Search Results (4,649)

Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. Full article

This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor strategy used in Oticon Medical devices. The study also explores how the SFE can be enhanced into a Temporal Fine Structure (TFS)-based strategy named Spectral Event Extraction (SEE), combining spectral sharpness with temporal cues. Background/Objectives: Many CI recipients understand speech in quiet settings but struggle with music and complex environments, increasing cognitive effort. De-smearing the power spectrum and extracting spectral peak features can reduce this load. The SFE targets feature extraction from spectral peaks, while the SEE enhances TFS-based coding by tracking these features across frames. Methods: The SFE strategy extracts spectral peaks and models them with synthetic pure tone spectra characterized by instantaneous frequency, phase, energy, and peak resemblance. This deblurs input peaks by estimating their center frequency. In SEE, synthetic peaks are tracked across frames to yield reliable temporal cues (e.g., zero-crossings) aligned with stimulation pulses. Strategy characteristics are analyzed using electrodograms. Results: A flexible Frequency Allocation Map (FAM) can be applied to both SFE and SEE strategies without being limited by FFT bandwidth constraints. Electrodograms of Crystalis and SFE strategies showed that SFE reduces spectral blurring and provides detailed temporal information of harmonics in speech and music. Conclusions: SFE and SEE are expected to enhance speech understanding, lower listening effort, and improve temporal feature coding. These strategies could benefit CI users, especially in challenging acoustic environments. Full article

A novel unified progressive hybrid censoring is introduced to combine both progressive and hybrid censoring plans to allow flexible test termination either after a prespecified number of failures or at a fixed time. This work develops both frequentist and Bayesian inferential procedures for estimating the parameters, reliability, and hazard rates of the inverted Nadarajah–Haghighi lifespan model when a sample is produced from such a censoring plan. Maximum likelihood estimators are obtained through the Newton–Raphson iterative technique. The delta method, based on the Fisher information matrix, is utilized to build the asymptotic confidence intervals for each unknown quantity. In the Bayesian methodology, Markov chain Monte Carlo techniques with independent gamma priors are implemented to generate posterior summaries and credible intervals, addressing computational intractability through the Metropolis–-Hastings algorithm. Extensive Monte Carlo simulations compare the efficiency and utility of frequentist and Bayesian estimates across multiple censoring designs, highlighting the superiority of Bayesian inference using informative prior information. Two real-world applications utilizing rare minerals from gold and diamond durability studies are examined to demonstrate the adaptability of the proposed estimators to the analysis of rare events in precious materials science. By applying four different optimality criteria to multiple competing plans, an analysis of various progressive censoring strategies that yield the best performance is conducted. The proposed censoring framework is effectively applied to real-world datasets involving diamonds and gold, demonstrating its practical utility in modeling the reliability and failure behavior of rare and high-value minerals. Full article

The development of multifunctional conductive hydrogels with rapid self-healing capabilities and powerful sensing functions is crucial for advancing wearable electronics. This study designed and prepared a polyvinyl alcohol (PVA)–borax hydrogel incorporating carbon nanotubes (CNTs) and biomass carbon nanospheres (CNPs) as dual-carbon fillers. This hydrogel exhibits excellent conductivity, mechanical flexibility, and self-recovery properties. Serving as a highly sensitive piezoresistive sensor, it efficiently converts mechanical stimuli into reliable electrical signals. Sensing tests demonstrate that the CNT/CNP/PVA–borax hydrogel sensor possesses an extremely fast response time (88 ms) and rapid recovery time (88 ms), enabling the detection of subtle and rapid human motions. Furthermore, the hydrogel sensor also exhibits outstanding cyclic stability, maintaining stable signal output throughout continuous loading–unloading cycles exceeding 3200 repetitions. The hydrogel sensor’s characteristics, including rapid self-healing, fast-sensing response/recovery, and high fatigue resistance, make the CNT/CNP/PVA–borax conductive hydrogel an ideal choice for multifunctional wearable sensors. It successfully monitored various human motions. This study provides a promising strategy for high-performance self-healing sensing devices, suitable for next-generation wearable health monitoring and human–machine interaction systems. Full article

In an era where sustainable development is increasingly a core strategic issue for businesses, how top management, as the architects of corporate strategy, can achieve a synergy of economic, social, and environmental benefits through internal management mechanisms to promote corporate sustainability is a central focus for both academia and practice. This study aims to explore how Executive Cognitive Flexibility (CF) influences Firm Performance and to uncover the mediating effects of Non-market Strategy. We use panel data from Chinese A-share listed companies between 2016 and 2022 to examine and empirically analyze this mechanism. Our findings indicate that CF has a positive impact on Firm Performance. This relationship is realized through the pathway of Non-market Strategy, specifically manifesting as a reduction in Corporate Social Responsibility (CSR) and an increase in Corporate Political Activity (CPA). Further analysis reveals that the impact of executive cognitive flexibility on firm performance is differentially influenced by internal and external environmental contexts. The findings of this study provide important practical insights and policy recommendations for companies on cultivating executive cognitive flexibility, optimizing non-market strategies, and enhancing firm performance in various internal and external environments. Full article

With the rapid digital transformation of healthcare services in India, this study investigates the factors influencing the behavioral shift from traditional hospital visits to e-health consultations. The primary objective was to analyze patient attitudes, satisfaction, and perceived barriers to adopting virtual healthcare, especially in urban and semi-urban settings. Methods: The methodology adopted in the study was descriptive, and a convenience sampling technique was used for data collection because the feasible times of the patients’ availabilities were taken into consideration for data collection. Both primary and secondary data were collected using questionnaires and literature. A sample size of 385 participants was used in this study. Various statistical tools, such as frequency, ANOVA, and Chi-square tests, were used to test the hypotheses. Results: It was observed from ANOVA and Chi-square tests that the factors for switching from traditional consultation to e-health services have a positive association. It was found that integrating data through influencing factors significantly (p < 0.001) improved decisions on e-health services. Conclusion: This study highlights the shift from in-person to e-health consultations driven by convenience, flexibility, and pandemic-related needs while acknowledging barriers such as digital literacy, infrastructure gaps, and trust issues. It recommends strategies, such as secure platforms, training, and integrated care models, for a more inclusive digital health future. Full article

This research explores the concept of open building (OB) in the context of low-cost housing, focusing on its historical applications in Egypt during the 1980s. By evaluating past experiences, the study aims to extract key lessons that can inform the design and implementation of contemporary social housing projects. The goal is to foster resilience and diversity in housing typologies to ensure they align with the evolving needs of residents. To achieve these objectives, the research employed a multi-dimensional strategy, beginning with a comprehensive literature review of the open building movement (OB); then, the study traced the evolution of the OB movement in Egypt using a qualitative analysis approach, which involved analyzing its implementation in low-cost housing projects over the past four decades. Through this historical lens, the study identifies design principles and strategies that can enhance social housing projects by applying OB. Considering the life cycle cost, OB enables an incremental process that would align with users’ financial capacities. The research revealed the substantial capacity of open building (OB) to address Egypt’s social housing challenges, primarily by fostering user-driven flexibility in housing unit design and area selection. This empowers occupants to choose spaces perfectly suited to their family’s evolving needs. Moreover, the findings provide a roadmap for revitalizing the OB movement by analyzing and overcoming past implementation difficulties, consequently balancing the initial cost and long-term economics for citizens and significantly reducing the governmental sector’s expenditure. Full article

Electrochromic (EC) devices are gaining increasing attention for next-generation smart windows and low-power displays due to their reversible color modulation, low operating voltage, and flexible form factors. Recently, electrochromic energy storage devices (EESDs) have emerged as a promising class of multifunctional systems, enabling simultaneous energy storage and real-time visual monitoring. In this study, we report a flexible dual-functional EESD constructed using polyaniline (PANI) films doped with anthraquinone-1-sulfonic acid sodium salt (AQS), coupled with a redox-active PVA-based gel electrolyte also incorporating AQS. The incorporation of AQS into both the polymer matrix and the gel electrolyte introduces synergistic redox activity, facilitating bidirectional Faradaic reactions at the film–electrolyte interface and within the bulk gel phase. The resulting vertically aligned PANI-AQS nanoneedle films provide high surface area and efficient ion pathways, while the AQS-doped gel electrolyte contributes to enhanced ionic conductivity and electrochemical stability. The device exhibits rapid and reversible color switching from light green to deep black (within 2 s), along with a high areal capacitance of 194.2 mF·cm⁻² at 1 mA·cm⁻² and 72.1% capacitance retention over 5000 cycles—representing a 31.5% improvement over undoped systems. These results highlight the critical role of redox-functionalized gel electrolytes in enhancing both the energy storage and optical performance of EESDs, offering a scalable strategy for multifunctional, gel-based electrochemical systems in wearable and smart electronics. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework’s applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework’s adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Full article

Grid-forming (GFM) converters have been critical in DER-dominant power systems, ensuring stability, but their performance is highly sensitive to grid conditions such as system strength. Testing GFM converters under a wide range of grid strengths (from strong high-inertia systems to very weak grids) and fault scenarios is challenging, as traditional test facilities and static grid simulators have limitations. To address this problem, this paper proposes a converter-based power line emulator that provides a flexible, programmable grid environment for GFM converter testing. The emulator uses power electronic converters to mimic transmission line characteristics, allowing for the adjustment of effective grid strength (e.g., short-circuit ratio changes). The proposed approach is validated through detailed PSCAD simulations, demonstrating its ability to provide scalable weak-grid emulation and comprehensive validation of GFM converter control strategies and stability under various grid conditions. This research highlights that the converter-based emulator offers enhanced flexibility and cost-effectiveness over traditional testing setups, making it an effective tool for GFM converter performance test. Full article

Enterprise architecture (EA) provides a strategic foundation for aligning business processes, IT infrastructure, and organizational strategy, enabling firms to navigate uncertainty and complexity. In developing economies, small and medium-sized enterprises (SMEs) face significant challenges in maintaining financial resilience and sustainable growth amidst frequent disruptions. This study investigates how EA-driven change events affect SME financial performance by activating three key adaptive mechanisms: improvisational capability, flexible IT systems, and organizational culture. A novel classification of EA change triggers is proposed to guide adaptive responses. Using survey data from 291 Pakistani SMEs collected during the COVID-19 crisis, the study employs structural equation modeling (SEM) to validate the conceptual model. The results indicate that improvisational capability and flexible IT systems significantly enhance financial performance, while the mediating role of organizational culture is statistically insignificant. This study contributes to EA and sustainability literature by integrating a typology of EA triggers with adaptive capabilities theory and testing their effects in a real-world crisis context. Full article

This article investigates the institutional and informational foundations of water governance in Southern Europe amid escalating climate stress. Focusing on Portugal, Spain, Italy, and Greece, it develops a multi-level analytical framework to explore how information asymmetries and governance fragmentation undermine coordinated responses to water scarcity. Integrating theories of information economics, polycentric governance, and critical institutionalism, this study applies a stylized economic model and comparative institutional analysis to assess how agents—such as farmers, utilities, regulators, and civil society—respond to varying incentives, data access, and coordination structures. Using secondary data, normalized indicators, and scenario-based simulations, the model identifies three key structural parameters—institutional friction (θ_i), information cost (β_i), and incentive strength (α_i)—as levers for governance reform. The simulations are stylized and not empirically calibrated, serving as heuristic tools rather than predictive forecasts. The results show that isolated interventions yield limited improvements, while combined reforms significantly enhance both equity and effectiveness. Climate stress simulations further reveal stark differences in institutional resilience, with Greece and Italy showing systemic fragility and Portugal emerging as comparatively robust. This study contributes a flexible, policy-relevant tool for diagnosing governance capacity and informing reform strategies while also underscoring the need for integrated, equity-oriented approaches to adaptive water governance. Full article

The increasing complexity of cybersecurity risks highlights the critical need for novel teaching techniques that provide students with the necessary skills and information. Traditional on-premises laboratory setups frequently lack the scalability, flexibility, and accessibility necessary for efficient training in today’s dynamic world. This study compares the efficacy of cloud-based solutions—specifically, Amazon Web Services (AWS) Elastic Compute Cloud (EC2)—against traditional settings like VirtualBox, with the goal of determining their potential to improve cybersecurity education. The study conducts systematic experimentation to compare lab environments based on parameters such as lab completion time, CPU and RAM use, and ease of access. The results show that AWS EC2 outperforms VirtualBox by shortening lab completion times, optimizing resource usage, and providing more remote accessibility. Additionally, the cloud-based strategy provides scalable, cost-effective implementation via a pay-per-use model, serving a wide range of pedagogical needs. These findings show that incorporating cloud technology into cybersecurity curricula can lead to more efficient, adaptable, and inclusive learning experiences, thereby boosting pedagogical methods in the field. Full article

Polyaniline (PANI) based thermoelectric materials have attracted much attention in flexible energy harvesting devices due to their unique molecular structure, excellent chemical stability, and low cost. However, the intrinsic thermoelectric performance of intrinsic PANI makes it difficult to meet the needs of practical applications due to its low electronic transport properties. This review focuses on the preparation methods and key strategies for developing high-performance PANI-based thermoelectric materials. It aims to comprehensively update knowledge regarding synthesis methods, microstructures, thermoelectric properties, and underlying mechanisms. The overall goal is to provide timely insights to promote the development of high-performance PANI-based thermoelectric materials. Full article