and heterogeneous environments, showcasing its efficiency. The conclusion emphasizes the prioritization of VM consolidation and energy efficiency in the proposed architecture, which has been tested on a Proliant G7- based data center using a variety of hosts. Notably, the CloudSim Toolkit is highlighted as outperforming OpenStack-based techniques in simulation results.


Assessing a Customized, Interactive Online Information Literacy Tutorial: The Getting Started with Research Module

Abstract: Following the creation of online information literacy tutorial in response to the shift to online learning due to the COVID-19 pandemic, a team of librarians developed a multi-pronged approach to assessing the module to determine how well it met the stated learning objectives. This article describes the assessment of the Getting Started with Research information literacy module, a learning experience developed using RISE software and accompanied by a Google Forms research log for students to complete. The authors present results from both a rubric-driven and textual analysis study undertaken to assess student responses to the research logs that accompany the module, and discuss how the findings of the study will inform future practice.


THE IMPACT OF LIP FORM ON THE PERCEPTION OF THE IDEAL INCISAL DISPLAY AT REST

Abstract: The perception of an aesthetically pleasing smile involves a multitude of factors, among which the shape and form of the lips play a pivotal role. Understanding how lip morphology influences the perception of an ideal incisal display during rest is crucial in the realm of cosmetic dentistry and facial aesthetics. The basic aim of the study is to find the impact of lip form on the perception of the ideal incisal display at rest. A total of 214 participants were enrolled in this observational study, meeting the inclusion criteria of being 18 years or older and representing diverse racial backgrounds. Exclusion criteria comprised individuals with oral health conditions impacting lip posture, recent dental procedures influencing incisal display, and cognitive impairments hindering compliance with instructions. Demographic information, including age, gender, and racial background, was recorded for each participant. Incisal display measurements were obtained and documented by trained evaluators following established procedures to minimize potential bias or variations in assessments. Thirty-six modified images simulating varied levels of maxillary incisal shown across three distinct lip forms—straight, moderate, and high—were created from the initial photographs of 214 volunteers. The mean incisal show for the linear lip form was hypothetically calculated as 1.5 mm (SD = 0.3), 2.3 mm (SD = 0.4) for the moderate lip form, and 3.0 mm (SD = 0.5) for the high lip form. Statistical comparison using ANOVA revealed significant differences in mean incisal display among lip form categories (F(2,105) = 80.72, p < 0.001). It is concluded that there is a strong association between lip form, cultural influences, and preferences for incisal display, highlighting the intricate role of individual perceptions in shaping dental aesthetic preferences. This inclination towards varied incisal displays appears to resonate across both dental professionals and laypersons, remaining consistent irrespective of gender.


Interpretable Software Defect Prediction from Project Effort and Static Code Metrics

Abstract: Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research also assesses the reliability of cross-project defect prediction. Well-known machine learning techniques, such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks, were applied to publicly available PROMISE datasets. The interpretability of this approach was demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.


Effectiveness of Lemongrass Warm Compress Intervention in Pediatric Meningoencephalitis Patients with Signs of Hyperthermia in the Picu Room Rsud Aji Muhammad Parikesit Tenggarong Hospital

Abstract: Meningoencephalitis is an inflammation that causes symptoms such as fever, severe headaches, convulsions, impaired consciousness and even death if not treated properly. One of the symptoms experienced by meningoencephalitis patients is an increase in body temperature. This body temperature can be treated with compresses. One compress that can be done is to use lemongrass or lemongrass (Cymbopogon citratus). Objective: To intervene with warm lemongrass compresses to reduce body temperature. Method: Quantitative descriptive with pre-test and post-test. The sample used was 1 child patient who was diagnosed with meningoencephalitis with signs of hyperthermia. Research Results: After the intervention of giving warm lemongrass compresses for 3 days, significant results were obtained, namely a decrease in body temperature to 36.1'C. Conclusion: Warm lemongrass compress is effective in reducing body temperature in meningoencephalitis patients.


The Role and Responsibilities of Academic Leaders in Effectuating Quality, Impact, and Excellence by Innovation in Higher Education

Abstract: Innovation is critical for higher education in the 21st century. It can improve teaching and learning, increase research quality and impact, solve social problems, and bring more funding and collaboration. It can also boost the happiness and creativity of staff and students, who can follow their interests, try new things, and work with different people. But innovation is not simple. It requires a positive and supportive culture, a clear and shared vision, a strong and effective leadership, and a regular and systematic evaluation. This seeks to review the existing research on innovation in higher education, analyze and discuss how academic leaders can encourage and enable innovation in higher education, and to further provide some helpful and evidence-based suggestions for academic leaders and practitioners who want to innovate in higher education.


Development of Physical Education Learning Media for Football Material forJunior High School Class VII Students on Smartphones Based on AndroidApplications

Abstract: This study aims to (1) produce a product development of Physical Education Sports And Health learning media for soccer material on android application-based smartphones for Junior High School students, (2) determine the feasibility of Physical Education Sports And Health learning media development products for soccer material on android application-based smartphones for Junior High School students, and (3) determine the level of practicality Physical Education Sports And Health learning development products for soccer material on android application-based smartphones for Junior High School students. This research is a development research with the ADDIE approach model, namely: analyse, design, develop, implement, and evaluate. The subjects of this study were a small-scale trial of 7 students in class VII SMP N 2 Sewon, a large-scale trial subject of 30 students in class VII SMP N 2 Bantul, expert validation including material experts and media experts. The results showed that Physical Education Sports And Health learning media product of soccer material on smartphones based on android applications had a very good / very feasible level of feasibility. The level of product feasibility based on material expert validation reached an average of 90% with a very feasible category, media expert validation reached 86.6% with a very feasible category, small-scale trials reached an average of 92.3% with a very good / very feasible category, large-scale trials reached an average of 90.2% with a very good / very feasible category.


Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange

Abstract: In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach's effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.


Our Journey to Encourage Makerspaces in Small Rural Libraries with the Help from the Library Innovation Studios Project

Abstract: Follow our journey to encourage permanent makerspaces in rural communities by providing 35 libraries a temporary makerspace. Paired with support and marketing tools, the communities garnered local support and an awareness of makerspace benefits. The Library Innovation Studios project proved valuable in broadening library services. Learn about the multi-faceted project – the makerspace kits, sustainability strategies employed, partner/library responsibilities, and the opportunity to make improvements as cycles progressed. We blended community engagement processes, trainings, user-friendly instructions, and supplied materials to promote immediate making. Other facets included local and statewide events and on-going networking tools – an e-mail group and a makerspace database.


VisFormers—Combining Vision and Transformers for Enhanced Complex Document Classification

Abstract: Complex documents have text, figures, tables, and other elements. The classification of scanned copies of different categories of complex documents like memos, newspapers, letters, and more is essential for rapid digitization. However, this task is very challenging as most scanned complex documents look similar. This is because all documents have similar colors of the page and letters, similar textures for all papers, and very few contrasting features. Several attempts have been made in the state of the art to classify complex documents; however, only a few of these works have addressed the classification of complex documents with similar features, and among these, the performances could be more satisfactory. To overcome this, this paper presents a method to use an optical character reader to extract the texts. It proposes a multi-headed model to combine vision-based transfer learning and natural-language-based Transformers within the same network for simultaneous training for different inputs and optimizers in specific parts of the network. A subset of the Ryers Vision Lab Complex Document Information Processing dataset containing 16 different document classes was used to evaluate the performances. The proposed multi-headed VisFormers network classified the documents with up to 94.2% accuracy, while a regular natural-language-processing-based Transformer network achieved 83%, and vision-based VGG19 transfer learning could achieve only up to 90% accuracy. The model deployment can help sort the scanned copies of various documents into different categories.


Predicting classroom activity index through multi-scale head posture classification network

Abstract: The quantification of the interplay between student behavior data and classroom teaching effectiveness using quantitative metrics has perennially posed a challenge in the evaluation of classroom instruction. Classroom activity serves as a reflection of student engagement, emotional ambiance, and other pertinent aspects during the pedagogical process. This article presents a methodology for quantifying student head posture during classroom instruction utilizing AI-driven video analysis technology, notably the Classroom Activity Index (CAI). A Classroom Activity Analysis System (CAAS) was designed and developed, integrating a multi-scale classification network based on ECA-ResNet50 and ECA-ResNet18. This network discerns and categorizes various head regions of students situated in both the frontal and real rows of a lecture-style classroom, irrespective of their dimensions. The classification network attains exceptional performance, boasting F1 score of 0.91 and 0.92 for student head-up and head-nodding. Drawing on the live classroom instruction at a higher vocational college in Wuhan, Hubei Province, China, a comparative experiment was executed. The findings revealed that three factors: teacher-student verbal interaction, teacher body language, and utilization of digital resource, all exert an influence on CAI. Simultaneously, the degree of classroom activity as gauged by FIAS and manual analysis fundamentally aligns with the CAI indicators quantified by CAAS, validating the efficacy of CAI in the quantification of classroom activity. Consequently, the incorporation of CAAS in teaching, research, and oversight scenarios can augment the precision and scientific rigor of classroom teaching assessment.


Research of news text classification method based on hierarchical semantics and prior correction

Abstract: News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms.


$R$-dependence of jet observables with JEWEL+v-USPhydro

Abstract: The $R$-dependence of jet observables provides a new tool in understanding the interplay between the jet energy-loss mechanism and medium response in heavy-ion collisions. This work applies the Monte Carlo events generator JEWEL and PYTHIA, coupled with $\rm T_{R}ENTo$ initial conditions and the state-of-the-art (2+1)D v-USPhydro, for the simulation of jet distributions and substructure observables for lead-lead collisions at LHC energy scales. We present the jet nuclear modification $R_{AA}$ and anisotropic flow coefficients $v_{n=2,3}$ varying the jet cone radius $R$, in the context of anti-$k_T$ jets, in addition to leading subjet fragmentation. The calculations indicate the impacts of the hydrodynamic evolution and weakly-coupled medium response, given by recoils, on the distributions. Results are compared to experimental data in a wide range of jet $p_T$ and collision centrality, and displayed along large jets ($R \ge 0.6$) predictions.


Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institution in England

Abstract: Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.


Effective Strategies and Methods to Enhance Music Practical Teaching in Higher Vocational Preschool Education

Abstract: Abstract:This paper studies of effective strategies and methods to elevate music practical teaching in higher vocational preschool education. Acknowledging the pivotal role of higher vocational preschools in shaping the foundational years of a child's development, the study navigates through a comprehensive exploration of the significance of music education. Grounded in a robust literature review, the paper highlights the importance of curriculum design, instructional techniques, assessment methods, and the seamless integration of technology in shaping impactful music education experiences.Theoretical frameworks, notably the Suzuki Method and Vygotsky's Sociocultural Theory, offer insights into pedagogical and socio-cultural aspects, emphasizing the creation of positive and immersive musical environments and collaborative learning experiences. Subsequently, the paper presents effective strategies, including the incorporation of technology, active learning approaches, and the customization of lessons to individual needs. The multifaceted benefits of music practical teaching in cognitive, emotional, and social domains are explored, emphasizing its role in holistic preschooler development. The study advocates for continuous professional development for music educators, stressing the importance of staying abreast of evolving pedagogical trends, engaging in collaboration and networking, and remaining informed about current trends and research in music education. By embracing these strategies, educators can cultivate a vibrant and enriching musical environment that fosters a lifelong love for music and nurtures the holistic development of preschool learners.


Adaptive teaching competence in university teacher education: theoretical framework, results and implications of interventional research

Abstract: Preparing future teachers for teaching heterogeneous learning groups can be seen as one of the fundamental challenges in teacher education. In this context, fostering adaptive teaching competence stands out as a promising approach for inclusive teaching and learning. To do so, the project ’Subject-Specific Didactic Qualification of Future Teachers for Inclusive Settings at the Humboldt-University Berlin’ (FDQI-HU) has developed digital teaching-learning modules for teacher education in a cyclical approach over several years. These modules can be adapted for individual subjects and used in a variety of ways. This article presents the theory and implementation of the modules for fostering adaptive teaching competence and – based on the empirical research on the interventions – presents central results as well as further impulses for innovative university teaching and teaching evaluation.


Natural Language Processing Advancements: Breaking Barriers in Human-Computer Interaction

Abstract: Natural Language Processing (NLP) advancements have revolutionized human-computer interaction, breaking barriers and opening new frontiers in technology. NLP techniques enable machines to understand, interpret, and generate human language, facilitating seamless communication between humans and computers. This paper explores recent advancements in NLP technology, highlighting their impact on various domains and discussing challenges and future directions in the field


Creating an atlas of normal tissue for pruning WSI patching through anomaly detection

Abstract: Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.


Challenges in the Adoption of Mobile Money Services by Mobile Phone Users in Lusaka, Zambia.

Abstract:
Purpose: This study investigates mobile money adoption in Lusaka, Zambia, utilizing a mixed-methods approach.
 Methodology: The study employs a mixed-methods approach to explore mobile money adoption in Lusaka, Zambia.
 Findings: The study uncovers several key findings regarding mobile money adoption in Lusaka, Zambia. Convenience, ease of use, and perceived security emerge as primary motivators for adoption. Conversely, lack of understanding, perceived risk, and a preference for traditional banking methods are identified as major deterrents. Interestingly, cultural factors, such as religious beliefs, play a minimal role in adoption decisions. Demographic factors, including age, education level, and income, exhibit a complex interplay with adoption rates, with younger, more educated, and higher-income individuals generally more inclined to use mobile money.
 Unique Contribution to Theory, Practice, and Policy: The study's findings contribute to both theory and practice in the field of mobile money adoption. They highlight the predominance of practical considerations over cultural factors in technology adoption, suggesting the need for enhanced adoption models that go beyond traditional socio-economic determinants. Additionally, the study identifies technological discomfort as a significant deterrent to adoption, indicating a need for improved digital literacy and user-friendly interfaces. Furthermore, an awareness-confidence paradox is revealed, wherein increased awareness of cyber scams negatively impacts trust in mobile money security. This suggests the importance of balancing risk education with strong security reassurances. Fee perception also emerges as a hurdle, with over 60% of respondents perceiving fees as unfair, underscoring the importance of educating users on the value proposition of mobile money services. Overall, the study offers valuable insights for strategic product and service development, regulatory and policy formulation, and initiatives aimed at advancing financial inclusion and promoting economic growth in similar urban and semi-rural settings.


Peningkatan literasi numerasi melalui model pembelajaran dan hubungannya dengan kemampuan self-efficacy: Systematic literatur review

Abstract: This study aims to describe the improvement of numeracy literacy through learning models and its correlation with self-efficacy capabilities.. The research method employed is a Systematic Literature Review (SLR) comprising three stages: planning, conducting, and reporting. Data collection involved identifying relevant articles based on predetermined criteria. The study examined 19 articles retrieved through a Google Scholar database search, facilitated by the Publish Or Perish software. Data synthesis was presented through tabulation and reinforced with a narrative format. The findings indicate that the improvement of numeracy literacy in elementary and secondary schools from 2020 to 2023 is predominantly pursued through the implementation of problem-based learning (PBL) models. Additionally, students' self-efficacy capabilities influence their success in learning. Furthermore, students' numeracy literacy correlates positively with self-efficacy, evidenced by higher self-efficacy correlating with higher numeracy literacy skills.


Interactive Tomi Tool To Improve Attitudes Towards Cyberbullying In Upper Basic Students

Abstract: Education changes frenetically at the pace of technological advances, likewise there are a series of difficulties that must be faced to avoid falling into cyberbullying that is converted into a way of discriminating against children and adolescents of any social class, the abuse of ICT exposes schoolchildren to this type of violation, where parents and teachers often do not know when a student is being a victim. This article aims to determine the effectiveness of the interactive TOMi tool in improving the attitudes of cyberbullying in students. of an Educational Unit, La Concordia - Ecuador 2023. The approach was quantitative, with a quasi-experimental design, using an intentional non-probabilistic sample of 30 students for the experimental group and 30 students for the control group. The instrument applied was the "European Cyberbullying Intervention Project Questionnaire", validated under the judgment of experts; reaching a coefficient of 0.75 in Cronbach's alpha, the data were displayed in frequency distribution tables and analyzed inferentially. It was concluded that the interactive TOMi tool positively influenced the improvement of attitudes towards cyberbullying in the students where the intervention was carried out.


Chatbot Service Quality in Banking : Analyzing Indian Banking Customer Perceptions and Influence on Customer Satisfaction and Value

Abstract: Purpose : The study has two objectives : first, to determine the quality of chatbot services provided by Indian banks; second, to assess the influence of chatbot service quality variables on customer satisfaction and customer value. Research Methodology : The study used a quantitative methodology, selecting individuals at random from a group of Indian banking clients. We used a questionnaire to collect data from the selected sample as part of a causal research investigation. We made use of SPSS and Python for this analysis. Customer satisfaction and value were taken into account as the dependent variables in our study. The seven elements of service quality—functionality, convenience, security, design, customization, enjoyment, and assurance—made up the independent variables. Findings : According to this study, client satisfaction and value were significantly shaped by the quality of the services provided. Customers’ value was significantly impacted by functionality and enjoyment, and their satisfaction was greatly influenced by assurance, design, and personalization. The unexpected negative impact assurance had on customer value is noteworthy and calls for more research. Practical Implications : In the highly competitive banking industry, this research has important ramifications for banks. It highlighted how important service quality is, which led banks to give priority to customer pleasure and think about making strategic changes. Banks could obtain a competitive advantage by improving the quality of their services, improving chatbot services, and implementing a customer-centric strategy by utilizing the research findings that have been presented. Our research helped banks evolve with the needs of their customers in mind, enabling them to gain credibility, repeat business, and long-term success in the ever-changing banking services market. Originality/Value : This study examined how consumers in Indian banks perceive the value and satisfaction of chatbot services and how they use them. The study provided useful recommendations and concepts to improve the general consumer experience.


Abstract: Irrigation and DrainageVolume 73, Issue 1 p. 378-380 ICID Restructuring the Knowledge Platforms of ICID Ashwin B. Pandya, Ashwin B. PandyaSearch for more papers by this author Ashwin B. Pandya, Ashwin B. PandyaSearch for more papers by this author First published: 15 February 2024 https://doi.org/10.1002/ird.2935Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume73, Issue1February 2024Pages 378-380 RelatedInformation


Abstract: Inside Precision MedicineVol. 11, No. 1 InterviewsCracking Down on Artificial Intelligence Malpractice in the EUHelen AlbertHelen AlbertHelen Albert is senior editor at Inside Precision Medicine and a freelance science journalist. Prior to going freelance, she was editor-in-chief at Labiotech, an English-language, digital publication based in Berlin focusing on the European biotech industry. Before moving to Germany, she worked at a range of different science and health-focused publications in London. She was editor of The Biochemist magazine and blog, but also worked as a senior reporter at Springer Nature's medwireNews for a number of years, as well as freelancing for various international publications. She has written for New Scientist, Chemistry World, Biodesigned, The BMJ, Forbes, Science Business, Cosmos magazine, and GEN. Helen has academic degrees in genetics and anthropology, and also spent some time early in her career working at the Sanger Institute in Cambridge before deciding to move into journalism.Search for more papers by this authorPublished Online:1 Feb 2024https://doi.org/10.1089/ipm.11.01.09AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 11Issue 1Feb 2024 InformationCopyright © GEN PublishingTo cite this article:Helen Albert.Cracking Down on Artificial Intelligence Malpractice in the EU.Inside Precision Medicine.Feb 2024.42-44.http://doi.org/10.1089/ipm.11.01.09Published in Volume: 11 Issue 1: February 1, 2024 TopicsArtificial intelligenceBiotechnology laws and regulatory affairs PDF download


Candidate Selection in Teams: Be the Best or Surround Yourself with the Best?

Abstract: ABSTRACT This study investigates, via an experiment, how the decentralization of a firm’s selection process affects the caliber of the chosen candidate in a team-based environment. We predict and find that, when decision makers have comprehensive and unambiguous candidate-specific information regarding who is the best for the job, the quality of the selected candidates is lower under a decentralized versus centralized selection process. We also find that nonpecuniary status concerns drive the effect. Results of two boundary conditions reveal that, as the clarity of information regarding who is the best candidate for the job decreases (due to decision-makers having weaker or mixed signals about job candidates), the quality of selected candidates becomes no worse under a decentralized than under a centralized selection process. Overall, our results indicate that nonpecuniary status considerations and information environment can influence candidate selection decisions in organizations. Data Availability: Data are available from the authors upon request.


Impact of Green Transformational Leadership on Employee Green Behavior : A PLS-SEM Approach

Abstract: Purpose : The purpose of this paper was to understand the impact of green transformational leadership (GTL) on employee green behavior (EGB) in the information technology (IT) sector. Methodology : For this research, the data were collected from IT employees of Pune, Maharashtra. The association between the latent variables, along with various characteristics, was observed. One hundred thirty employees from IT organizations were assessed through the survey method, and analysis was done using SPSS and the PLS-SEM approach. Findings : The empirical study showed a positive relationship between GTL and EGB. The results of the study also showcased that a green organizational identity (GOI) strongly mediated the relationship between GTL and EGB, and green training (GT) as a moderator strengthened the relationship. Practical Implications : Pro-environmental initiatives and policies cultivated an atmosphere of unity and collaboration in the workplace toward the environment. Originality/Value : This study added value to GTL. The findings would be helpful for policymakers.


Blockchain in the Lens of Populism : A Short Communication of Research Evidence

Abstract: Purpose : Blockchains are a sophisticated and extensively utilized technology that facilitates bulk data management operations and increases productivity for many companies, including governments. However, because of the high prices and complexity, not all large firms could take advantage of this disruptive weapon, which leads to populist ideas. The purpose of this brief message was to examine the new, popular ideas in blockchain technology that improve the global productive efficiencies of businesses. Methodology : The two-stage method that produced the insights in this communication was followed by a sentiment analysis, network map, theme map, and latent Dirichlet allocation. Findings : Blockchain technology has been widely adopted by the majority of the major services businesses. The industries that are currently scaling up, however, lack the viability to use this technology. The results also showed that neither academic research nor the corporate framework had examined the new populism situation. Practical Implications : The productive efficiency of industries would be improved if governments and policymakers used populistic settings in blockchain technology. Additionally, eliminating the cost-based inequalities would inspire populist ideas throughout all industries, generating combined revenues. Originality/Value : This brief message implied negative opinions about blockchain technology and was taken from a significant study on the impact of this technology on business success. This message is meant to benefit not only corporations but also governments and policymakers in facilitating regular access to technology for expanding industries that could strengthen economies.


Distributed asynchronous measurement system fusion estimation based on inverse covariance intersection algorithm

Abstract: For state estimation of multi-source asynchronous measurement systems with measurement missing phenomena, this paper proposes a distributed sequential inverse covariance intersection (DSICI) fusion algorithm based on conditional Kalman filtering method. It is mainly divided into synchronized state space module, local filtering module and fusion estimation module. The missing measurements occurring in the system are modelled and described by a set of random variables obeying a Bernoulli distribution. The synchronized state space module uses a state iteration method to synchronize the asynchronous measurement system at the moment of measurement update and it ensures the integrity of the measurement information. The local filtering module uses a conditional Kalman filtering algorithm for filter estimation. The reliability of the local filtering results is guaranteed because the local estimator designs a method to interact information with the domain sensors. The fusion estimation module designs a DSICI fusion algorithm with higher accuracy and satisfying consistency, which fuses the filtering results provided by each sensor when the relevant information between multiple sensors is unknown. Simulation examples demonstrate the excellent performance of the proposed algorithm, with a 33% improvement in accuracy over existing algorithms and an iteration time of less than 3 ms.


Efficient image restoration with style-guided context cluster and interaction

Abstract: Abstract Recently, convolutional neural networks (CNNs) and vision transformers (ViTs) have emerged as powerful tools for image restoration (IR). Nonetheless, they encountered some limitations due to their characteristics, such as CNNs sacrificing global reception and ViTs requiring large memory and graphics resources. To address these limitations and explore an alternative approach for improved IR performance, we propose two clustering-based frameworks for general IR tasks, which are style-guided context cluster U-Net (SCoC-UNet) and style-guided clustered point interaction U-Net (SCPI-UNet). The SCoC-UNet adopts a U-shaped architecture, comprising position embedding, Encoder, Decoder, and reconstruction block. Specifically, the input low-quality image is viewed as a set of unorganized points, each of which is first given location information by the continuous relative position embedding method. These points are then fed into a symmetric Encoder and Decoder which utilize style-guided context cluster (SCoC) blocks to extract potential context features and high-frequency information. Although SCoC-UNet has obtained decent performance for image restoration, its SCoC block can only capture connectivity at points within the same cluster, which may ignore long-range dependencies in different clusters. To address this issue, we further propose a SCPI-UNet based on SCoC-UNet, which leverages a style-guided clustered point interaction (SCPI) block in place of the SCoC block. The SCPI block utilizes a cross-attention mechanism to establish the connections of feature points between different clusters. Extensive experimental results demonstrate that the proposed SCoC-UNet and SCPI-UNet can handle several typical IR tasks (i.e., JPEG compression artifact reduction, image denoising, and super-resolution) and achieve superior quantitative and qualitative performance over some state-of-the-art methods.


Interactive Video Aided Interactional on Students’ Listening Skill

Abstract: This research aimed to investigate the effect of interactive video on students' listening skills of the seventh grade at SMPN 1 Gedung Surian and to know where the better media used interactive or Powerpoint media in teaching listening skills. The research employed a quasi-experimental design with a pre-test post-test control group. The sample of this research consisted of 60 seventh-grade students who were cluster random sampling selected and divided into two groups: experimental group and control group. The experimental group was taught using interactive video, while the control group was taught using conventional methods. Data were collected through a listening test before and after the treatment. The data were analyzed using independent samples t-test. The results showed that the experimental group's mean score (73.67) was significantly higher than the control group's mean score (47.23) with a p-value of 0.000. The findings indicated that the use of interactive video had a positive effect on students' listening skills, and as the result of the mean score of both classes the interactive video was more significant than PowerPoint media. Therefore, it is recommended that English teachers use interactive videos as a teaching method to improve students' listening skills.


PENGARUH PENGGUNAAN KOIL RACING TERHADAP PERFORMA MESIN 4 LANGKAH 125 CC

Abstract: The aims of this study are : (1) To determine the effect of using racing coil on the power generated of 4 step motorcycle 125 cc, (2) To determine the effect of using racing coil on the torque generated of 4 step motorcycle 125 cc. The research method used is experimental. The data of this research was obtained by taking measurements on sample of Honda Blade 125 FI 2014 motorcycle. The independent variables were standard ignition coil and racing ignition coil. The variables were maximum shaft power dan maximum shaft torque measured using a dynamometer. The data obtained were then analyzed using quantitative descriptive techniques by describing in the form of graphs and tables. The result of the study are : (1) The use of racing ignition coil has a higher effect on the power produced. (2) The use of racing ignition coil has a lower effect on the torque produced.


Incorporation of age into patient early warning scores significantly improves mortality prediction

Abstract: Abstract Background Age is a critical factor for the assessment of patients attended by emergency medical services (EMSs). However, how age modifies early warning scores’ (EWSs) predictive ability should be unveiled. Aim To determine how age influences the performance of EWS [National Early Warning Score 2 (NEWS2), VitalPAC-Early Warning Score (ViEWS), Rapid Acute Physiology Score (RAPS) and modified Rapid Emergency Medicine Score (mREMS)] to predict 2-day mortality. The secondary objective was to determine the performance of EWSs at different age ranges. Design A prospective, observational study performed between November 2019 and July 2023. Methods A multicenter, ambulance-based study, considering 38 basic life support units and six advanced life support units referring to four tertiary care hospitals. Eligible patients were adults recruited from among all phone requests for emergency assistance who were later evacuated to emergency departments. The primary outcome was 2-day in-hospital mortality (includes all-cause mortality). The main measures were demographical and vital signs needed for EWS calculation. Results and discussion A total of 8028 participants fulfilled the inclusion criteria, with 7654 survivors and 374 non-survivors. Among age ranges, the 2-day mortality was 2.8% for the ≤44 years, 3.3% for the 45–64 years, 4.1% for the 65–74 years and 6.7% for the ≥75-year age group. The inclusion of age significantly improved the Area Under the Curve (AUC) in all the scores (P = 0.006 for non-age-adjusted mREMS, P = 0.001 for NEWS2, P = 0.002 for ViEWS, P = 0.028 for RAPS, all compared with their counterparts with age). Conclusion Our results demonstrated that the incorporation of age into the EWS improved the performance of the scores. These results will allow the EMS to improve patient management and resource optimization by including an easy-to-obtain variable.


EcoSketch: promoting sustainable design through iterative environmental assessment during early-stage product development

Abstract: Sustainability has long been a topic of substantial interest the design and human-centered computing communities. With industries increasingly prioritizing climate targets, there is a growing demand for sustainable product design. This paper addresses this need through EcoSketch, a digital tool designed to democratize environmental impact assessments for product designers. Shifting typically retrospective evaluations to the early stages of product development, EcoSketch enables proactive consideration and adoption of sustainable alternatives. Unlike software tailored to environmental scientists, it minimizes the need for specialized training or extensive data inputs. We delve into the development and evaluation of EcoSketch, highlighting its unique features and usability strengths. The paper concludes by discussing design implications and proposing future research avenues to strengthen the intersection of human-computer interaction and sustainable product design, advancing progress on environmental challenges at the systems level.