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Deterministic, Stochastic, and Deep Learning Approaches to Understand the Economic Fluctuations in India
In the present work, a new mathematical framework is proposed for studying the interrelation among population growth rate, GDP, inflation rate, and unemployment rate within deterministic and stochastic frameworks. The values of the parameters of the proposed model are estimated using real data from India. The local and global uniqueness of solutions is established for the stochastic model. The deterministic model is solved by using the Adams-Bashforth-Moulton predictor-corrector method, and Milstein's method is used for solving the stochastic model. Numerical simulations correlated quite strongly with observed data, while projections for the 20242030 period indicate that controlled population growth bodes well for the outlook of the economy for India, supporting economic prosperity alongside reduced inflation and better employment conditions. The findings presented in this work are correlational; therefore, to find the possible cause for this phenomenon, further research is required with detailed datasets. Comparing our model's GDP predictions with that obtained using a long short-term memory recurrent neural network model returned very high values of predictive accuracy, thus reinforcing the strength and reliability of our framework. 2025 John Wiley & Sons Ltd. -
Exploring Revenge Travel Intentions to Offbeat Destinations: Assessing the Mediating and Moderating Mechanisms
This study integrates the Health Belief Model and Theory of Planned Behavior to examine factors influencing revenge travel intentions to offbeat destinations, including perceived susceptibility, perceived severity, perceived benefits, perceived barriers, attitude, social norms, and perceived behavioral control. It also explores the moderating role of social media influencer information search. Data from 363 online survey responses were analyzed using SmartPLS and structural equation modeling. Results show that constructs from both models significantly impact revenge travel intentions to offbeat destinations, with attitude and perceived behavioral control as partial mediators. Notably, social media information search strengthens the positive link between attitudes toward offbeat tourism and revenge travel intentions to offbeat destinations. The proposed model outperforms the original models in predictive capacity. The study acknowledges its limitations and discusses theoretical and practical implications for understanding revenge travel behavior to offbeat destinations. 2026 The Author(s). International Journal of Tourism Research published by John Wiley & Sons Ltd. -
Personal Cognitive Predictors Influencing Career Resilience Among Indian Women Information Technology Professionals
Career resilience, which is culturally and contextually determined, has been insufficiently explored in the literature regarding women, with an inadequate investigation into the factors predicting their resilience. This investigation offers fresh insights into the determinants of career resilience among women professionals in the Indian information technology sector by examining career-related personal cognitive factors. The study specifically explored the effects of career self-management skills, work volition, career salience, and occupational self-efficacy on career resilience (N = 306). Hierarchical multiple regression analysis revealed that career self-management skills, work volition, and occupational self-efficacy significantly predicted career resilience in women professionals. Additional analyses revealed occupational self-efficacy as a mediating factor. These findings contribute to formulating strategies to enhance career resilience through organizational support and targeted interventions. 2025 by the American Counseling Association. -
Transition in Kpen Climate Zones and Its Impacts on Hydroclimatic Extremes Across India
Shifting climatic zones across India are reshaping the country's hydroclimatic balance, with significant consequences for drought behaviour and water security. This study examines how spatial changes in KpenGeiger climate zones between two climatological periods (19611990 and 19912020) are influencing long-term drought characteristics. Using high-resolution gridded rainfall and temperature data from the India Meteorological Department, the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI) are used to assess drought intensity and extent across five major climate categories: tropical, arid, temperate, continental and polar. Results reveal a noticeable expansion of the arid zone by 3.86% and a contraction of the temperate zone by 6.94%, indicating a transition toward warmer and drier climates. These spatial shifts have altered regional drought behaviour, with formerly moderate zones experiencing more frequent and intense droughts. The arid and tropical zones, where expansion is observed, show increasing drought severity, largely driven by rising evapotranspiration due to temperature increases of 0.12C0.25C/decade (Tmax) and 0.10C0.20C/decade (Tmin). In contrast, regions where the temperate climate is receding are showing a loss of climatic buffering capacity against drought. SPEI captures more widespread and severe drought events than SPI, underscoring the increasing role of thermal stress in water balance anomalies. This study highlights that changes in the spatial extent of climate zones are a key driver of evolving drought patterns in India. Recognising these shifts is essential for improving temperature-sensitive drought monitoring and formulating zone-specific adaptation strategies in the face of accelerating climate change. 2026 Royal Meteorological Society. -
Executive Function Decline and Its Association With TNF-? in the Later Stages of Post-Acute Sequelae of COVID
Beyond the immediate impact of the COVID-19 pandemic, survivors often grapple with incapacitating post-infection symptoms, referred to as Post-Acute Sequelae of COVID (PASC) when persistent beyond 90 days. Cognitive manifestations, encompassing attention, memory, and executive functions (EF), collectively termed brain fog, contribute to functional challenges in PASC. This infection also elicits a long-lasting pro-inflammatory response that persists even after viral clearance, potentially correlated with brain fog. However, it is unclear whether pro-inflammatory responses and cognitive sequelae persist beyond 1 year after the onset of infection. Thus, this study sought to investigate the long-term consequences of PASC on EFs as well as a potential association with markers of inflammation. Forty individuals with PASC who passed performance validity testing (PVT) and 40 matched healthy controls (HC) underwent neuropsychological assessments, including the Montreal Cognitive Assessment to assess global cognition, Victoria Stroop Test to assess inhibitory control, Wisconsin Card Sorting Test to assess cognitive flexibility, Digit Span Task to assess working memory, and Mackworth Clock Test to assess sustained attention on the Psychology Experiment Building Language (PEBL) toolkit. Serum was assayed for tumor necrosis factor-? (TNF-?) and interleukin-10 (IL-10). Results indicate significant EF decline in PASC, inversely correlated with serum TNF-? concentrations, approximately 562 225 days after the onset of infection. Thus, there exists protracted EF decline in PASC, persistent even beyond 1 year after the onset of infection. Increased levels of TNF-? are observed to be associated with poorer executive functioning in PASC. 2025 Wiley Periodicals LLC. -
Navigating the Autism Journey: Parental Experiences, Barriers and the Role of Early Intervention in India
Parents of children with autism spectrum disorder (ASD) often encounter significant challenges in accessing timely diagnosis and appropriate support services. This study explores the experiences of parents navigating autism-related services in India, focusing on barriers to diagnosis, post-diagnosis support and the role of early intervention. Using a qualitative research design, we conducted focus group discussions with 11 parents of children with ASD and analysed the data using thematic analysis. Sentiment analysis and chi-square statistical testing were also applied to assess parental perspectives across key themes. The findings reveal systemic delays in diagnosis, limited public awareness and inconsistencies in service availability, which contribute to heightened parental stress. Parents expressed difficulties in implementing intervention strategies at home and reported challenges related to accessibility and affordability of professional support. Whereas some parents acknowledged the benefits of available services, many highlighted gaps in tailored, culturally appropriate interventions. Sentiment analysis showed a relatively even distribution of positive, neutral and negative sentiments across themes, indicating the complexity of parental experiences. This study underscores the need for a more structured and inclusive approach to ASD support, including digital tools, peer support networks and early screening programmes. Strengthening policy frameworks and expanding accessible interventions can enhance the effectiveness of autism services and improve outcomes for families. These findings contribute to the growing body of research advocating for parent-inclusive, culturally responsive autism support systems. 2025 International Society for Developmental Neuroscience. -
Digital Bridges: Harnessing Social Media for Social and Cultural Unity in Disaster Recovery
This manuscript investigates the role of social media as digital bridges which link individuals, groups, and organizations within a?community in post-disaster recoveries. By synthesizing research from disaster studies, social capital theory, and digital communication literature, the paper draws on empirical evidence on social media supporting situational awareness, community mobilization, digital storytelling, and cross-cultural?solidarity. Simultaneously, it also?examines the problems of fake news, social media apartheid, surveillance dystopia, and the fleeting nature of online solidarity. Instead of a general literature review, the article provides an integrative conceptual synthesis that?connects theory to policy and practice. It concludes with concrete suggestions for policymakers, platform designers, and recovery specialists who want to leverage social media's connecting?power while reducing its separating dangers. 2026 Policy Studies Organization. -
Associations Between Religious Coping and Anxiety Symptoms Among Emerging Adults in India: Religious Centrality as a Potential Moderator
Anxiety disorders are globally prevalent, with the highest disease burden in low- and middle-income countries. However, most research on protective factors of anxiety is predominantly conducted in high-income countries. Focusing on India, the most populous middle-income country where religion is salient, this study examined the association between religious coping and generalised anxiety symptoms and whether aspects of social identity moderated this association. A religiously and ethnolinguistically diverse sample of emerging adults (N = 484, Mage = 20.48 years) completed measures of religious coping, religious centrality and anxiety. Results indicated that negative religious coping was positively associated with anxiety symptoms, whilst positive religious coping was unrelated to anxiety. Religious centrality did not moderate the relation between religious coping and anxiety. However, ethnolinguistic identity (Northeastern vs. other regions) moderated the association, such that negative religious coping predicted higher concurrent anxiety among Indians from other regions, but not among Northeasterners. Findings support the role of negative religious coping in anxiety and suggest investigations into the role of ethnolinguistic identity as a critical contributing factor to mental health. 2026 The Author(s). International Journal of Psychology published by John Wiley & Sons Ltd on behalf of International Union of Psychological Science. -
Collaborative Processes in the Development of the International Competences for Undergraduate Psychology (ICUP) Model
Across all nations, undergraduate psychology programmes aim to promote the acquisition of foundational psychology competences. Yet, until recently, a universally recognised model outlining essential competences did not exist. The International Collaboration on Undergraduate Psychology Outcomes (ICUPO) addressed this gap by developing the International Competences for Undergraduate Psychology (ICUP) Model. The aim of this article is to provide guidance about how other groups might successfully approach similar efforts to delineate discipline-specific key competences. We describe the processes that led to the development of the ICUP Model, framed by group development theory (Preparing, Forming, Storming, Norming, and Performing Stages), with additional consideration of individual ICUPO Committee member psychological needs for competence, relatedness, and autonomy. Each group development Stage section (a) describes project activities relevant to the characteristics of that Stage, and (b) lists key strategies employed and lessons learned, as well as commentary on psychological needs. To further enhance the value of this endeavour, the Discussion includes (a) commentary on the strengths and limitations of these theories for understanding and enhancing the effectiveness of such project processes, and (b) actionable insights for educational leaders undertaking similar projects. 2025 The Author(s). International Journal of Psychology published by John Wiley & Sons Ltd on behalf of International Union of Psychological Science. -
Dual-Phase-Lag Bioheat Analysis of Non-Fourier Thermal Wave Propagation in Multilayer Ocular Tissues
This study presents an advanced analytical framework for predicting thermal wave propagation in a multilayer ocular structure using the dual-phase-lag (DPL) bioheat formulation. The results confirm that non-Fourier thermal transport mechanisms are essential for accurately capturing transient heat behavior in biological tissues, particularly under external thermal exposure. Compared with classical Fourier and LordShulman models, the DPL model predicts smoother temperature gradients and lower peak thermal loads, thereby providing more physiologically realistic temperature distributions. The model validity regime analysis demonstrates clear operational boundaries where classical diffusion-based formulations fail and non-Fourier effects dominate thermal response. Sensitivity analysis reveals that ambient temperature and evaporation primarily control anterior ocular thermal behavior, while tissue porosity and blood perfusion significantly influence deeper layers such as the retina and sclera. Transient thermal comparisons confirm that classical models overpredict early-time heating due to the absence of relaxation effects. Multi-parameter response surface and thermal safety mapping highlight strong nonlinear coupling between environmental and physiological transport mechanisms, enabling quantitative identification of safe exposure limits. Additionally, surrogate modeling demonstrates high prediction accuracy relative to full DPL solutions while significantly reducing computational cost, enabling real-time thermal prediction and parametric optimization. Overall, the proposed hybrid analyticalcomputational framework establishes a robust platform for ocular thermal safety assessment, biomedical treatment planning, and environmental exposure risk evaluation. The findings also provide a generalized foundation for studying non-Fourier heat transport in layered porous biological media and support the development of next-generation predictive thermal modeling tools. 2026 Wiley Periodicals LLC. -
Influence of Sinusoidal and Non-Sinusoidal Two-Frequency Gravity Modulation in Viscoelastic Fluids Driven by Triple Diffusivity
This study focuses on understanding the system's response to gravity modulation with two frequency components, characterized by both sinusoidal (sine wave) and non-sinusoidal (square, triangular, and sawtooth) waveforms, on three-component convection, considering a viscoelastic fluid modelled using an Oldroyd-B fluid. We apply the Venezian approach to evaluate the Rayleigh number, its corrected form, and the wave number by deriving a five-mode Lorenz model to investigate the onset of convection. A nonlinear analysis is conducted to investigate the dynamics of heat and mass transfer by solving an extended eight-mode Lorenz model, capturing higher order interactions. The onset of convection and the transport properties were observed to be influenced by combinations of sinusoidal and non-sinusoidal waveforms. This study optimizes convection-driven systems subjected to external periodic forcing by offering a more comprehensive understanding of convective instabilities in viscoelastic fluids. 2025 Wiley Periodicals LLC. -
Study of Kpers?Lortz Instability in a Weakly Electrically Conducting Couple-Stress Fluid
The study aims to investigate the Kpers?Lortz instability in rotating RayleighBard convection of a weakly electrically conducting couple-stress fluid. A novel aspect of this study is the incorporation of weakly electrically conducting couple-stress fluid in a rotating RayleighBard setup to analyze Kpers?Lortz instability and examine heat transfer in both primary and secondary regimes. The main goal is to understand how the combined effects of the couple-stress, rotation, and magnetic field alter stability thresholds and impact the heat transfer. KpersLortz instability (KLI) means the roll systems obtained during the regular convection get deformed and form an angle with each other, making the system unstable. The critical Rayleigh number for regular convection is obtained using linear stability analysis. A ninth-order Lorenz model is obtained using truncated Fourier expansions to study secondary instability. A weak magnetic field (Hartmann number) and couple-stress parameter hinders the onset-of-regular convection. We also obtain the critical values at which the KLI manifests. The critical values are found at a marginal steady state. The Hartmann number and couple-stress parameters hinder the onset-of-secondary instability. Further, the Nusselt number expression is derived, and it is observed that an increase in the couple-stress parameter and Hartmann number diminishes the heat transfer. Additionally, the Nusselt number is obtained for primary and secondary regimes, showing the impact of the parameters on the efficiency of heat transfer in each regime. To validate the results on secondary instability, the study compares its findings with existing literature in the absence of a weak magnetic field and couple-stress effects. A reasonably good agreement is observed, confirming the reliability of the results. 2025 Wiley Periodicals LLC. -
Investigating Salt-Finger Convection Under Time-Dependent Gravity Modulation in Micropolar Liquids
This paper investigates how gravity modulation affects salt-finger convection in a micropolar liquid layer confined between two parallel, infinitely long plates separated by a thin gap. The system is heated and has solute added from above. The study uses linear stability analysis to examine when and how salt-finger convection, driven by the salt-finger process, begins. To analyze this, the partial differential equations governing the system are solved numerically using normal mode analysis. The Venezian approach is applied to find the critical Rayleigh number and the solutal Rayleigh number, which are key to understanding the onset of convection. Also, the paper explores how different micropolar fluid parameterssuch as the coupling parameter, micropolar heat conduction parameter, couple stress parameter, and inertia parameteraffect the system when gravity modulation is present. It is found that gravity modulation can either stabilize or destabilize convection, depending on its frequency. At very high frequencies (approaching infinity), the effect of gravity modulation becomes minimal, having little impact on the convection process. The paper also examines the relationship between the critical Rayleigh number and the solutal Rayleigh number, which are related to heat and solute concentration, respectively. 2024 Wiley Periodicals LLC. -
Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset
Disasters significantly impact people's lives; among them, flooding is the worst common, and it causes sudden and secure damage to both lives and property. Addressing such real-time crisis demands intricate and sophisticated flood prediction models with enhanced capabilities. The development of efficient flood prediction models is often hindered by the lack of available datasets and the need for optimal feature. To address the challenge of data availability, in the proposed research, we have manually prepared a novel dataset by collecting data from NASA's (National Aeronautics and Space Administration) Power Project. The proposed dataset is experimentally evaluated and verified and has been organized into a balanced benchmark dataset with 33 features using the SMOTE algorithm. To enhance the provenance of flood prediction model, we propose a novel feature selection method. This method integrates outcomes from three different feature selection techniques to identify the most prominent features. The proposed feature selection method improves the model's performance and efficiency by identifying optimal predictors. Experimental results demonstrate that the artificial neural network trained with the selected relevant features accurately predicts flood occurrences, showing enhanced accuracy compared to state-of-the-art methods. 2026 John Wiley & Sons Ltd. -
Pratixa: A Cognitive Framework for Behavioral Decision-Making and Its Mathematical Formalization
The present study introduces pratixa, an internal cognitive structure that functions as a reference architecture guiding human decision-making. Pratixa is a dynamic, event-sensitive archive of anticipated outcomes of behavior, learned event-behavior-outcome associations, and adaptive behavioral responses, drawing on the theories from decision science, psychology, and behavioral adaptation. Past experiences shape pratixa, and iterative learning reinforces it. It supports predictive mental representations by enabling individuals to anticipate the outcomes of their own behavioral responses and adjust those responses when discrepancies arise between anticipated and actual outcomes. Pratixa supports anticipatory learning and real-time correction, making it a future-oriented cognitive structure for decision making. It matures in a spiral progression, from null pratixa, where no prior event-behavior-outcome associations exist, through quixotic pratixa, characterized by illusory or arbitrary associations, to realistic pratixa, where causal relationships are adequately approximated. This spiral maturation reflects how individuals adapt through experiential learning and reinforcement, transitioning from effortful reasoning to increasingly automatic and context-sensitive decision-making. By positioning decision-making within this evolving structure, pratixa offers a distinct perspective on predictive cognition in complex and ambiguous contexts, with implications for strategic foresight, behavioral economics, and adaptive behavioral decision making. The study also proposes a mathematical formulation to represent how this reference architecture evolves through reinforcement-based learning and guides decision-making, providing a computational basis for modeling human foresight and adaptation. 2025 John Wiley & Sons Ltd. -
Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm
The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability of the current network, especially in Internet of Things (IoT) and cloud environments. Traditional detection methods often struggle with the inability to achieve a balance between detection accuracy and computational efficiency. In this manuscript, the Classification of Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized with Gazelle Optimization Algorithm (DDOS-AD-BWRF-GOA) is proposed. First, the raw data is gathered from the CICDDoS2019 dataset. Then, input data are preprocessed utilizing Adaptive Bitonic Filtering for normalizing the values. The preprocessed data are fed to the Improved Feed Forward Long Short-Term Memory technique for selecting features that increase the model's execution time. The selected features are supplied to the Bayesian Weighted Random Forest (BWRF), which classifies the multiclass DDOS attack. In general, Bayesian Weighted Random Forest does not adopt any optimization methods to define optimal parameters to guarantee exact DDOS identification. Hence, GOA is proposed to optimize the Bayesian Weighted Random Forest classifier. The proposed method is implemented in MATLAB. The performance metrics, such as Accuracy, Precision, Recall, F1-score, Specificity, Error rate, and Computational time are evaluated. The proposed method attains 15.34%, 24.1%, and 18.9% higher accuracy and 12.4%, 18.24%, and 22.6% higher precision when analyzed with existing techniques: Hybrid deep learning method for DDOS detection and classification (HDL-DDOS-DC), Edge-HetIoT Defense against DDoS attack utilizing learning techniques (EHD-DDOS-LT), and Digital twin-enabled intelligent DDOS detection for autonomous core networks (DTI-DDOS-ACN), respectively. 2025 John Wiley & Sons Ltd. -
Effect of functionalization on the energy storage performance of super capacitors derived from wood charcoal
The electrochemical performance of wood charcoal is investigated with respect to the disorders in the system after subjecting to oxidation and exfoliation conditions. The Cyclic voltammetry and galvanostatic charge discharge curves indicate an improvement in the electrochemical behavior, resulting in a marginal increase in the specific capacitance values at higher exfoliation temperatures. The improvement is predominantly due to the change in the structural disorder in the system accompanied by the incorporation of oxygen functional groups which act as electrochemical active species. The exfoliation of wood charcoal at 160 and 200C yield a specific capacitance of 6.23 and 12.24 F/g at a current density of 0.01 A/g. The ESR values representing the overall resistance of the system are observed to be 6.07 ? for 200C as opposed to 10.41 ? of the bare material, making the material more conducting. The drastic change in the structural morphology along with the optimal amount of oxygen functional groups can be the reason for this behavior. The acquired results offer useful information for investigating the possibilities of fabricating supercapacitors with wood charcoal by tuning the defects of the system. 2024 American Institute of Chemical Engineers. -
NiSe2@CdO Nanocomposite: A Next-Generation Electrode for Asymmetric Supercapacitors with Gel Electrolyte
This article investigates the electrochemical performance of NiSe2@CdO nanocomposites synthesized by combining melt-diffusion-synthesized NiSe2 and hydrothermally prepared CdO, followed by ball milling to obtain the final NiSe2@CdO composite. Structural, morphological, and electrochemical analyses revealed flake-like NiSe2 nanoparticles decorated with rod-shaped CdO nanostructures, exhibiting exceptional electrochemical performance. The nanocomposite electrode achieved a specific capacitance of 255 F/g at 10 mV/s from the three-electrode setup, and also, was achieved at an energy density of 48 Wh/kg at the power density 2000 W/kg by the NiSe2@CdO||AC asymmetric device. fourier transform infrared analysis confirmed the structural integrity, while transmission electron microscopy images revealed nanostructures with clear lattice fringes, and energy-dispersive X-ray Spectroscopy verified elemental uniformity. The device demonstrated 96.7% capacitance retention even after 5000 cycles and displayed superior energy and power density characteristics in the Ragone plot. These results in turn, highlight the potential of NiSe2@CdO nanocomposites for next-generation energy storage systems. 2025 Wiley-VCH GmbH. -
Air Jet Erosion Behavior of FDM-Printed PLA Composites Reinforced With Steel Powder Fillers
This paper reports the air jet erosion behavior of FDM-printed polylactic acid (PLA) composites reinforced with 5 wt% and 10 wt% steel powder for solving the problem of the development of durable, sustainable, and high-performance materials for engineering applications. Test specimens were fabricated by fused deposition method with uniform dispersion of steel particles based on a twin-screw extrusion and were tested using ASTM G76 air jet erosion with angular Al2O3 particles as erodent at impact angles of 30, 60, and 90. For the material loss, pure PLA showed the maximum material loss, while steel filled composite showed significantly reduced erosion (2.38% and 14.29%, 8.16% and 18.37%, and 16.07% and 26% at 30, 60 and 90, respectively) and showed the durability of the materials and their material effective utilization. The presence of embedded steel particles was verified by SEM and confocal microscopy showing that the embedded steel particles really acted as crack stoppers, diverted the crack propagation, minimized plowing and crater formation, and improved the toughness, thus extended the potential service life and supported resource-efficient engineering solutions. Among all the compositions, the 10 wt% composite showed a better erosion resistance and the smoothest post-erosion surface owing to a higher particle density with efficiency of stress transfer. Overall, steel reinforcement significantly enhanced the erosion resistance, especially in normal impact conditions and confirmed steel-filled PLA as a suitable material for components in harsh erosive environments. 2026 The Author(s). Engineering Reports published by John Wiley & Sons Ltd. -
Adaptive Hybrid Multi-Objective Evolutionary Algorithm for Wireless Sensor Network Optimization: A Comprehensive Framework Integrating Opposition-Based Learning and Levy Flight Strategies
Wireless sensor networks constitute a foundational technology for ubiquitous monitoring and data acquisition across diverse application domains ranging from environmental surveillance to critical infrastructure management. The operational efficacy and longevity of these networks critically depend on strategic configuration of multiple design parameters including field coverage, sensors per cluster in-charge, sensor out-of-range error, overlaps per cluster in-charge, and network energy consumption. These objectives exhibit inherent trade-offs, rendering the optimization problem a complex multi-objective challenge characterized by conflicting criteria and high-dimensional search spaces. This research presents a novel adaptive hybrid multi-objective evolutionary algorithm that synergistically integrates opposition-based learning for enhanced population diversity and initialization, Levy Flight mutation for effective escape from local optima, and adaptive operator selection for dynamic adjustment of genetic operator probabilities. We conducted exhaustive empirical evaluation comprising independent runs with individuals evolved over multiple generations, benchmarking the proposed algorithm against three state-of-the-art approaches. Performance metrics were computed using global normalization with respect to theoretical problem bounds to ensure measurement validity and cross-algorithm comparability. Statistical analysis including non-parametric rank tests, pairwise comparisons, and effect size quantification confirm the proposed algorithm achieves statistically significant improvements with very large practical significance. The algorithm demonstrates superior convergence characteristics, solution diversity, and Pareto front quality, establishing a robust framework for automated wireless sensor network configuration in resource-constrained environments. 2026 The Author(s). Engineering Reports published by John Wiley & Sons Ltd.