About Emerging Science Journal (ESJ) Emerging Science Journal is a multidisciplinary, an open-access, internationally double-blind peer-reviewed journal which is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome. ESJ covers the following main subjects: Engineering and Technical Sciences Natural Sciences Social and Management Sciences Formal Sciences Physical Sciences For more information about ESJ Focus and Scope, you can click here. Current areas of interest, along with related associate editors, are listed below: Engineering and Technical Sciences Prof. Francesco Tornabene Department of Innovation Engineering, University of Salento Lecce, Italy Associate Editor Natural Sciences Prof. Nour Shafik Emam El-Gendy Egyptian Petroleum Research Institute (EPRI) Cairo, Egypt Associate Editor Social and Management Sciences Prof. Ferda Halicioglu University of Lincoln Lincoln, United Kingdom Associate Editor Formal Sciences Prof. Sunil Kumar National Institute of Technology Jamshedpur, India Associate Editor Physical Sciences Prof. Shen Xiu-Min Beijing Normal University Beijing, China Associate Editor Editorial Team View More International Emerging Science Journal Open Special Issues Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Aim and Scope Current issues in education involve differing views on standardized tests, socioeconomic factors, educational equity, and adaptive learning. It is important to understand that these issues are distinct from current trends in education, such as changes in the higher education system, the rise of online education, and the adoption of standards-based learning. To address and resolve these challenges, innovative ideas must be conceived and implemented. This Special Issue prioritizes studies that integrate insights from diverse disciplines, contextualize research findings within a broader framework, advance methodological and theoretical understanding, and possess the potential to influence education policy and practice. It welcomes quantitative, qualitative, and mixed-method research papers, as well as comprehensive reviews of significant research studies and technical reports previously published. Guest Editor: Dr. Stamatis Papadakis Affiliations: Department of Preschool Education, School of Education, University of Crete, Crete, Greece. Interests: Distance Learning; Mobile Learning; Learning Analytics; Game-based Learning; Preschool Education; Primary Education; Secondary Education; Educational Robotics. 0000-0003-3184-1147 Co-Guest Editor: Prof. Paula Escudeiro Affiliations: Instituto Superior de Engenharia do Porto, Porto, Portugal. Interests: Educational Technology; Learning Technology; Information Technology in Education; Serious Games. 0000-0003-2528-572X Manuscripts should be submitted online at www.ijournalse.org by registering and logging in to this website. For more information Click Here. This Issue is now open for submissions. Deadline for manuscript submissions: 31 December 2025. Closed Special Issues Special Issue "Internet of Things, Internet of Vehicles, and Blockchain"-2020-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2021 - Published Papers Special Issue "COVID-19: Emerging Research"-2022 - Published Papers Special Issue "Current Issues, Trends, and New Ideas in Education"-2022 - Published Papers Most Cited & Viewed Articles The most cited articles from Emerging Science Journal published since 2019, extracted from Scopus. Citation Article Big Data, Knowledge Mapping for Sustainable Development: A Water Quality Index Case Study by: Lei Zhang Effect of Salinity on Evaporation and the Water Cycle by: Jude Iloabuchi Obianyo Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications by: Allan Ramone Araujo Scharnberg, Adrison Carvalho de Loreto, Annelise Kopp Alves Revisiting Agroforestry for Building Climate Resilient Communities: A Case of Package-Based Integrated Agroforestry Practices in Nepal by: Kishor Aryal, Prakash Singh Thapa, Dhananjaya Lamichhane Impact of Transportation on Carbon Dioxide Emissions from Locally vs. Non-locally Sourced Food by: Bradley Striebig, Eric Smitts, Samuel Morton Preliminary Studies on the Use of Sawdust and Peanut Shell Powder as Adsorbents for Phosphorus Removal from Water by: Marian Asantewah Nkansah, Moses Donkoh, Osei Akoto, James Hawkins Ephraim
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The Use of Eye-Tracking Technology in Mathematics Education: A Mapping Study with Bibliometric Analysis
This study aims to identify trends, domains, and research topics of eye-tracking methodology in mathematics education published in Scopus-indexed journals. A systematic mapping study with bibliometric was employed to investigate the field. The analysis identified 119 eye-tracking studies in mathematics education published between 2013 and 2023, reflecting fluctuating publication trends. In this period, 333 authors, 78 sources, 156 organizations, and 38 countries contributed to the field. Schindler authored the most documents, while Germany and the United States recorded the highest output. The most cited work was by Cortina et al., and the International Journal of Science and Mathematics Education was the most frequently cited journal. Collaborations analysis identified Brockmole and Hannula as the most collaborative authors, and the University of Helsinki as the most active institution. Topic and domain analysis showed that the studies primarily focused on numbers and arithmetic, problem-solving, reasoning, individual differences, mathematical anxiety, creativity, mathematical representation, multimedia in learning, embodied cognition, mathematics learning, learning difficulties, geometry, and preschool mathematics. The findings suggest that several mathematical domains remain underexplored, offering opportunities for further eye-tracking research in mathematics education.
Artificial Intelligence in Recruitment: A Multivocal Review of Benefits, Challenges, and Strategies
This study investigates the role of artificial intelligence (AI) in recruitment, with a specific emphasis on small and medium enterprises (SMEs) and cultural diversity, two dimensions frequently underrepresented in existing research. The objective is to evaluate the benefits, challenges, and strategies for the responsible adoption of AI in recruitment. To achieve this, a Multivocal Literature Review (MLR) was conducted, systematically synthesising peer-reviewed studies and grey literature published from 2018 onwards. Following Kitchenham’s systematic review guidelines and Garousi’s multivocal extensions, academic and practitioner perspectives were analysed to capture both theoretical insights and real-world practices. The findings indicate that AI can streamline recruitment processes, improve decision-making accuracy, and enhance candidate experience through tools such as résumé screening, predictive analytics, and generative AI applications. However, issues of algorithmic bias, limited transparency, data quality, regulatory compliance, and workforce scepticism persist, particularly in SMEs that face resource constraints. Although much of the available evidence reflects Western contexts, this review broadens the scope by integrating global perspectives and highlighting how cultural and regional factors influence AI acceptance. The novelty of this study lies in combining academic and industry evidence to propose actionable strategies—such as bias audits, explainable AI frameworks, and human-in-the-loop approaches—for more inclusive, sustainable, and globally relevant adoption of AI in recruitment.
FedBHAD: Energy-Efficient Federated Learning for Black Hole Attack Detection in RPL-Based Low-Power IoT Networks
The internet of things is a network of connected devices that share and send information over the internet, frequently in resource-constrained situations. These are often built using the routing protocol for low-power and lossy networks (RPL), face significant security problems because of their limited computing power, and have energy constraints. The objective of this study is to design an efficient and lightweight mechanism for detecting black hole attacks on RPL-based internet of things networks. The proposed framework presents a distributed collaborative learning framework to reduce the processing load on central nodes while enhancing real-time threat detection. The novelty of the present work lies in integrating distributed learning with feature-based anomaly detection tailored for RPL environments, thereby improving IoT network security while reducing communication and energy overhead. A customized data retrieval algorithm is developed with the Cooja simulator’s configuration and extracts essential network parameters, including rank, expected transmission count, power consumption, forward count, and reception count. The analysis of this dataset allows the detection of black hole attacks. The research analysis indicates that the proposed framework achieves 99.6% detection accuracy, surpassing existing machine learning and deep learning techniques and offering enhanced security, reduced overhead, and lower computational needs.
YOLOv10-MsA: Attention-Augmented Real-Time Insulator Defect Detection from UAV Imagery
The reliable operation of transmission systems depends on early detection of defects within high-voltage insulators because it helps stop power outages. Scalable inspection remains a challenge since UAV-based imagery and deep learning generate recent promising solutions. The goal of this study is to build an accurate and time-efficient insulator defect detection system through the YOLOv10 architecture integration of Manhattan Self-Attention (MsA), which strengthens spatial feature detection and increases robustness during evaluations in complex aerial inspection scenarios. The designers implemented the MsA modules into the backbone and neck sections of YOLOv10 to develop YOLOv10-MsA as their novel detection model. The model relied on 5,000 annotated insulator images acquired by unmanned aerial vehicles throughout various defect classes during training and evaluation. Standard object detection metrics consisting of mAP@0.5, mAP@0.5:0.95, precision, recall, F1-score, and inference speed evaluated the performance of the model. The YOLOv10-MsA system reached an mAP@0.5 performance of 93.1% and an F1-score of 91.9% at an inference speed of 79 FPS, which surpasses YOLOv8, YOLOv9, and baseline YOLOv10. The model demonstrated its best performance at detecting various small and hard-to-detect defects such as chipping and contamination. The application of MsA in detection systems resulted in better accuracy while preserving real-time operation, according to related model assessment. The proposed YOLOv10-MsA serves as a powerful deployable system for UAV-based insulator inspection because it achieves both high accuracy and fast operation. The method establishes conditions for real-time smart infrastructure observation with attention-augmented frameworks for object detection.
DC Motor Angular Speed Controller Using an Embedded Microcontroller-Based PID Controller
This research presents the implementation of a Proportional Integral Derivative (PID) controller to control the angular speed of a Direct Current (DC) motor using an embedded system (microcontroller). The system’s hardware consists of an Arduino microcontroller, a DC motor with an encoder sensor, a driver motor, and a power supply. Proportional control regulates the response proportionally to the calculated error, while integral control manages the cumulative error over time, and derivative control responds to the rate of change of the error, preventing overshoot. With a proper combination, PID control achieves stability, speeds up response, and reduces overshoot, improving overall system performance. Based on experimental data, the DC motor angular speed control system using PID control achieves the best results, in which the parameter values are Kp=1; Ki=0.3; and Kd=0.6. The augmented system responded with 0.0890 seconds of the rise time, 11.772 seconds of settling time, and 0.12 seconds of the peak time, with an overshoot of less than 10% (7%).
Small Dual Polarized UWB Antenna and Its Array Analysis for 5G/6G Applications
In this work, a novel low-profile tunable ultra-wideband (UWB) K/Ka-band (14–40 GHz) dual circularly polarized magneto-electric antenna element has been designed, analyzed, and validated through circuit modeling, simulations, fabrication, and experimental testing for application in 5G/6G phased-array antennas. The antenna has compact dimensions of 0.5λ₀× 0.5λ₀ × 0.06λ₀/0.09λ₀, which can be further reduced to 0.25λ₀× 0.25λ₀ × 0.05λ₀ when metal–insulator–metal (MIM) and/or gap capacitors are employed. The proposed antenna exhibits a high gain of 9 dB, a wide scanning angle of ±75°, and an efficiency exceeding 85% across the entire operating frequency band. In addition, it demonstrates high isolation between ports and between co-polarized and cross-polarized radiation patterns, reaching 25 dB. The resonant frequency of the antenna is tunable, with a variation of up to 97% over the K/Ka-band frequency range. This tuning capability is achieved using MIM capacitors connected to the vias of the circular patch and/or gap capacitors, which collectively function as split-ring resonators (SRRs). Fabrication and experimental testing of the antenna confirm good agreement with the simulated results. The antenna is easily fabricated using glass substrates and standard epoxy/glass processes with only two layers, making it highly suitable for antenna-in-package applications based on glass technology. Since the antenna element is specifically designed for phased-array applications, array configurations were also investigated. Analysis of 512-element arrays shows that the Sunflower layout provides enhanced gain and overall performance while utilizing more than 50% fewer antenna elements compared to a conventional rectangular array.
A Novel Statistical Process Control Approach for PM2.5 Monitoring Using Time Series Modeling
This research seeks to create a novel control chart capable of managing autocorrelated time series data by proposing a modified Exponentially Weighted Moving Average (EWMA) approach tailored to processes following the ARMA(p,q) model, which also makes use of exponential white noise. The key methodological contribution involves an explicit formula to compute the Average Run Length (ARL), while the Numerical Integral Equation (NIE) approach is utilized for verification purposes. The proposed formula not only demonstrated 100% agreement with NIE results but also significantly reduced computational time, requiring less than 0.001 seconds per run, compared to the 3–4 seconds typically needed by NIE. To assess the performance, simulation experiments and real-world case studies on PM2.5 air pollution data from Nakhon Phanom, Nan, and Nonthaburi provinces in Thailand were conducted. Our modified control chart was better at identifying minimal changes than a standard EWMA chart, as shown by lower ARL1, SDRL1, AEQL, and optimal PCI values. The one-sided chart structure, designed to monitor upward shifts in pollutant levels, further supports its application in environmental surveillance. Overall, the study introduces a fast, accurate, and practical tool for quality control in autocorrelated environments, offering both analytical and computational advantages over existing methods.
Exploring Contextual and Behavioral Determinants of Environmental Auditing Adoption
This study's primary objective is to identify the determinants of environmental auditing (EA) adoption intention in environmentally sensitive industries. Its novelty lies in developing and testing an integrated model that extends the Theory of Planned Behavior (TPB) by incorporating four contextual factors: Coercive Pressure, Stakeholder Pressure, Internal Resources, and Corporate Culture. For its methodology, the research analyzed survey data from 336 senior managers using Structural Equation Modeling (SEM). The findings confirm that the three core TPB constructs - Attitude, Subjective Norms, and Perceived Behavioral Control - are all direct, positive, and significant predictors of EA intention, with Attitude emerging as the strongest. Crucially, the study finds that the four contextual factors only influence intention indirectly, as their effects are fully mediated by the TPB constructs; Corporate Culture and Internal Resources exhibited the greatest total indirect effects. This research provides a significant improvement over existing models by empirically demonstrating this dual-pathway mechanism, suggesting that efforts to promote EA in Vietnam must focus not only on external pressures but also on cultivating positive managerial attitudes and enhancing internal organizational capabilities.
Enhanced Optimization Strategy to Maximize Achievable Rate of Millimeter-Wave Full-Duplex UAV on Multiple User
This study proposes an enhanced optimization strategy to maximize the achievable data rate of millimeter-wave (mmWave) full-duplex (FD) unmanned aerial vehicles (UAVs) in multi-user scenarios. The objective is to address signal degradation from high-frequency path loss and self-interference while ensuring efficient resource allocation across multiple user equipment (UEs). A joint optimization framework is introduced, integrating UAV positioning, beamforming vector design at both the gateway and UAV, and power allocation. Initially, the Alternating Interference Suppression (AIS) algorithm is adapted for multiple UEs, but due to emerging non-convexity, the problem is reformulated using a first-order approximation approach. The solution is decomposed into two iterative sub-problems—optimizing UAV location and then solving for beamforming and power distribution. MATLAB-based simulations validate the proposed approach, revealing a threefold increase in achievable data rate and a 40.85% improvement in power efficiency compared to non-optimized systems. The novelty of this work lies in its scalable multi-user adaptation and its integrated, power-aware optimization algorithm, outperforming conventional FD and half-duplex strategies. This contribution significantly advances the design of efficient, high-throughput UAV communication systems for next-generation wireless networks, especially in environments with frequent line-of-sight obstructions.
Development of Control and Measurement Procedures for Geometrically Complex Surfaces
This study aims to develop and automate control and measurement procedures for parts with complex geometric surfaces under multiproduct manufacturing conditions. By integrating combinatorial analysis, statistical testing, and probe trajectory optimization into a unified framework, the proposed methodology formalizes measurement planning within an automated system. The actual dimensional characteristics of each workpiece are determined at the design stage, enabling the adaptation of the technological process to specific components. Experimental validation was performed on a FARO 9 ARM coordinate measuring machine using six types of complex parts, and statistical testing was performed to identify the optimal number of control points (108) with a minimum measurement time of 72 min per part. The methodology achieved a defect rate reduction of 5% and demonstrated an annual cost savings of 641,172 Rubles. This study integrates control point selection, probe trajectory planning, and measuring instrument choice into a single automated system that adapts to actual workpiece geometry, advancing Metrology 4.0 principles. The proposed approach significantly improves performance compared with conventional methods, reducing metrological preparation time by 76%, lowering defect rates by 50%, and decreasing the number of measurement operations by over 40%. These results confirm the potential of the methodology for enhancing productivity and economic efficiency in digital manufacturing environments.
Feature Transformation on Big Data for Species Classification in Machine Learning
Classification of bacterial species, particularly for closely related taxa, remains a major challenge in many areas, e.g., public health, food industries, and many others. The issues are mainly caused by overlapping genetic features of organisms and data complexities. In this study, a bacterial taxonomic identification framework that integrates genome-derived motif sequences with machine learning was introduced. Two hundred and forty genome sequences from Salmonella enterica, representing six subspecies and ten serovars, were used for modelling. Sequence motifs were predicted from single-copy orthologous core genes of the downloaded genomes. Single nucleotide polymorphisms (SNPs) within these motifs were extracted and numerically encoded as machine learning features. The 20 top-most informative predictors from feature selections were used for model training in Random Forest and Support Vector Machine. Comparing the output from multiple analyses, the Random Forest model achieved the highest accuracy of 97.92%, demonstrating reliable differentiation of Salmonella at both subspecies and serovar levels. This research presents two key innovations: i) the use of sequence motifs as molecular signatures for bacterial classification; ii) a novel feature engineering method that transforms genome-derived data into machine learning-readable features. The proposed framework offers a practical and scalable solution for fine-level bacterial classification and has high potential to be applied for other microbial taxa.
The Performance Impact of Management Control Systems: Assessing the Mediating Role of Organizational Culture
This research examines the dual-pathway impact of Management Control Systems (MCS) on Company Performance (CP) in China’s liquor industry, with a focus on the mediating role of Organizational Culture (OC). The research aims to address gaps in understanding how MCS enhances both financial and non-financial performance through cultural mechanisms, a critical yet underexplored dynamic in heritage-based industries. Employing a mixed-methods approach, the research analyzes survey data from 497 firms using Structural Equation Modeling (SEM) and mediation analysis to test three hypotheses: (1) MCS directly improves CP, (2) MCS fosters OC, and (3) OC mediates the MCS-CP relationship. Key findings reveal that MCS significantly boosts CP (β=0.438, p<0.001), while OC partially mediates this relationship (indirect effect β=0.249, p<0.001). The novelty lies in demonstrating how MCS transcends operational efficiency to shape cultural assets, which in turn drive competitive advantage. This research advances contingency theory by highlighting sector-specific adaptations, such as digital MCS tools balancing tradition with market responsiveness, and offers practical insights for integrating control systems with cultural stewardship in traditional industries.
Policy Recommendations for Enhancing the Green Banking and Sustainable Development
This study examines the key factors influencing green banking and sustainable development in Vietnam to provide evidence-based policy recommendations to promote the integration of sustainability within financial institutions. A mixed-method approach combining qualitative and quantitative analyses was adopted. The research process began with focus group discussions with 30 banking experts, followed by in-depth interviews with senior managers to refine measurement scales. Subsequently, a structured survey was conducted among 900 commercial bank managers in the Southeast region, and the collected data (n = 845 valid responses) were analyzed using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling (SEM). The findings reveal seven knowledge-driven factors that significantly affect green banking and sustainable development: the legal framework and supporting policies, awareness and trends in sustainable consumption, financial technology, leadership commitment and corporate culture, pressure from investors and international organizations, climate change and environmental risk management, and public-private partnerships. Among them, the legal framework and supporting policies emerged as the most influential drivers. Green banking practices are also shown to directly contribute to sustainable development by financing environmentally friendly projects and integrating ESG principles. The study’s novelty lies in its knowledge-based economy perspective, demonstrating how policy knowledge, financial technology, and organizational learning interact to enhance sustainability. Practical implications highlight the need for regulatory reform, technology adoption, and cross-sectoral collaboration to accelerate Vietnam’s transition to a green economy.
The Impact of Climate-Smart Technology Adoption on Agricultural Productivity and Environmental Sustainability
This study provides an empirical assessment of emerging opportunities and offers a conceptual framework for understanding the potential impacts of climate-smart agriculture (CSA) adoption on agricultural productivity and environmental sustainability. Focusing on Uzbekistan, the research employs quantitative analysis of farm-level data, adoption gradient modeling, and return-on-investment (ROI) estimation to examine how CSA technologies influence key farm-level outcomes, including yields, income, resource-use efficiency (RUE), soil erosion, and water quality under world constraints. In cotton-wheat systems, the usage of six or more CSA is associated with a 71% increase in farm income, a 43% rise in crop yields, and a 48% improvement in resource-use efficiency (RUE), compared to farms with low levels of CSA usage. Fertilizer micro-dosing is associated with an average increase in cotton yields of 245.8 kg ha⁻¹ yr⁻¹ and delivers a ROI of 456%. Multivariate regression models account for 57.3% of the variation in yield and 61.8% in farm income, underscoring the explanatory power of CSA adoption patterns. Comparative analyses demonstrate that organic matter-based practices consistently outperform capital-intensive alternatives in both economic and environmental terms. The methodological approach integrates monitoring, reporting, and verification (MRV) indicators, payback period estimations, and threshold analyses tailored to risk-sensitive smallholder contexts. The findings provide robust empirical support for evidence-informed CSA policy formulation, including the design of targeted subsidies, extension services, and investment strategies in Uzbekistan. By reconciling global CSA implementation paradigms with localized constraints, the study generates scalable and empirically validated approaches, offering methodological relevance for analogous agroecological and institutional contexts.
Retrieval-Augmented Generation Assistant for Anatomical Pathology Laboratories
Accurate and efficient access to laboratory protocols is essential in Anatomical Pathology (AP), where up to 70% of medical decisions depend on laboratory diagnoses. However, static documentation such as printed manuals or PDFs is often outdated, fragmented, and difficult to search, creating risks of workflow errors and diagnostic delays. This study proposes and evaluates a Retrieval-Augmented Generation (RAG) assistant tailored to AP laboratories, designed to provide technicians with context-grounded answers to protocol-related queries. We curated a novel corpus of 99 AP protocols from a Portuguese healthcare institution and constructed 323 question-answer pairs for systematic evaluation. Ten experiments were conducted, varying chunking strategies, retrieval methods, and embedding models. Performance was assessed using the RAGAS framework (faithfulness, answer relevance, context recall) alongside top-k retrieval metrics. Results show that recursive chunking and hybrid retrieval delivered the strongest baseline performance. Incorporating a biomedical-specific embedding model (MedEmbed) further improved answer relevance (0.74), faithfulness (0.70), and context recall (0.77), showing the importance of domain-specialized embeddings. Top-k analysis revealed that retrieving a single top-ranked chunk (k=1) maximized efficiency and accuracy, reflecting the modular structure of AP protocols. These findings highlight critical design considerations for deploying RAG systems in healthcare and demonstrate their potential to transform static documentation into dynamic, reliable knowledge assistants, thus improving laboratory workflow efficiency and supporting patient safety.
LH-Moments Parameter Estimation of Weibull Distribution
Natural disasters such as sudden floods, storms, severe snowfall, and droughts are major problems in the world. Generally the distributions of extreme values are heavy-tailed distributions, and an important heavy-tailed distribution is the Weibull distribution, especially for non-linear behaviors. Therefore, accurately estimation of the occurrence of disasters is required to deal with such situations in a timely and efficient manner. Several methods can be used to estimate the parameters, for example, moments estimate, maximum likelihood estimate, linear of moment, and high-order L-moments. The objectives of this article are to estimate the parameters of the four-parameter Weibull distribution with weak non-linear effects (W4DN) based on the LH-moments method, and to propose a new parameter estimation formula. The proposed formula is classified into two cases based on the coefficient of the second-order term (δ): Case 1, where the coefficient is positive (δ > 0) and Case 2, where the coefficient is negative (δ < 0). In both cases, the corresponding estimation formulas are derived βr and λrp for p=1, 2, ... and r=1, 2, ..., respectively. The parameter estimations (γ ̂,α ̂,δ ̂,ϕ ̂ and κ ̂) are then optimized using the augmented Lagrangian adaptive barrier minimization algorithm. These formulas provide a practical approach for parameter estimation that is essential for forecasting extreme events in various disciplines, including hydrology, meteorology, insurance, finance, and engineering.
Design and Evaluation of C-Band Microstrip Antenna Array for Portable Ground Surveillance Radar
This study aims to design, simulate, fabricate, and evaluate a high-gain C-band microstrip antenna array with a corrugation plate for Portable Ground Surveillance Radar (PGSR) applications, addressing the need for compact, high-performance antennas in border security operations. The proposed design targets a minimum gain of 20 dBi, a horizontal beamwidth of ≤ 2.8°, a vertical beamwidth of ≤7.5°, horizontal polarization, and compact physical dimensions for field portability. The methodology involved electromagnetic simulations to optimize the slit-patch array geometry, fabrication using Rogers RO-4350B substrate for its stable dielectric properties, and performance validation in an anechoic chamber using a vector network analyzer. The fabricated prototype achieved strong agreement with simulations in key metrics: realized gain exceeded 20 dBi, return loss reached -27.35 dB, and SWR was approximately 1.2, confirming effective impedance matching. The corrugation plate enhanced impedance matching, improved transmission efficiency (S21), and reduced reverse isolation (S12), while S22 remained stable. Despite these strengths, the measurement beamwidths, especially vertical beamwidth (~30°), exceeded both simulation and target values, highlighting fabrication precision and alignment as areas for improvement. The novelty of this work lies in integrating a corrugation plate to improve impedance matching and the correlation between simulation and measurement, offering a practical, tuneable enhancement to microstrip antenna arrays for PGSR and similar radar systems.
DML-IDS: Distributed Multi-Layer Intrusion Detection System for Securing Healthcare Infrastructure
In recent years, the number of cyberattacks targeting healthcare resources has rapidly increased. Conventional IDSs rely heavily on predefined rules and attack signatures. However, modern zero-day attacks with unpredictable behavior and multi-vector attack patterns can still breach healthcare networks. When a new type of cyberattack targets a specific server, an existing IDS may fail to detect it because it depends on static, predefined rules. To address these issues, we propose DML-IDS: Distributed Multi-Layer Intrusion Detection System, designed to operate across multiple nodes in a network to collaboratively detect suspicious activities. The proposed approach employs a multi-layer ensemble strategy to improve detection accuracy while reducing computational overhead on a single machine. All incoming network packets are first analyzed by the Distributed Threat Analysis Module (DTAM), which runs a Random Forest-based model as the base classifier to distinguish between benign and malicious traffic. Based on the nature and severity of the threat, malicious packets are flagged as highAlert (HA) in the Threat Prioritization Layer (TPL) and then forwarded to the respective Confirmatory Ensemble Model (CEM) for further, attack-specific analysis. These CEM models are designed to scale efficiently and detect zero-day as well as multi-vector attacks. The proposed model was trained on the CICIDS-2017 dataset. DTAM achieved an accuracy of 98.5%, while the CEM models for DDoS, Patator, and Web Attack achieved 99.01%, 98.87%, and 98.91% accuracy, respectively. Furthermore, the computational overhead of the DML-IDS architecture was evaluated and compared with an existing ensemble learning-based IDS.
Leaving No One Behind in Access to Higher Education in the Baltic States and Poland
The article presents research on the accessibility of higher education in the Baltic States and Poland and its compliance with the principle of “Leaving No One Behind”. A qualitative approach was used to achieve the research objective using analysis of statistical indicators and the contents of documents. The results of the study reveal that the progress on higher education attainment is not homogeneous and not consistent in the countries analyzed. Document analysis indicates that higher education accessibility differs across Lithuania, Latvia, Estonia, and Poland. Lithuania and Latvia are the most accessible due to unified application systems, while Poland lacks such a system, creating extra costs and delays. A comparison of the mandatory examinations and minimum requirements set by the countries shows that Lithuania has the highest barriers to access to higher education: people with lower results or learning gaps in secondary education are deprived of the opportunity to acquire higher education, and their chances of avoiding falling behind are lower. Lithuania does not provide for exceptions for the admission of people with disabilities: they must meet the same requirements as other applicants. Another group of people who may face exclusion in Lithuania, Latvia, and Poland are members of national minorities. These results suggest that the governments of the countries and universities need to be more inclusive in their admission policies.
The Influence of Work Motivation on Job Performance: Engagement and Burnout as Mediators
Based on self-determination theory, a conceptual model is proposed in which work motivation operates as the antecedent variable, with work engagement and occupational exhaustion acting as dual mediators. To test this framework, data were collected through a structured questionnaire from 469 academic staff members across 24 private higher education colleges in Jiangxi Province and analyzed using structural equation modeling (SEM). The results demonstrate that greater levels of educators' work motivation are significantly correlated with improved job performance and that this effect is channeled through increased work engagement and reduced burnout. By elucidating these mediatory pathways, the findings deepen theoretical comprehension of how motivation drives performance and yield practical guidance for devising effective motivation and performance-management strategies within private higher education institutions.
Credit Allocation to Private Sector and Growth: An ARDL Analysis for a Transitional Economy
This study examines the role of credit allocation to the private sector in driving economic growth in Vietnam’s transitional economy. The primary objective is to evaluate whether bank credit allocation fosters sustainable output expansion or, conversely, produces diminishing returns when it surpasses optimal levels. Employing the Autoregressive Distributed Lag (ARDL) bounds testing framework, the analysis uses annual data for 1990–2024 and compares three specifications: credit to the private sector, aggregate credit to the economy, and credit to the state sector. Findings indicate a robust long-run cointegration between credit and output, but with a clear nonlinear pattern: private credit enhances growth up to a threshold of roughly 91% of GDP, beyond which its marginal effect declines. While capital formation and moderate inflation consistently support long-term growth, foreign direct investment exerts mainly short-term benefits, and state-directed credit shows no significant contribution. The novelty of this paper lies in extending previous studies through a longer time horizon, updated post-GDP-revision data, and explicit disaggregation between private and state credit. By highlighting threshold effects and sectoral inefficiencies, this research improves understanding of the credit–growth nexus in transitional economies and underscores the need to prioritize credit quality, efficiency, and SME access in credit policy. JEL Classification: E51, G21, O47, P27.
Rateless Polar Codes Exploiting Repetition Coding Principle with EXIT Analysis for Broadband Transmissions
This paper proposes a novel design of polar codes for rateless transmissions employing extended parity (EP) to enhance performance under broadband channel conditions. The idea of the proposed design is to achieve diversity across all samples by employing simple butterfly XOR operations, which inherently support rateless broadband transmissions. In particular, the design exploits the principle of repetition, where simple XOR operations do not only contribute to error protection but also strengthen the polarization effect and reinforce the rateless property of polar codes. The proposed codes are evaluated over Rayleigh fading, fully interleaved, and additive white Gaussian noise (AWGN) channels. The results show that the proposed codes achieve significant performance improvements, particularly in AWGN and fully interleaved environments, thereby confirming that the use of XOR operations effectively enhances transmission reliability. Furthermore, the proposed codes are investigated through extrinsic information transfer (EXIT) analysis using closed-form expressions. The analysis reveals that the decoding process exhibits faster convergence when EP is employed. In addition, computational complexity analysis shows that the additional overhead introduced by EP remains minimal. Importantly, the proposed structure preserves the standard polar transform and decoding graph, ensuring scalability similar to conventional polar codes. Hence, the proposed design balances performance and computational efficiency, making it a compelling solution for broadband scenarios and dynamic channel environments.
Extrusion Technology for Complex Processing of Brewery Waste Into Feed Products for Livestock and Poultry
An energy-efficient extrusion technology for the complex processing of wet brewing waste into feed products for animals and poultry is proposed and evaluated. The study aims to replace traditional energy-intensive drying methods – typically involving natural gas, steam, or boiler exhaust gases – with a more sustainable extrusion process. The approach allows direct utilization of wet brewing by-products, such as brewers’ grains and brewers’ yeast, without preliminary drying, thereby reducing energy consumption by up to 50%. The technological development was based on systems analysis and synthesis of extrusion processes, combining wet brewing waste with dry feed components. The research identified optimal parameters for extrusion: a feed mixture to compound feed component ratio of 1:1.85–2; initial moisture content of 28–30%; extrusion temperature of 140–150 °C; and barrel pressure of 4–8 MPa. The final product was a partially dehydrated mass with a moisture content of 60–65%, suitable for use as a feed additive or complete compound feed. The results demonstrate improved product quality and extended shelf life due to thermal and mechanical treatment during extrusion. The novelty of the approach lies in bypassing the conventional drying step, offering a cost-effective and environmentally friendly way to increase the value of brewing industry waste.
Linking Psychological Safety Climate to Dual Innovation Through AI-Enabled Dynamic Capabilities
Objective: This study develops and empirically validates an integrated model that explains how the psychological safety climate influences dual innovation through AI-enabled dynamic capabilities in Chinese design organizations. Methods: A cross-sectional survey was conducted among 281 designers from industry design firms and departments. Data analysis employed partial least squares-structural equation modeling, including mediation bootstrapping analysis, importance-performance map analysis, necessary condition analysis, and quadratic effect analysis. Findings: All hypotheses received strong empirical support. The psychological safety climate has a significant influence on AI-enabled dynamic capabilities, with a path coefficient of 0.452 at p <0.001, and on dual innovation, with a coefficient of 0.383 at p < 0.001. AI-enabled dynamic capabilities have a positive impact on dual innovation, with a coefficient of 0.384 at p < 0.001, and significant mediation effects, indicating an indirect effect of 0.174 at p < 0.001. The model explains 42.7% of the variance in dual innovation. Importance-performance analysis reveals a psychological safety climate as highly important but moderately performing, indicating strategic opportunities for improvement for organizations. Necessary condition analysis confirms both constructs as essential requirements for innovation outcomes. The findings demonstrate that psychological safety climate, as a higher-order cultural resource, enables lower-order AI-enabled dynamic capabilities, supporting socio-technical systems structure for dual innovation. Organizations should prioritize investments in psychological safety while maintaining their AI capabilities. Novelty: This research introduces AI-enabled dynamic capabilities as a second-order formative construct and establishes the meta-capability role of psychological safety climate in AI-enabled dynamic capabilities and dual innovation, thereby extending the resource-based view and dynamic capabilities theories through micro-foundational perspectives.
Evaluating Digital Transformation Risks in Logistics and Supply Chain Management with PLS-SEM-ANN-fsQCA
This study investigates the risks associated with digital transformation (DT) implementation in Vietnam’s logistics and supply chain management (SCM) sector, utilizing a hybrid PLS-SEM-ANN-fsQCA methodology to analyze data from 243 valid questionnaires. Anchored in the Technology-Organization-Environment framework augmented with human factors (TOE+H), the research aims to examine how technological, organizational, environmental, and human factors influence DT adoption and associated risks, including financial, operational, cybersecurity, and reputational risks, while exploring the moderating roles of firm size and digital literacy. Findings reveal that TOE+H factors significantly drive DT implementation, but misalignment, ineffective management, market volatility, and limited digital literacy amplify risks, particularly cybersecurity vulnerabilities. Moderation analyses indicate that high digital literacy, larger firm size, and regulatory compliance mitigate these risks. Artificial neural network (ANN) analysis highlights non-linear relationships, emphasizing technological and human factors as key drivers, while fuzzy-set qualitative comparative analysis (fsQCA) identifies configurations, such as strong technological-human factor alignment, linked to successful DT outcomes. Importance-Performance Map Analysis (IPMA) prioritizes technological and human factors for resource allocation to enhance sustainability. This study advances the TOE+H framework by integrating a hybrid methodology, offering novel insights into DT risk dynamics and practical strategies for sustainable logistics in Vietnam’s SCM sector.
Influence of Digital Infrastructure on Project Management Success: Readiness, Fitness, and Tools as Moderators
In an era defined by rapid digital transformation, project-based organizations face increasing pressure to modernize and digitalize their operational frameworks to ensure competitive advantage and Project Management Success. However, only limited research has examined how foundational pillars of digital infrastructure interact and jointly influence project outcomes, leaving a gap in understanding the structural pathways that link digital infrastructure to Project Management Success and informed decision-making. This study investigates how the three pillars of digital infrastructure (Digital Readiness, Digital Fitness, and Digital Tools) jointly and individually influence Project Management Success through applying Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze survey data collected from experienced project management professionals who possess appreciable experience in digital technology systems and digital transformation leadership. The developed model captures both direct effects of the Digital Infrastructure Pillars on Project Management Success and the moderation effects between them. The results show that all three pillars significantly enhance Project Management Success, with Digital Readiness emerging as the most influential strategic enabler. Moreover, Digital Readiness positively moderates the effect of Digital Tools on Project Management Success, indicating that digital technology investments are more effective when supported by foundational digital preparedness. These findings offer a validated framework for assessing digital maturity in project environments and provide strategic and leadership guidance for organizations seeking to enhance project performance and achieve Project Management Success through balanced investment in digital infrastructure, integrated digital transformation strategies, digital technology adoption, and digital competencies.
Enhance Multimodal Retrieval-Augmented Generation Using Multimodal Knowledge Graph
Large Language Models (LLMs) have shown impressive capabilities in natural language understanding and generation tasks. However, their reliance on text-only input limits their ability to handle tasks that require multimodal reasoning. To overcome this, Multimodal Large Language Models (MLLMs) have been introduced, enabling inputs such as images, text, video and audio. While MLLMs address some limitations, they often suffer from hallucinations because of over-reliance on internal knowledge and face high computational costs. Traditional vector-based multimodal RAG systems attempt to mitigate these issues by retrieving supporting information, but often suffer from cross-modal misalignment, where independently retrieved text and image content cannot align meaningfully. Motivated by the structured retrieval capabilities of text-based knowledge graph RAG, this paper proposes VisGraphRAG to address the challenge by modelling structured relationships between images and text within a unified MMKG. This structure enables more accurate retrieval and better alignment across modalities, resulting in more relevant and complete responses. The experimental results show that VisGraphRAG significantly outperforms the vector database-based baseline RAG, achieving a higher answer accuracy of 0.7629 compared to 0.6743. Besides accuracy, VisGraphRAG also shows superior performance in key RAGAS metrics such as multimodal relevance (0.8802 vs 0.7912), showing its stronger ability to retrieve relevance information across modalities. These results underscore the effectiveness of the proposed Multimodal Knowledge Graph (MMKG) methods in enhancing cross-modal alignment and supporting more accurate, context-aware generation in complex multimodal tasks.
Evaluating Sensitivity of Double EWMA Chart for ARL Under Trend SAR(1) Model and Applications
The goal of this study is to offer the precise average run length (ARL) on the Double Exponentially Weighted Moving Average (double EWMA) control chart for the data underlying the first-order seasonal autoregressive (SAR(1)L) with trend model. A comparison was made between the explicit formula and the computed ARL obtained using the numerical integral equation (NIE) approach, employing four quadrature methods: the midpoint, Simpson’s, trapezoidal, and Boole’s rules. The comparison was based on accuracy percentage (%Acc) and computation time (in seconds). The results showed that there was not much variation in accuracy between the ARL results of the explicit ARL and ARL via the NIE method. The findings indicate that the explicit ARL and NIE approaches produce very consistent accuracy values; however, the explicit formula is significantly more rapid (instantaneous compared to 1.5–26 seconds). The advantage of the double EWMA chart compared to the extended EWMA chart in identifying process changes is demonstrated, encompassing evaluations under both one-sided and two-sided setups with varied LCL values. The results are additionally corroborated by sensitivity measures (AEQL, PCI, RMI) and checked with actual durian export data, guaranteeing that the conclusions are firmly established in both simulated and empirical evidence.
Human-Centered Organizational Culture in the Global Workplace: Strategic Approaches, Trends, and Practical Models
Amid accelerating global transformations, the need to reconsider human resource management strategies through the lens of human-centricity is becoming increasingly urgent. This study aims to examine the systemic implementation of human-centered organizational culture within international labor contexts, with a focus on enhancing employee well-being, adaptability, and organizational resilience. A mixed-methods approach was employed, combining comparative policy analysis, content analysis of regulatory documents, and empirical case studies. The empirical sample included 320 employees from multinational companies across four sectors (education, IT, healthcare, and manufacturing). The findings revealed statistically significant improvements following the implementation of the proposed model: autonomy increased from 5.48 to 5.86 (p = 0.012), competence from 5.33 to 5.61 (p = 0.038), and relatedness from 5.07 to 5.58 (p = 0.004). Positive emotion expression scores rose from 3.98 to 4.42 (p = 0.009), while the Human-Centeredness Index increased from 4.18 to 4.71 (p = 0.002). These results underscore the limitations of hierarchical management models and highlight the value of flexible, emotionally supportive systems. The scientific contribution of the study lies in the typologization of human-centric management models and the empirical validation of a scalable integration framework that combines emotional intelligence development, inclusive feedback cycles, and leadership support. This model provides a strategic foundation for building sustainable, inclusive, and ethically grounded organizational environments.
Multi-Objective Optimization of Injection Molding Using Taguchi, Fuzzy Methods, and GA
The objective of this research is to optimize the injection molding process of an automotive window regulator bracket by improving the moldability index while minimizing key defects. To achieve this, a multi-objective framework is developed that combines the Taguchi method with Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy-TOPSIS. Five critical processing parameters—melt temperature, mold temperature, filling time, holding pressure time, and cooling time—were investigated, with polypropylene as the base material. A Taguchi L25 orthogonal array was employed to reduce the number of experimental trials from 3,125 to just 25, thereby saving resources while maintaining reliability. The evaluation considered warpage, residual stress, and shear stress, which are the most influential defects affecting part performance. Finite Element Analysis (FEA) was incorporated to validate the accuracy of the results, while a hybrid ANFIS-GA predictive model was applied to forecast the moldability index, demonstrating an improvement of about 1% over conventional optimization methods. The optimized settings resulted in minimized warpage (1.8122 mm), residual stress (43.03 MPa), and shear stress (0.08 MPa). The novelty of this work lies in integrating Taguchi with FAHP and Fuzzy-TOPSIS for a single-objective transformation, offering a systematic and efficient approach for multi-objective optimization in injection molding applications.