Theoretical Advances Research Articles

Factors Affecting to Acceptance and Adaption of HR Analytics of Apparel Companies in Western province Sri Lanka

This study examines the factors influencing the adoption and acceptance of Human Resource (HR) analytics in Sri Lanka’s apparel industry, focusing on bridging the gap between theoretical advancements and practical implementation. As HR analytics becomes an essential strategic tool for human capital management, understanding its key drivers is crucial. This research explores the impact of social influence, resource availability, data availability, and HR professionals’ self-efficacy on adopting of HR analytics technologies. Using a cross-sectional survey of HR experts in apparel companies, the study finds strong positive relationships among these factors, with data availability and self-efficacy emerging as the most significant predictors. While resource availability shows a positive association, its impact on adoption remains statistically insignificant. The findings highlight the necessity of equipping HR professionals with the required tools and fostering confidence in analytics-driven decision-making. Organizations should prioritize structured training programs and create supportive environments that enable HR specialists to engage effectively with analytics. By addressing key barriers to data-driven HR practices, this study contributes to the broader understanding of HR analytics adoption in developing economies and provides insights applicable to other labor-intensive industries.

Disturbance and stability dynamics in microbial communities for environmental biotechnology applications.

Disturbance and stability dynamics in microbial communities for environmental biotechnology applications.

Intelligent Fault Diagnosis in 330 kV Power Networks Using SVM and ANN Techniques: Case of the Onitsha-New Haven Route

This paper investigated the application of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for transient fault detection and classification in power transmission systems, focusing on Nigeria’s Onitsha-New Haven 330 kV network. A Multilayer Perceptron (MLP) ANN, optimised with 10 hidden layers and the Levenberg-Marquardt algorithm, achieved near-perfect regression (correlation coefficient R > 0.999 ) and classification accuracy (≈99%) using 9,180 operational samples, validated against IEEE-compliant transient stability indices (Si) and dynamic voltage margins (DVm). Comparatively, SVMs demonstrated ~95–98% accuracy with sub-5 inference times, using pre-engineered features (such as harmonic distortions and wavelet coefficients) for real-time efficiency in resource-constrained environments. The study proposed a hybrid ANN-SVM framework, combining SVM’s rapid fault detection with ANN’s precision for post-event diagnostics, addressing the speed-accuracy trade-off in dynamic grids. Engineering implications highlight enhanced grid resilience, cost-effective deployment strategies, and support for renewable integration. At the same time, contributions include empirical validation of AI models in sparse-data contexts and methodological benchmarks aligning machine learning with power quality standards. Recommendations advocated phased AI adoption, workforce training, and regulatory standardisation, with future research directions spanning hardware-in-the-loop validation, cybersecurity, and adaptive learning for renewable-rich grids. This work bridges theoretical AI advancements with practical power system needs, offering scalable solutions for global energy transitions.

Big Data Analytics in E-Commerce using Distributed Deep Neural Networks

Currently, there is a pressing need to emphasize big data analytics (BDA) within the e-commerce sector. Despite its potential, BDA remains not sufficiently explored, which obstructs both its theoretical and practical advancement. This paper systematically reviews the literature on BDA in e-commerce, offering a comprehensive framework that addresses its definitional aspects, characteristics, types, business values and challenges within the e-commerce context. Furthermore, the paper encourages broader discussions regarding future research challenges and opportunities in both theoretical and practical domains. Overall, the findings provide a thorough understanding of various BDA concepts, offering deeper insights into its diverse applications in e-commerce. In recent years, distributed deep neural networks (DDNNs) and neural networks (NNs) have demonstrated exceptional performance across a wide range of applications. For instance, deep convolutional neural networks (DCNNs) have continuously acquired new and additional features for various tasks in computer vision. Concurrently, a large number of end devices including Internet of Things (IoT) devices, has expanded significantly. These devices present attractive targets for machine learning applications due to their frequent connection to sensors such as cameras, microphones, and gyroscopes, which record substantial amounts of input data in a streaming mode. This study delineates the architecture of a Distributed Deep Neural Network (DDNN) that encompasses various computational hierarchies, including multiple end devices, edge nodes, and cloud platforms. The proposed concept is recognized for being new due to its belief in two fundamental layers: the convolutional layer and the pooling layer. The primary aim of integrating these two layers is to achieve optimal performance outcomes. Our findings demonstrate that the proposed methodology yields superior results in terms of accuracy and costeffectiveness, achieving a precision rate of 99% and an economical cost of 25. Consequently, we conclude that these results surpass those reported in recent literature.

Corruption dynamics: Integrating structure, agency and institutional logics across contexts

AbstractAlthough the literature on corruption is extensive, it is also fragmented, with inconsistencies and inconclusive findings that limit theoretical and practical advancements. Scholars have called for multi‐factor, multi‐level frameworks that integrate macro, micro and longitudinal aspects to provide a more cohesive understanding of corruption's persistence and mitigation. This study addresses these calls through a systematic literature review, offering a critical synthesis of structural and agential perspectives on corruption. Thereby it identifies gaps in existing scholarship and proposes an integrated framework that situates corruption within the interaction of two key dimensions: institutional resource allocation and institutional maturity. By drawing on institutional logics, it positions corruption and anti‐corruption as outcomes of competing institutional logics, shaped by the resources committed to their embedding. This perspective enables a dynamic examination of how structure and agency interact to sustain or challenge corruption. Specifically, it accounts for how institutional resources either reinforce dominant logics or enable shifts towards alternative, reformist logics. It allows for the study of dynamic feedback loops and pathways that influence corruption's evolution. Resource allocation and institutional maturity interact recursively, shaping institutional logics over time. These dynamics highlight the role of feedback loops in driving corruption escalation, stabilization or reform. By synthesizing insights from the literature, the study highlights pathways for future research, including investigating the role of institutional resource allocation, temporal dynamics and the mechanisms underlying transitions between stages of corruption. It reconciles theoretical tensions between structural and agential accounts, offering actionable insights for scholars and policymakers.

Integrating Indigenous Rattan Elements for Functional Sustainability in Contemporary Interior Design

Purpose: This study investigates the potential of maximising rattan use in interior design, focusing on its versatility, environmental benefits, and the artisans' sustainability consciousness. The primary objective was to assess artisans' awareness of environmental issues related to the use of rattan. Design/Methodology/Approach: The population of artisans directly involved in the trade in the central enclave of Accra was asked to respond to a questionnaire. A cross-sectional approach, which allows for collecting data that reflects the current circumstances, was used. The convenience sampling technique was employed to collect data from 32 respondents. Findings: It was found that rattan is widely accepted for both indoor and outdoor use, especially in living rooms, parlours, and balconies. Artisans demonstrated a strong awareness of the need for sustainable practices, although they rely heavily on government support for effective resource management. Research Limitation: The respondents were predominantly urban or peri-urban dwellers. Practical Implication: The outcome of this study would enhance the sustainability of the aesthetic rattan handicraft industry, its contribution to eco-friendly interior design, and the subsequent ripple effects of reducing global warming through carbon sequestration. Social Implication: This paper advocates for the development of rattan plantations and the handicraft industry in Ghana, thereby reducing rural and urban poverty and contributing to a decline in carbon emissions, which are crucial tools for achieving Sustainable Development Goals 1 and 13. Originality/Value: This study advances beyond conventional sustainability metrics by introducing the concept of "functional sustainability" as a design approach, where materials must simultaneously meet contemporary performance requirements while preserving their traditional ecological and cultural functions. This represents a significant theoretical advancement in the discourse on sustainable design.

The history and future of resting-state functional magnetic resonance imaging.

Since the discovery of resting-state functional connectivity in the human brain, this neuroimaging approach has revolutionized the study of neural architecture. Once considered noise, the functional significance of spontaneous low-frequency fluctuations across large-scale brain networks has now been investigated in more than 25,000 publications. In this Review, we provide a historical overview and thoughts regarding potential future directions for resting-state functional MRI (rsfMRI) research, highlighting the most informative analytic approaches that have been developed to reveal the brain's intrinsic spatiotemporal organization. We review the collaborative efforts that have led to the widespread use of rsfMRI in neuroscience, with an emphasis on methodological innovations that have been made possible by contributions from electrical and biomedical engineering, physics, mathematics and computer science. We focus on key theoretical and methodological advances that will be necessary for further progress in the field, highlighting the need for further integration with new developments in whole-brain computational modelling, more sophisticated approaches to brain-behaviour mapping, greater mechanistic insights from concurrent measurement of neurophysiology, and greater appreciation of the problem of generalization failure in machine learning applications. We propose that rsfMRI has the potential for even greater clinical relevance when it is fully integrated with population neuroscience and global health initiatives in the service of precision psychiatry.

Research hotspots and trends in the Historic Urban Landscape: a bibliometric perspective

ABSTRACT As urbanisation accelerates and climate change intensifies, the tension between urban development the preservation of cultural heritage more pronounced. By adopting interdisciplinary integrative approach, the Historic Urban Landscape (HUL) offers a significant framework for amalgamating cultural heritage conservation and urban planning. This study employs a combination of bibliometric and qualitative analyses to evaluate the current status in the field as well as developmental trends. Furthermore, it utilises software tools to thoroughly analyse the development trajectory and evolution, identifying the most influential countries, institutions, and authors, a network and co-occurrence analysis of the HUL. Over the last 20 years, literature on the subject has increased significantly while research focuses have changed, moving from static preservation to adaptive reuse, cultural tourism, and technology integration. Europe and North America have been at the forefront of theoretical and technological advancement, whilst Asia and Latin America have added value through practical case studies. Nevertheless, social equity, ecological sustainability, and conservation practices in resource-limited environments remain significant gaps within the current literature. By constructing a knowledge map of the HUL, this study reveals the core issues and research hotspots and proposes future research directions, providing scientific basis for cultural heritage preservation and sustainable urban development.

Concluding Notes—Looking Back; Looking Forward

This summary paper provides a brief review of major contributions in a group of papers that were collected to honor the work of Bernard Maskit, who died in March 2024. Professor Maskit was a mathematician who contributed major theoretical and methodological advances in the study of multiple code theory and the referential process. Each of the papers in this issue incorporate applications of his ideas and the new measures that he developed. The applications include basic epistemological ideas, linguistic findings, and innovative approaches to psychotherapy theory and research. Plans for future theoretical and applied research directions are discussed.

Organization-Market (OM) Composite: Beyond the Reflexive Approach for A Theory of Market and Marketing

Abstract: Organizational decisions and actions are a balance between two interacting forces; an entrepreneurial aspiration and market dynamics. Marketing is the strategic interface and a function for market engagement. Theorization in marketing has been addressing specific organizational issues, but falls short in conceptualizing a unified marketing theory. This research examines objectives of marketing research, relationships and questions if they are grounded in a foundational understanding of ‘what the market is?’, the very environment to which organizations are inherently tied. We derive basic characteristics of market reflexively from the current theorization around market and marketing, where marketing has been a seller-oriented function and market is a mix of discrete parts and activities. Beyond these concepts, we identify and redefine market as a three-dimensional domain. Accordingly, we conceptualize market-based perspective of organization and its marketing function. The three-dimensional market enables an environment through ‘Marketing Objectives of Interaction’ consisting of guiding principles for interaction between the market and the organization. Organization and the objectives of interaction, together create an ‘Organization-Market’ composite making the organization a participant, along with many other participants who constitute the market environment. The composite presents the dynamic nature of organization’s marketing and its complexity in dealing with the multitude of market variables. Together these concepts evolve a new approach for theoretical advancements in understanding market, various facets of marketing and relationships in a dynamic organizational context. This framework offers a novel approach for marketing research, facilitating integration with organizational studies on design, strategy and management.

Synchronized Carrier-Wave and High-Frequency Square-Wave Periodic Modulation Strategy for Acoustic Noise Reduction in Sensorless PMSM Drives

High-frequency injection (HFI) is widely adopted for the sensorless control of permanent magnet synchronous motors (PMSMs) at low speeds. However, conventional HFI strategies relying on fixed-frequency carrier modulation and square-wave injection concentrate current harmonic energy within narrow spectral bands, thereby inducing pronounced high-frequency motor vibrations and noise. To mitigate this issue, this paper proposes a noise suppression strategy based on synchronized periodic frequency modulation (PFM) of both the carrier and high-frequency square-wave signals. By innovatively synchronizing the periodic modulation of the triangular carrier in space vector pulse width modulation (SVPWM) with the injected high-frequency square wave, harmonic energy dispersion and noise reduction are achieved, substantially lowering peak acoustic emissions. First, the harmonic characteristics of the voltage-source inverter output under symmetric triangular carrier SVPWM are analyzed within a sawtooth-wave PFM framework. Concurrently, a harmonic current model is developed for the high-frequency square-wave injection method, enabling the precise derivation of harmonic components. A frequency-synchronized modulation strategy between the carrier and injection signals is proposed, with a rigorous analysis of its harmonic suppression mechanism. The rotor position is then estimated via high-frequency signal extraction and a normalized phase-locked loop (PLL). Comparative simulations and experiments confirm significant noise peak attenuation compared to conventional methods, while position estimation accuracy remains unaffected. This work provides both theoretical and practical advancements for noise-sensitive sensorless motor control applications.

Neutrinoless double beta decay in 2025

Neutrinoless double-beta decay is currently the most sensitive known method for determining if neutrinos are Majorana particles. Significant progress over the past two decades in experimental techniques, background mitigation, and nuclear matrix element calculations has been coupled with negative results, implying potentially extremely long lifetimes. This review summarizes the process, recent theoretical and experimental advances, the challenges of increasing sensitivity to very long lifetimes, and promising future experimental efforts.

Materialized Action: Reformulating the “Doing of” Math Through Fiber Crafting

This article examines how fiber crafting as a category of activity can develop mathematics learning and the conditions under which various fiber crafting traditions differentially cultivate mathematical understanding. Modifying the constructionist paradigm with relational materialist principles, this paper advances the notion of “materialized action,” which describes the natural inquiry process that results through emergent patterns between learners and the materialized traces of their actions. This paper takes a qualitative approach, combining a design and intervention phase to look closely across a set of materials (i.e., three fiber crafts, knitting, crochet, and pleating) and engagement in a “powerful idea” (i.e., the role of unitizing in multiplicative proportional reasoning), as instantiated across three youth case studies, and as an illustration of how we can better understand micro-developmental learning processes. We identified three levels of unitizing that make up the larger idea of enacting proportional reasoning (PR) through materialized action, which build and catalyze toward one another and support emergent understanding of PR from the intra-action of the material and the learner. In their engagement with PR, youth employed different strategies based on personal choice, affordances of the materials, and practices of the crafting traditions. Materialized actions as a theoretical advancement has the potential to reformulate what counts as mathematics and can guide the design of mathematics learning that is embracing (rather than reducing) worldly concreteness in learning key domain ideas, with implications for the design of more equitable learning environments.

Quantifying Temporal Dynamics in Global Cyber Threats: A GPT-Driven Framework for Risk Forecasting and Strategic Intelligence

Despite the exponential rise in cybersecurity incidents worldwide, existing analytical approaches often fail to detect subtle temporal dynamics in cyber threats, particularly on a quarterly scale. This paper addresses a critical research gap in the domain of temporal cyber risk analysis by introducing a mathematically rigorous and AI-augmented framework capable of identifying, validating, and forecasting quarterly shifts in global cyber-attack patterns. The methodology integrates a hybrid data acquisition pipeline with GPT-based AI classification to construct a structured, high-dimensional dataset comprising 11,497 cybersecurity incidents spanning from October 2023 to March 2025. These incidents cover 106 attack types, 29 industries, and 257 countries. The framework decomposes the dataset into quarterly intervals and applies mathematical formulations to compute frequency shifts across categorical variables (attack types, industries, countries) and numerical variables (attack significance), followed by robust statistical validations (Chi-square and ANOVA tests), time-series forecasting via ARIMA, and the computation of a Quarterly Composite Index (QCI). Key results reveal dominant attack types—Social Engineering (ing1733) and Zero-Day Exploits (1657)—and highlight sectoral vulnerabilities in IT (5959) and Government (2508). Statistically significant quarterly variations were confirmed (χ2=2319.13, F=3.78, p<0.001). ARIMA forecasts predict 1782–2080 incidents per quarter for 2025–2026, while QCI trends average around 0.75, signifying sustained volatility. The research delivers both theoretical and practical advancements by combining generative AI, temporal segmentation, and statistical modeling to create an operationalizable intelligence system. This contribution enhances strategic cybersecurity preparedness and policymaking in a complex, evolving threat landscape.

Comprehensive Review of Hybrid Energy Systems: Challenges, Applications, and Optimization Strategies

This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

Theoretical Advancements in Small Area Modeling: A Case Study with the CHILD Cohort

Developing accurate predictive models in statistical analysis presents significant challenges, especially in domains with limited routine assessments. This study aims to advance the theoretical underpinnings of longitudinal logistic and zero-inflated Poisson (ZIP) models in the context of small area estimation (SAE). Utilizing data from the Canadian Healthy Infant Longitudinal Development (CHILD) study as a case study, we explore the use of individual- and area-level random effects to enhance model precision and reliability. The study evaluates various covariates’ impact (such as mother’s asthma, mother wheezed, mother smoked) on model performance to predict child’s wheezing, emphasizing the role of location within Manitoba. Our main findings contribute to the literature by providing insights into the development and refinement of small area models, emphasizing the significance of advancing theoretical frameworks in statistical modeling.

‘To Avoid a Strange European Explanation’

This year marks a century since Finnish ethnographer Hilma Granqvist (1890–1972) embarked on her first journey to Palestine, an undertaking that proved intellectually rich alongside becoming a professional impasse. Initially setting out to study ‘The Women of the Old Testament’, she soon shifted her topic to reflect new methodological and theoretical insights. Yet, her pioneering research ultimately fell victim to the fate often faced by those ahead of their time. In Palestine, al-Sitt Halimah’s (as she was known amongst her Palestinian interlocutors) work has remained highly regarded for decades, with her detailed recordings of rural Palestinian customs contributing to Palestinian scholarship as well as identity-building (Abou-Hodeib 2021). Today, her work resonates more than ever given Israel’s ongoing genocide against the Palestinian people as well as against the erasure of Palestinian heritage and knowledge systems which continues unchecked—a process that Granqvist witnessed in its early stages in the 1920s. Upon her return to Finland, Granqvist was among the very few who openly criticised the Zionist colonial expansion in Palestine. Arguably, this stance, along with her theoretical and methodological advancements, contributed to her effective banishment from pursuing an academic career in Finland. In recent years, there has been a growing interest in her work, both in her native Finland and in Western academia at large.

Socially inclusive infrastructure for disaster risk reduction in urban planning: insights from the SADC region

IntroductionThe accelerating pace of urbanization in the 21st century has intensified the need for inclusive and resilient infrastructure development, particularly in the face of rising socio-economic inequalities and escalating disaster risks. This study explores the integration of social equity dimensions and Disaster Risk Reduction (DRR) principles within urban planning frameworks as a pathway toward sustainable and resilient urban futures.MethodsA qualitative research design was adopted, drawing on a triangulated methodology that includes document analysis, an extensive literature review, and selected case studies on urban planning practices. These methods were employed to interrogate current urban development paradigms and assess the extent to which equity and resilience are operationalized in planning processes.ResultsFindings indicate that, despite growing theoretical advancements in equitable and risk-sensitive urban development, significant systemic challenges remain. These include limited community participation in planning processes, persistent institutional silos, and resource constraints that hinder transformative action. The analysis further reveals that current urban governance mechanisms often lack the capacity to meaningfully integrate long-term resilience and equity considerations.DiscussionIn response to these challenges, the study recommends a paradigm shift in urban planning towards the adoption of future-oriented and participatory approaches. Key policy recommendations include: (1) embedding equity-focused DRR strategies within urban development policies; (2) fostering multi-stakeholder collaboration to bridge institutional divides; and (3) mainstreaming anticipatory and inclusive planning models in urban governance systems. Such integrative approaches are essential for aligning infrastructure development with the broader goals of social justice, environmental sustainability, and urban resilience.

Multiscale comparative connectomics

Abstract The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This information-rich data modality has the potential to transform our understanding of the relationship between patterns in brain connectivity and neurological processes, disorders, and diseases. However, existing computational techniques used to analyze connectomes are often insufficient for interrogating multi-subject connectomics datasets: many current methods are either solely designed to analyze single connectomes or leverage heuristic graph statistics that are unable to capture the complete topology of multiscale connections between brain regions. To enable more rigorous connectomics analysis, we introduce a set of robust and interpretable statistical hypothesis tests motivated by recent theoretical advances in random graph models. These tests facilitate simultaneous analysis of multiple connectomes across different scales of network topology, enabling the robust and reproducible discovery of hierarchical brain structures that vary in relation to phenotypic profiles. In addition to explaining the theoretical foundations and guarantees of our algorithms, we demonstrate their superiority over current state-of-the-art connectomics methods through extensive simulation studies and real-data experiments. Using a set of high-resolution connectomes obtained from genetically distinct mouse strains (including the BTBR mouse—a standard model of autism—and three behavioral wild-types), we illustrate how our methods successfully uncover latent information in multi-subject connectomics data and yield valuable insights into the connective correlates of neurological phenotypes that other methods do not capture. The data and code necessary to reproduce the analyses, simulations, and figures presented in this work are available at https://github.com/neurodata/MCC.

Recent Advances in Xenes Based FET for Biosensing Applications.

In recent years, monoelemental 2D materials (Xenes) such as graphene, graphdiyne, silicene, phosphorene, and tellurene, have gained significant traction in biosensing applications. Owing to their ultra-thin layered structure, exceptionally high specific surface area, unique surface electronic properties, excellent mechanical strength, flexibility, and other distinctive features, Xenes are recognized for their potential as materials with low detection limits, high speed, and exceptional flexibility in biosensing applications. In this review, the unique properties of Xenes, their synthesis, and recent theoretical and experimental advances in applications related to biosensing, including DNA/RNA biosensors, protein biosensors, small molecule biosensors, cell, and ion biosensors are comprehensively summarized. Finally, the challenges and prospects of this emerging field are discussed.