International Journal of Basic and Applied Sciences
Title: International Journal of Basic and Applied Sciences E-ISSN: 2227-5053
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An Efficient Hybrid Model for Healthcare System to Detect‎Disease Using Machine Learning Techniques
Over the past decade, the huge demand for personalized and proactive healthcare has led to the ‎integration of advanced technologies such as wearable devices, IoT and AI into modern ‎healthcare. The project proposes the design of a smart health care system specifically for elderly ‎people and disabled persons, which continuously monitors their health and provides early disease ‎prediction. It proposes integrating a system of patient medical records with real-time ‎physiological data collected by multiple IoT-based wearable devices, while ensuring reliable and ‎timely assistance. IoT-based networks work on standardized communication protocols to ‎promote and manage data efficiency. In addition, it uses sophisticated machine learning ‎algorithms to evaluate collected data, identify potential health risks and support clinical decision-‎making with utmost accuracy and precision. It enhances the overall effectiveness and scalability ‎of healthcare service in addition to improving early treatment outcomes. Thus, by addressing ‎issues of heterogeneity, missing values, and interpretability, this proposed strategy advances the ‎creation of an inclusive healthcare system‎.
Healthcare; Machine Learning; Personalized Medicine; Predictive Analytics; Prognosis and ‎Disease Prediction
Copyright (c) 2025 International Journal of Basic and Applied Sciences
A Study on The Psychological Determinants of Substance Use among Indian ‎University Students Diagnosed with Borderline Personality Disorder
Background: Borderline personality disorder (BPD) frequently co-occurs with substance use, yet ‎the psychological mechanisms underlying this relationship remain insufficiently understood. This ‎study examines the association between BPD and substance use, with a focus on impulsivity, ‎emotional dysregulation, anxiety, and depression.‎ Method: A cross-sectional study was conducted between September 2023 and December 2024 ‎among undergraduate students from multiple institutions within a single university in Eastern ‎Odisha. Substance use was assessed using the Alcohol, Smoking, and Substance Involvement ‎Screening Test (ASSIST, Version 3). Psychological constructs were measured using the Difficulties ‎in Emotion Regulation Scale, Barratt Impulsiveness Scale, and Hamilton Depression and Anxiety ‎Rating Scales (HAM-D, HAM-A).‎ Results: Of the total participants, 5.8% (N = 115) met criteria for BPD, and 37.3% (N = 43) of ‎these reported substance use. Cannabis (81.4%) and tobacco (60.5%) were the most commonly used ‎substances among individuals with BPD, whereas alcohol, opioids, sedatives, inhalants, and other ‎drugs showed lower prevalence. Psychological symptoms were highly prevalent in the BPD group, ‎including impulsivity (62.6%), emotional dysregulation (67.9%), anxiety (53.1%), and depression ‎‎(34.8%). Substance use demonstrated strong associations with anxiety (χ² = 20.786, OR = 10.307, p ‎‎< 0.001), emotional dysregulation (χ² = 8.196, OR = 4.296, p = 0.004), and impulsivity (χ² = 12.624, ‎OR = 6.576, p < 0.001). In contrast, depression was not significantly associated with substance use ‎‎(p = 0.409). ‎ Conclusion: Adults with BPD exhibit significantly higher rates of substance use, particularly ‎cannabis and tobacco, compared to their non-BPD counterparts. Psychological factors, especially ‎impulsivity, emotional dysregulation, and anxiety, emerged as strong predictors of substance-use ‎vulnerability, whereas depression did not show a significant association‎.
Borderline Personality Disorder; Substance Use; Anxiety; Impulsivity; Emotional Dysregulation
A Hybrid K-Means, MST, and DFS Approach for Solving ‎The Capacitated Routing Problem
The Routing Problem (VRP) is a central challenge in logistics, where optimizing costs and adhering to operational constraints are critical ‎for efficient distribution networks. These challenges include minimizing distances traveled, reducing transportation costs, and limiting ‎computation times for real-time applications. This paper proposes a hybrid approach combining K-Means clustering, Minimum Spanning ‎Tree (MST), and Depth-First Search (DFS) to efficiently solve the Capacitated Routing Problem (CVRP). Using the Solomon datasets as a ‎benchmark, we evaluate the method’s performance. The results demonstrate a significant reduction in total distance traveled while ‎maintaining low computation times, making this approach competitive with traditional heuristics‎.
Topological Codescriptors for Predicting Lithium Clusters Binding Energy and ‎Comparative Topological Study of Hexagonal and Rectangular Porous Nano Graphene
The stability of lithium-ion batteries is heavily impacted by the uncontrolled ‎formation of lithium dendrites, which pose a significant safety risk. The chemical and ‎structural properties of small lithium clusters are critical to controlling this phenomenon ‎and improving electrode potential. The use of traditional techniques, including large-scale ‎experiments and DFT calculations, provides useful insights but is often hindered by high ‎costs and long timeframes. This study addresses this challenge by proposing a novel ‎approach using the CoM Polynomial, a new tool in chemical graph theory. We construct ‎molecular graphs for minimized lithium cluster structures from previous studies and utilize ‎the CoM Polynomial to generate codescriptors for each cluster. This approach extracts ‎significant structural information, enabling a more robust analysis of cluster properties. ‎Curvilinear regression analysis then identifies highly significant regression equations and ‎informative codescriptors, revealing strong relationships between these codescriptors and ‎the binding energy of the clusters. Furthermore, lithium metal holds tremendous promise as ‎an anode material for lithium-ion batteries but faces challenges like dendritic growth and ‎unstable solid electrolyte interphase formation. Researchers are exploring porous graphene ‎frameworks as a potential solution. In this vein, we investigated the molecular structure of ‎hexagonal and rectangular porous nano graphene using the CoM Polynomial to obtain ‎analytical expressions for their topological codescriptors. This study offers insights that ‎can help researchers improve the safety, efficiency, and sustainability of energy storage ‎devices by better understanding the properties of lithium clusters and porous nano ‎graphene.
CoM Polynomial; Lithium Clusters; Binding Energy; Hexagonal and Rectangular Porous Nano ‎Graphene Structures
Smart Solar Water Heating System: Design, Optimizationand ‎Cost-Effective Performance for Sustainable Domestic Use
This paper presents the design, production, and extensive testing of an economical and efficient solar water heating (SWH) system for ‎household applications, which integrates Internet of Things (IoT) technology for enhanced performance as well as user experience. The ‎system consists of two photovoltaic components: a flat-plate solar collector for thermal water heating and a PV solar panel integrated with a ‎water circulation pump for circulating water. Sensors are strategically placed on pipes, exterior surfaces, and storage tanks to collect real-time data. These sensors are linked to Arduino microcontrollers, which support remote monitoring and control via a mobile app. ANSYS ‎computational modeling was done for the optimization of thermal performance, and simulated results were very close to experimental findings, with a maximum error of 6.8%. The system achieved a constant-state temperature rise of water from 20 °C to approximately 55 °C in ‎five hours and an efficiency of 39.15%, greater than reported in most of the studies. Cost analysis indicates that the SWH system has 79.5% ‎lower cost than conventional electric water heaters with a payback period of ten years. The result confirms that local-level production of ‎solar water heating solutions is a viable, eco-friendly, economical, and sustainable alternative to conventional heating practices.
Sustainability; Solar Water Heating System; ANSYS; Simulation Results; Economic Viability
Blockchain-Driven Decentralized Green Energy Trading ‎using Python and Ganache
Efficient energy sharing among solar-based microgrids was crucial for enhancing grid reliability, scalability, and sustainability in ‎modern energy systems. This research presents a novel blockchain-powered decentralized energy trading framework that integrates ‎Raspberry Pi 4, IoT-driven real-time monitoring, and Ethereum-based smart contracts to facilitate seamless and secure peer-to-peer ‎‎(P2P) energy exchange. The proposed system enables real-time data acquisition and transmission of critical energy parameters, ‎including current, voltage, and power generation, from five interconnected solar microgrids. Raspberry Pi 4 serves as the centralized ‎edge computing node, aggregating and transmitting real-time energy data to the ThingSpeak IoT platform, where advanced AI-driven ‎analytics optimize grid efficiency. Blockchain technology, specifically Ethereum with Ganache, was employed to create a tamper-proof, ‎transparent, and trustless energy marketplace, eliminating reliance on centralized energy intermediaries. The incorporation of Solidity-based smart contracts automates transactions, ensuring secure, immutable, and fair energy trading while enabling dynamic pricing ‎models based on real-time demand-supply conditions. Python, integrated with Web3.py, facilitates seamless interaction between ‎Raspberry Pi 4 and the blockchain network, ensuring low-latency transaction execution and verifiable trade settlements. Through the ‎integration of IoT-enabled smart grids, blockchain-based energy transactions, and AI-driven predictive analytics, the proposed system ‎offers a scalable, autonomous, and energy-efficient solution for decentralized energy management. Experimental validation confirms the ‎system's effectiveness, demonstrating its ability to achieve real-time energy balancing, seamless P2P trading, and enhanced security ‎through blockchain immutability. This cutting-edge approach significantly advances the adoption of renewable energy sources, ‎optimizes microgrid autonomy, and reinforces the resilience of next-generation smart power networks, paving the way for a sustainable ‎and decentralized energy economy‎.
Peer-to-Peer; Energy Trading; Ethereum; Blockchain; Python
Enhancing Convective Heat Transfer with Bisectional Passive ‎Flow Divider Inserts
The high rate at which conventional energy sources are being exhausted has increased the world's attention on the effective use and ‎management of the available energy. Heat exchangers are very important parts of thermal systems, and they are used to transfer the heat ‎energy between two or more fluids. Their thermal performance should be enhanced to increase the overall energy efficiency. This can be ‎done by several heat transfer augmentation methods, which are broadly divided into active, passive, and hybrid methods. Passive techniques ‎are especially appealing among them because they are simple and do not require external energy.‎ The paper investigates the use of flow divider-type inserts as a passive method of improving heat transfer in a circular tube heat exchanger. ‎The inserts used were 90o, 60o, 45o and 30o twist angle inserts at a Reynolds number range of 7000 to 21000. The experimental results ‎showed that the Nusselt number increased with the Reynolds number, showing that convective heat transfer was improved.Nevertheless, ‎the Thermal Enhancement Factor and Friction Factor (f) exhibited a decreasing trend with the Reynolds number.‎ The insert with 45o twist angle was the most successful in overall performance of all the tested configurations. It produced the best Thermal ‎Enhancement Factor and Overall Performance Criteria (n) and was an effective compromise between the heat transfer enhancement and ‎the pressure drop. In particular, the 45o insert showed up to 1.99 times better Nusselt number (Nu), 2.21 times higher heat transfer ‎coefficient (h), 1.7 times higher thermal enhancement factor , and an Overall Performance Criteria (η) of 1.22 than a plain tube. Conversely, ‎the 30o twist angle, despite enhancing heat transfer, had a very high friction factor that resulted in increased pressure losses and low flow ‎efficiency
Heat Transfer Enhancement; Inserts; Flow Divider Type; Reynolds Number; Bisectional Inserts
Deep Learning-Based Lung Cancer Classification Using TNM‎Coding: Insights for Pharmacological Interventions
A multilevel deep learning approach known as TNM coding is commonly employed to classify lung cancer and predict disease stages. This ‎method integrates multiple deep learning networks to address complex challenges effectively. The objective of this study is to categorize CT ‎lung scans into three Tumor-Node-Metastasis (TNM) staging classes in accordance with the American Joint Committee on Cancer's (AJCC) ‎guidelines and to explore how accurate classification can support targeted pharmacological interventions. Initially, different lung diseases ‎, such as juxtapleural and internal nodules, are segmented automatically using an optimized conditional generative adversarial network (c-GAN). Following that, a combination of support vector machine classifiers and deep learning models is used to categorize tumors, nodes, ‎and metastases based on the AJCC staging criteria. This automated TNM cataloging method provides accurate cancer staging without ‎requiring manual detection of the region of interest (ROI) within CT scans. The proposed approach enhances the precision and cost-‎cost-effectiveness of CT scan analysis for lung cancer classification, thereby facilitating more tailored and effective pharmacological treatments‎.
Cancer Stage Prediction; CT Lung Scans; Lung Cancer Classification; Multilevel Deep Learning; Tumor Node Metastasis (TNM) Staging; ‎Pharmacological Interventions
Variant Anatomy of The Portal Vein According to 3D Modeling ‎Data
The study aimed to evaluate the variant anatomy of the portal vein based on computed tomography. Materials and methods. The material ‎was the results of 224 multispiral computed tomography of abdominal organs from the archive of clinics of Samara State Medical Universi-‎ty (Samara, Russia) for 2018-2019. Plug-ins were used in the Luch and Avtoplan programs to mathematically model and create 3D models ‎of the vascular bed. These programs make it possible to obtain 3D models based on multispiral computed tomography data. The portal vein ‎confluence variant and angle parameters formed by the trunk and roots of the portal vein were studied, considering the gender and age of the ‎patients. Results. The study found that type A confluence occurred in 45.5% of cases. Type B confluence was detected in 26.8%. Type C ‎confluence occurred in 16.5%. There were no statistically significant differences in the angles formed by the trunk and roots of the portal ‎vein in patients of different age groups and genders. Portal vein type A division was detected in 71.9% of observations, and portal vein type ‎B division was detected in 20.1%‎.
Portal Vein; Computed Tomography; Confluence, 3D; Abdominal Organs
Development of A Methodology and ‎Hardware-Software Complex for Multi-Level ‎Monitoring of High-Energy Particles
This article presents the development of a methodology and hardware-software complex for multilevel monitoring of high-energy particles ‎in the Earth's atmosphere. The study is aimed at eliminating the deficit of complex experimental data on the distribution of radiation ‎throughout the entire thickness of the atmosphere, obtained synchronously from ground stations, stratospheric probes and spacecraft. The ‎authors describe the creation of a stratospheric probe equipped with radiation detector, GPS module, atmospheric parameter sensors and ‎data transmission system. The software of the probe and ground station provides for the collection, processing and visualization of telemetry ‎in real time. The experiment included synchronous measurements from three platforms: ground station, stratospheric probe and spacecraft, ‎which made it possible to get a vertical radiation profile. The results showed a decrease in the radiation background in the surface layer and ‎its increase with height, confirming the hypothesis of the weakening effect of the atmosphere. The developed complex is proposed as a ‎flexible platform for further research in the field of space meteorology and radiation safety‎.
Cosmic Rays; High-Energy Particles; Radiation Monitoring; Stratospheric Probe; Vertical Radiation Profile
Development and Evaluation of Comprehensive Performance‎Framework for Industry 4.0 Implementation in Indian‎Automotive Manufacturing System
This research presents a robust, hypothesis-driven framework for evaluating Industry 4.0 ‎implementation in Indian automotive manufacturing. Building upon a comprehensive ‎readiness assessment of 55 automotive firms, the study integrates advanced analytics, ‎structural equation modeling (SEM), and digital twin technology to assess the ‎multidimensional impact of digital transformation initiatives. The research incorporates ‎empirical results from hypothesis testing across five primary domains: technological ‎capabilities, workforce skills, organizational culture, maturity model effectiveness, and ‎regulatory environment impacts. The proposed Performance Evaluation Index (PEI) ‎synthesizes 47 key performance indicators (KPIs) across five critical domains, demonstrating ‎that organizations implementing the integrated framework achieved 34% improvement in ‎overall equipment effectiveness (OEE), 28% reduction in production defects, and 22% ‎decrease in operational costs. The study validates the framework through advanced statistical ‎techniques including confirmatory factor analysis, partial least squares-structural equation ‎modeling (PLS-SEM), and time-series analysis, providing robust evidence for the factors ‎driving Industry 4.0 readiness and implementation success‎.
Industry 4.0; Performance Evaluation; Maturity Model; Automotive Manufacturing; Advanced ‎Analytics; SEM; Digital Twin; Indian Industry; Hypothesis Testing
Enhanced Diagnosis of Polycystic Ovarian Syndrome Using ‎Stacking Classifier with Random Forest As a Meta Learner
Polycystic Ovary Syndrome (PCOS) is considered a very serious health problem among the women of childbirth age. Early and accurate ‎detection is essential for effective management but remains challenging due to symptom variability and subjective traditional diagnostics. ‎Machine learning (ML) algorithms have recently emerged as promising tools for PCOS diagnosis by analyzing multidimensional clinical, ‎hormonal, and imaging data. This study evaluates and compares multiple supervised ML methods—including Gradient Boosting (XGBoost, ‎LightGBM, and CatBoost), AdaBoost, Logistic Regression, Naive Bayes, Random Forest, Decision Tree, Support Vector Machine (SVM), ‎and Stacking classifiers—on a publicly available dataset of 541 patients. Various evaluation measures like Receiver Operating Characteristic ‎curve (AUC-ROC), recall, accuracy, F1-score, and precision are used for the evaluation. Stacking classifier outperforms all single models ‎used in this research. The Stacking classifier with Random Forest as meta-learner achieved 98% accuracy with AUC of 98%. These find‎ings demonstrate that advanced ensemble ML models can robustly detect PCOS, with potential for integration into clinical workflows for ‎early, objective diagnosis. Future work should focus on multi-center validation and explainable AI to enhance clinical trust and deployment‎.
Polycystic Ovary Syndrome (PCOS); Machine Learning; Ensemble Methods; Feature Selection; Diagnostic Accuracy.
Fractional-Order Modeling and Neural Network Simulation of ‎NK Cell-Oncolytic Virus Interactions
Oncolytic virotherapy (OV) uses viruses to selectively destroy cancer cells, but the role of natural killer (NK) cells ‎in this process remains unclear. This study develops a discrete fractional-order model to examine NK cell activity ‎and identify activation conditions. Existence of the model solution is established via fixed-point theorems, and ‎the basic reproduction number (R_0) is derived using the next-generation matrix method with sensitivity analysis ‎to determine key parameters. Stability of equilibria is analyzed, and numerical simulations reveal how NK cell ‎activation affects interactions among cancer cells, infected cells, and viruses. Additionally, an artificial neural ‎network (ANN) trained with the Levenberg-Marquardt algorithm efficiently approximates the model's dynamics, ‎showing high accuracy (low MSE) and providing a rapid, reliable tool for predicting complex biological behaviors.
Oncolytic Virotherapy; Mathematical Modeling; Discrete Fractional-Order Model; Existence Theory; Stability Analysis.
Implementing Bidirectional Encoder Representations fromTransformers and Gated ‎Recurrent Units in a Chatbot for Student Support Services
Establishing efficient communication among students, faculty, and administrators is imperative ‎to the effective operation of educational institutions. Understanding students’ expectations for ‎fast, flexible, and personalized access to support services can improve satisfaction and strengthen ‎institutional performance. The increasing demand for prompt solutions in question–and–answer ‎services has led to the emergence of intelligent automation, particularly AI–powered chatbots, to ‎address existing educational challenges. This study developed and evaluated a web–based ‎student support system featuring a chatbot that integrates Bidirectional Encoder Representations ‎from Transformers (BERT) and Gated Recurrent Units (GRU) to enhance student support ‎services. The NLP–driven chatbot provides real–time, automated responses to student inquiries, ‎such as enrollment, schedules, tuition, and academic policies. Experimental results showed that ‎the BERT model effectively identified user intent, achieving precision, recall, and F1 scores of ‎‎0.87 and above. The GRU–based decoder generated coherent and contextually relevant replies, ‎validated through ROUGE metrics. Usability testing based on ISO 9241–11:2018 yielded mean ‎scores above 4.5, confirming the system’s effectiveness and user satisfaction. Overall, the ‎BERT–GRU chatbot demonstrates a practical and scalable solution for providing 24/7 student ‎assistance and improving institutional support. Recommendations were proposed to enhance the ‎implementation, effectiveness, and future development of BERT–GRU chatbot systems in ‎educational settings‎.
BERT; GRU; NLP; Seq2seq Architecture; Domain–Specific Chatbot; Student Support System; ‎Contextual Understanding.
Design and Development of Spiral Antenna at ISM Band for ‎WBAN Devices
The advancement in Body Area Network (BAN) was started in 1995 for the idea of combining Wireless Personal Area Network with Wire-‎less BAN which can be implemented to communicate near and around the human body. Initially, ISM (Industrial, Scientific, and Medical) ‎band has been allocated to industries, scientific research and medical field research, but later, it has been allowed to be used in applications ‎such as Bluetooth, Wi-Fi, Cordless phone, etc. For the above-mentioned applications, the antennas such as helical antenna, microstrip or ‎dielectric patch antenna, etc., have been used. Since microstrip patch antenna are compact in size and weight, they have become dominant in ‎this field. The design of this antenna needs 36 × 28 mm³ sheet of FR-4 substrate material. The detuning impact caused by the movement and ‎posture of the body can be tolerated by a wide bandwidth of 400 MHz. The electromagnetic radiation effects on free space are investigated. ‎Radiation gain, VSWR and return loss are also measured for analyzing the antenna design and making the antenna a better component for ‎wearable devices‎.
WBAN; ISM Band; Microstrip Patch Antenna; Gain; Wearable Devices‎.
Enhanced Black Ice Detection in Road EnvironmentsUsing ‎LiDAR and Angle-Thermal Modulation
This paper explores a LiDAR-based approach for identifying black ice on road surfaces by analyzing variations in distance measurements ‎under controlled changes in temperature and sensor orientation. Utilizing the compact and economical Lidar Lite v3, we observed how sub-‎tle differences in reflectivity and surface angle can distinguish black ice from asphalt. Despite the sensor's ±1 cm error margin, experimental ‎outcomes suggest it is feasible to detect black ice through this method. The proposed approach offers a potential solution for real-time road ‎hazard monitoring and sets the foundation for further enhancements aimed at improving robustness in dynamic environments‎.
Black Ice Detection; LiDAR Lite v3 Sensor; Surface Reflectivity Analysis.
Material Synergy and Efficiency Enhancement in Axial Flux ‎PMSM: Influence of Magnet, Core and Conductor ‎Combinations
This study investigates the material-dependent performance of an axial flux permanent magnet synchronous motor (AF-PMSM) through a ‎systematic evaluation of magnet, core, and conductor combinations. The analysis reveals that transitioning from ferrite to high-energy ‎NdFeB magnets significantly enhances efficiency, ranging from 74.58% to 85.91%, while concurrently increasing sensitivity to iron-core ‎losses. Employing low-loss core materials, particularly Hiperco50, markedly improves overall efficiency, underscoring the necessity of ‎advanced magnetic alloys in high-flux-density motor systems. Among the examined magnets, the advanced N55 grade NdFeB achieves the ‎highest efficiency (87.19%) and lowest loss, surpassing Alnico5 and SmCo24. Although SmCo24 offers superior thermal stability, its per-‎formance remains inferior to that of N55. Conductor analysis further reveals that silver windings yield the highest efficiency (88.03%) and ‎lowest total losses, slightly outperforming conventional copper at the expense of increased weight. Gold and aluminium conductors, by ‎contrast, exhibit reduced efficiency due to higher resistivity. The findings highlight the crucial interplay between magnet strength, core loss ‎characteristics, and conductor resistivity in determining overall AF-PMSM efficiency, providing valuable insight for the material optimiza-‎tion of traction motors in electric vehicle applications‎.
Axial Flux Permanent Magnet Synchronous Motor (AF-PMSM); Material Optimization; Motor Efficiency; Electric Vehicle Traction Drive‎.
Mathematics Teachers’ Competence and Instructional Quality ‎in Senior High Schools in The Province of Laguna
This study explored the relationship and effects of teachers’ competence to their instructional quality in public senior high schools in the ‎Philippines. Employing a descriptive-correlational design, the study surveyed 129 mathematics teachers from seven schools’ divisions of ‎Laguna Province. Stratified random sampling ensured equitable representation. Teachers’ instructional quality was assessed through cogni-‎tive activation, student support and classroom management with a reliability coefficient of 0.865. Meanwhile, teachers’ competence was ‎measured across five domains such as mathematics content knowledge (MCK), pedagogical content knowledge (MPCK), general pedagogi-‎cal knowledge (GPK), affective-motivation characteristics, and situation-specific skills with a reliability coefficient of 0.931. Multiple re-‎gression analysis was used to determine statistical correlations and its effect. Findings revealed very high instructional quality, while accom-‎plished levels were observed in teacher competence aspects. A strong positive correlation was found between overall teacher competence ‎and all aspects of instructional quality. Specifically, MCK positively influenced cognitive activation, while both MCK and situation-specific ‎skills impacted on student support. Classroom management was significantly affected only by situation-specific skills. These results under-‎scored the multifaceted nature of teacher competence in driving effective mathematics instruction, informing the development of a Mathemat-‎ics Teachers' Instructional Quality Framework. Future research with a larger sample is recommended to further explore these complex rela-‎tionships‎.
Mathematics Competence; Instructional Quality; Senior High School Teachers; Framework
Designing Scalable Multivariate Testing ‎Frameworks for High-Traffic E-Commerce ‎Platforms
Abstract: High-traffic e-commerce outlets use experimentation to perfect the user experience and, in ‎turn, conversions and revenue. Multivariate testing, however, acts as an elegant solution, ‎considering changes occurring in multiple variables and their interaction effects simultaneously, ‎thus generating richer insights than in the scenario of an A/B test. Nonetheless, the implementation ‎of a scalable multivariate testing framework on such large platforms harbors considerable ‎architectural challenges, data infrastructure issues, and, most importantly, statistical and operational ‎integration concerns. This paper covers complete principles, system design considerations, and ‎statistical methods that guide the construction of engineering-grade privacy-compliant ‎experimentation systems that support millions of concurrent users. By considering architecture ‎approaches, data pipelines, performance improvements, and integration with personalization, ‎inventory, and marketing systems, current best practices for performing experimentation as a ‎fundamental operational capability are offered. Topics discussed in case studies offer evidence-‎based stories on successes and pitfalls on the canvas, emerging trends such as reinforcement ‎learning and privacy-preserving analytics that will dictate the future of experimentation in e-‎commerce‎.
Multivariate Testing; E-commerce Optimization; Scalability; Experimentation Framework; ‎Statistical Analysis
Optimization of The Process of Identifying Sleep-Disordered‎Breathing Based on CNN and LSTM Recurrent NeuralNetworks ‎Using PSG EEG Signals
One of the challenges of Clinical polysomnographic (PSG) Datasets is the volume of information they contain, as they can consist of hun-‎dreds to thousands of PSG records, and each PSG record contains more than a dozen clinical time series about eight hours in length. Manu-‎al analysis of such datasets is a slow and laborious process, which is highly dependent on the experience and skill of the sleep technologist ‎and consequently limits PSG-based sleep-related studies. The main objective of this project is the automatic identification of arousals due to ‎respiratory events, specifically RERA (Respiratory Effort-Related Arousal) events and events associated with apnea/hypopnea using PSG ‎electroencephalography (EEG) signals. This paper implements technique to deal with data imbalance and improve systems performance in ‎identifying Respiratory events, design and implements different classification systems for identifying arousals related to respiratory events ‎using PSG EEG signals. Also analyze the performance obtained for each of the implemented classifiers compared to the models presented ‎in the literature. For this four systems were developed based on convolutional neural networks (CNN) and Long-Short Term Memory ‎‎(LSTM) recurrent neural networks. In this context, this research aims to contribute to the optimization of the process of identifying sleep-‎disordered breathing.
Respiratory Effort-Related Arousal; Polysomnographic; Electroencephalography; Long-Short Term Memory; Convolutional Neural Networks
Mechanistic and Applied Insights into religious Emotion:Mediation–‎Moderation Evidence Linking Religiosityto Optimism–Pessimism in ‎Taiwanese Undergraduates
Religious experience is inherently affective, yet the mechanisms by which religious belief relates to psychological ‎outlook remain under-specified. This study tests a three-factor model of emotions toward God as affective ‎mechanisms that mediate and/or moderate links between religiosity and dispositional outlook in Taiwanese ‎undergraduates. A sample of 551 students completed validated measures of multidimensional religiosity (CRSi-14), ‎emotions toward God (EtG; good, bad, moral), and dispositional optimism/pessimism (LOT-R). Confirmatory factor ‎analysis supported the three-factor EtG structure. Hayes PROCESS models examined mediation (Model 4) and ‎moderation (Model 1). Mediation analyses showed that good emotions significantly mediated the positive ‎association between religiosity and optimism (indirect β=0.32, 95% Confidence Interval; CI [0.13, 0.51]), whereas ‎bad emotions fully mediated the religiosity-pessimism link (indirect β=0.15, 95% CI [0.048, 0.254]); moral ‎emotions did not mediate either outcome. Moderation tests indicated that good emotions moderated the ‎religiosity-pessimism relationship (β interaction = -0.282, p=.019), such that religiosity related more strongly to ‎reduced pessimism when good emotions were lower; no significant moderation emerged for bad or moral emotions. ‎Findings advance basic affective theory by specifying valenced religious emotions as distinct mechanisms, and ‎they inform applied mental-health practice by identifying emotion-focused targets (amplifying “good” emotions, ‎addressing “bad” emotions) through which religiosity may shape youth outlook‎.
Religiosity; Emotions Toward God; Optimism; Pessimism; Affective Mechanisms; Mediation; Moderation; ‎Taiwanese Undergraduates
Extracting and Minimizing Relations for Enhanced Coverage of User ‎Stories
User stories are the first choice of practitioners for expressing requirements in agile projects. ‎Semi-structured natural language (NL) user stories, though easy to read and write, cannot ‎accurately represent a problem domain holistically. The conceptual model, constituting ‎relations (e.g., Teacher teaches Students) among key concepts in a domain, comes in handy to ‎serve the purpose. Several NLP-based approaches exist that extract these relations ‎automatically from user stories for conceptual modeling. These approaches do not make ‎optimum use of NLP capabilities, and consequently, inefficiency and incompleteness in the ‎extracted models are observable. To deal with these issues, we propose an approach for ‎relation extraction using the Open Information Extraction (OpenIE) NLP technique. The ‎OpenIE facilitates our process by automatically extracting relation triples (subject, relation, ‎object). The primary extraction results in a multitude of relations that we reduce into a ‎minimal set by applying a two-step reduction process. The minimal set of relations helps us ‎achieve efficiency as we plate up necessary minimum relations for further processing in ‎software development. The expanded coverage of the input user story by the minimal relation ‎set indicates a high degree of completeness, as our minimal set of relations represents most of ‎the domain knowledge hemmed in by the user story. We devise two metrics, Relation Set ‎Reduction (RSR) and Relation Set Coverage (RSC), to evaluate our approach. The evaluation ‎of two extensive user story datasets shows promising results as we are able to achieve a ‎‎65.91% reduction (RSR) in relations, with 86.45% coverage (RSC) of the user stories‎.
Conceptual Model; Relation Extraction; Relation Minimization; NLP; OpenIE
Mathematics Assessment Framework for Junior High Schools in ‎The Province of Batangas
This research evaluated the classroom assessment practices based on the National Council of Teachers of Mathematics (NCTM) stan-dards ‎as the basis for designing mathematics assessment framework for junior high school (JHS). Specifically, this study aimed to describe the ‎characteristics of the respondents; evaluated the mathematics assessment practices of teachers and determined the manifestation of the mathematics assessment purposes. It also identified the significant difference in the evaluation of mathematics assessment when grouped according to the respondent’s characteristics and the significant relationship between the evaluation of mathematics assessment and its purpose. ‎The exploratory method of research was utilized through questionnaires. The respondents of this study are the 289 JHS mathematics teachers in the province of Batangas which were selected using stratified proportional random sampling. The study revealed that most of them are ‎new in the teaching profession, have baccalaureate degree, have only 1 to 3 relevant trainings in the past 3 years and in their maturing stage ‎in using technology. Moreover, their assessment practices adhere to the NCTM standards. Additionally, there is a moderate extent of manifestation of the mathematics assessment purpose relative to evaluating mathematics curriculum, making instructional decisions, evaluating ‎student’s achievement, and monitoring student’s progress. Furthermore, significant differences in the evaluation of mathematics assessment ‎with reference to NCTM standards when grouped according to the respondents’ characteristics varies. Likewise, there are significant strong ‎positive relationships between the evaluation of mathematics assessment and its purpose. The designed framework aims to meet the purpose ‎of classroom assessment and develop mathematically literate students‎.
Assessment Framework; Assessment Practices; Junior High School; Mathematics Assessment; NCTM Standards
The Implementation of The Wolbachia Mosquito Program in ‎Dengue Fever Control Efforts in Indonesia
Dengue fever remains a significant public health threat in Indonesia, with periodic outbreaks contributing to morbidity and mortality across ‎the archipelago. Traditional vector control methods, such as insecticide use and environmental management, have shown limited long-term ‎effectiveness due to resistance development and inconsistent implementation. In response, Indonesia has adopted the Wolbachia Mosquito ‎Program as an innovative biological approach to suppress dengue transmission. This study employs a qualitative literature review methodology to examine the implementation and effectiveness of Wolbachia-infected Aedes aegypti mosquitoes in controlling dengue cases across ‎various Indonesian regions. Drawing on peer-reviewed journals, government reports, and project documentation, this review explores the scientific rationale for Wolbachia use, its field deployment strategies, community engagement processes, and preliminary health outcomes. Findings suggest that Wolbachia can significantly reduce the vectorial capacity of mosquitoes by limiting viral replication, offering a sustainable, non-chemical vector-control alternative. In areas such as Yogyakarta, early trials have demonstrated a substantial decline in dengue incidence following Wolbachia release. Furthermore, strong community involvement, transparent communication, and collaboration ‎with local health authorities were identified as critical success factors. Nevertheless, the review also highlights challenges, including scalability-‎ity, long-term monitoring, and public acceptance in diverse socio-cultural contexts. This study contributes to the growing body of evidence ‎supporting biologically based disease control methods and provides insights into the factors influencing their successful integration into ‎national public health strategies‎.
Dengue Fever; Indonesia; Qualitative Study; Vector Control