Abstract Heartbeat sound classification plays a crucial role in the early detection of cardiovascular abnormalities. In this study, a novel framework, CRCapsNet, that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Capsule Networks to enhance classification accuracy and robustness is proposed. Analysis of the proposed model is performed on two datasets: dataset 1, with 832 audio samples in WAV format, and dataset 2, with 3240 heart sound recordings. The pre-processing techniques, including noise addition, time shifts, time stretching, and pitch shifts, are applied to the datasets, and Mel-Frequency Cepstral Coefficients (MFCC) are employed for feature extraction. The spectrograms are passed through a CNN with four convolutional blocks for spatial feature extraction, followed by an RNN module to capture temporal patterns in the heartbeat sequences. A Capsule Network is further incorporated to retain hierarchical relationships that are typically lost in traditional max-pooling operations. The achieved classification accuracies are 88.5% for the CNN, 98.67% for RNN, and an impressive 99.32% and 99.64% for the proposed CRCapsNet model on dataset 1 and dataset 2, respectively, demonstrating its superior performance in heartbeat sound classification. This research underscores the significance of heartbeat sound classification in augmenting traditional diagnostic practices and highlights the role of advanced computational techniques in healthcare innovation. Future directions include exploring multimodal integration and real-time clinical deployment.

Workplace safety remains a critical concern across industries, with self-reported and unreported work injuries posing significant risks to employee well-being and organizational sustainability. This study examines the role of moral disengagement as a mediating factor in the relationship between psychological safety climate and both self-reported and unreported work injuries. Drawing on social cognitive theory, this study argues that a strong psychological safety climate discourages moral disengagement, which, in turn, influences employees’ likelihood of reporting or concealing workplace injuries. The study also considers the specific reporting practices found in Pakistani industrial settings, where safety procedures are often informal and shaped by hierarchical relations. Data was collected from 303 employees working in large-scale industries in Pakistan, including the woolen, textile, shoe, sugar, and seed sectors. The analysis was conducted using partial least squares structural equation modeling (PLS-SEM). The psychological safety climate was negatively associated with moral disengagement, whereas moral disengagement had an inverse relationship with self-reported work injuries and a positive relationship with self-underreported work injuries. Moreover, moral disengagement mediates the relationship between psychological safety climate and workplace injuries. The model explained .078 variance ( R 2 ) in MD, .394 in SR, and .357 in SU. Similarly, the effect size f 2 was 0.163 for MD, 0.025 for SR, and 0.027 for SU. This study contributes to the workplace safety literature by integrating moral disengagement as an intervening mechanism between psychological safety climate and workplace injuries. The contribution is context-specific, offering insight into how cognitive mechanisms operate in Pakistani manufacturing environments where reporting norms are often informal. The findings emphasize the need for organizations to cultivate a robust psychological safety climate to minimize moral disengagement and encourage accurate injury reporting. The theoretical and practical implication of the study shows that by fostering psychological safety and ethical practices, workplace injuries are reduced, and firms are urged to make safety a core priority, especially in high-risk settings. Limitations and future research directions are also discussed. The study also acknowledges limitations such as purposive sampling, cross-sectional design, and reliance on self-reports, which can be addressed in future research. Limitations and future research directions are also discussed.

Decision making processes in the modern world are characterized by dynamics as far as evaluation criteria, relevant information, and human judgment itself change over time. Classical fuzzy soft set approaches, even when extended to the time series, suffer from inflexibility due to the fixed structure of parameters. In order to solve this problem, the novel approach, called the Dynamic Fuzzy Soft Set (DFSS) concept, is formulated in this paper. In contrast to previous approaches, DFSS allows parameters to be added, removed or to change their status in terms of importance. This makes the DFSS approach more applicable to longitudinal decision making processes and to situations when the structure of parameters evolves. The framework is elaborated within the rigorous mathematical formulation including definition of basic DFSS constructs, corresponding algebraic operations, and structural properties compatible with classical fuzzy soft set theory. Relations and mappings are defined for dynamic fuzzy soft structures as well. An important property of DFSS framework distinguishing it from other approaches is a proper treatment of temporal evaluation and temporal aggregation without structural biases. Examples illustrating differences in the behavior of DFSS framework compared to temporal fuzzy soft approaches are provided together with a case study demonstrating application of the proposed approach to educational decision making process.

The application of Industry 4.0 technologies specifically the internet of things (IoT) and big data (BD) have changed the global industrial system. Many emerging economies are increasingly investing in Industry 4.0 technologies but these economies have not achieved the expected benefits due to insufficient digital infrastructure, financial liquidity and manpower inadequacy. This study reveals the impact of BD and IoT on operational performance (OP) in manufacturing context, with the mediating role of employee training (ET). Drawing upon the resource-based view (RBV) theory, the research examines how the strategic incorporation of technological abilities and human capital leads to improve productivity, quality, flexibility, and cost efficiency in manufacturing sector. A quantitative method was used to analyse survey data collected from 320 managers and executives representing manufacturing firms engaged in Industry 4.0 implementation. To analyse the relationship among variables statistical analyses were performed using SPSS 26 and Smart PLS 4. The findings indicate that BD and IoT have a positive and significant impact on OP, while ET further strengthens these effects by establishing the connection between technological aspects and performance outcomes. The results underscore the importance of ongoing employee training and digital innovation endeavours. This study recommends valuable information for practitioners and policymakers to enhance sustainable competitiveness through advanced technology and human resource abilities. Empirically, this research is among the first to study the impact of Industry 4.0 adoption with employee training in developing economies, providing valuable insights for both researchers and practitioners.

Heart disease is rapidly increasing. For early and accurate prediction, advanced machine learning (ML) models are needed. However, the sensitive nature of clinical data, along with strict regulatory constraints, creates significant challenges to traditional centralized learning systems. Federated learning (FL) has emerged as a decentralized paradigm for collaborative model training, enabling multiple clients to jointly learn a global model without exchanging raw data. However, various existing FL frameworks rely on single-model architectures. They struggle to capture complex feature interactions in tabular healthcare data. To address these limitations, we propose a novel DP-FedHybrid, differentially private federated stacking framework that integrates heterogeneous models within a secure and decentralized learning architecture. We use a multi-client FL setup, where each client independently trains CatBoost and Transformer models in parallel, and their predictive outputs are combined through a stacking-based meta-learning mechanism. The Transformer component is optimized using differentially private stochastic gradient descent (DP-SGD), incorporating gradient clipping and calibrated Gaussian noise injection to ensure formal privacy guarantees. To further strengthen, we use a stacking-based meta-learning layer that aggregates probabilistic outputs from client-side models. It enables effective knowledge fusion and enhances robustness and generalization under non-independent and identically distributed (non-IID) data. The proposed framework is evaluated on a benchmark heart disease dataset, where we obtained an accuracy of 95.12% under standard FL and 94.15% under DP constraints, outperforming closely related works. The proposed work advances the existing literature by providing a scalable, hybrid, and privacy-preserving FL paradigm for heart disease prediction.

Abstract The concept of emergence has great importance in physical phenomena as it highlights how local rules can generate global behavior and reveal optimal choices hidden within isolated data sets. Emergent integration of heterogeneous information sources remains a fundamental challenge in computational systems, particularly when uncertainty, hierarchy, and partial ordering must be preserved simultaneously. Whereas fuzzy soft set theory offers a convenient structure on which to model uncertainty, the current methods do not have an organized mechanism to combine multiple partially ordered fuzzy soft domains, with consistency in order and hierarchical readability. This limitation restricts the development of scalable and transparent multi-domain decision systems. This paper proposes an emergence model of partially ordered fuzzy soft sets using geometry. The framework is a systematic analysis of symmetric geometries, non-symmetric but identical-layered geometries, and non-identical layered structures. A formally defined layer-contraction process with blended nodes is presented to build balanced representations with component-wise order relations and fuzzy membership semantics, in the case of heterogeneous configurations. The process of emergence is characterized on the basis of layer-wise aggregation and order-preserving fusion. The proposed model is applied to descriptor-centric message filtration by integrating three ordered fuzzy soft domains, which are extracted features, threat indicators, and signal descriptors. Empirical validation on SMS Spam Collection data demonstrates that the hybrid emergence model achieves competitive predictive accuracy with respect to baseline models (Rule-Based Filtering, Logistic Regression, Support Vector Machine, Random Forest, Neural Network, Fuzzy Soft Emergence) while retaining structural interpretability. The proposed model achieved the highest accuracy (0.933), the strongest ROC-AUC (0.958), an $$F_1$$ score of 0.777, and a substantially reduced False Positive Rate (FPR = 0.059) compared to baseline models. This contribution presents a mathematically consistent and computationally feasible framework for structured multi-domain integration into fuzzy, soft-based intelligent systems.

The pharmaceutical industry has a strong connection with human health and wellness and is thus considered a very important part of the nation’s economy and public welfare. Being an integral part of the supply chain of drugs, pharmaceutical firms face several risks that could hinder their ability to deliver medicine effectively. The disruption could have effects on the quality, amount, and timely availability of the products to the intended recipients. Even though there could be the continuation of the operation of the supply chain of pharmaceuticals without taking note of these risks, the success, sustainability, and resilience of the supply chain would be greatly affected. Thus, it is very important to determine some of the major risks to improve the performance of the supply chain. Based on some of the challenges in this regard, this research is aimed at determining those risks that cause disruption in the pharmaceutical supply chain. Initially, potential risks were established through a literature review and further prioritized on the basis of expert opinion. This research involved the application of Fuzzy Total Interpretive Structural Modelling and MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) for the structural analysis of the risks. Further, MoSCoW and rank sum techniques have been used for the prioritization of the risks identified. The study reveals that, ”unpredictable trade barriers” is the most significant risk, while ”limited suppliers” follows next among 20 risks. Additionally, this study is also a contribution to the existing body of literature as it has discovered four more risks.

A novel technique is developed for nonlinear optimization problem which is convex, separable and having multiple objective functions. In the development of the model all the objectives and the constraints of the multi objective model are linearly approximated over suitable intervals. The linear approximations are then aggregated to account for the original problem. The developed technique has been utilized for portfolio optimization problem. Firstly, the minimum variance model has been formulated and solved with machine leaning techniques. Secondly, the risk aversion model has been formulated and solved. The results obtained are combined into a multi objective framework of convex separable programming problem. All the three problems have been solved with the help of the XGBoost, neural network, and decision forest regression models. The renowned Python machine libraries of scikit-learn and keras have been utilized. The results identified portfolios that can return more financial benefits to the investors while investing in the capital market. The results of the proposed MOCSP approach are 22.5% improved in case of risk aversion model. Additionally, 17% improvement has been recorded in case of the minimum risk model. The MAE and RMSE for both XGBoost and decision forest regression have a frail value 0.0001. MAE and RMSE for the neural network regression have been recorded 1% and 2%, respectively. Both Accuracy and F1 score for XGBoost are 91%, for neural network regression are 98%, and for decision forest are 92%, respectively.

The integration of nanofillers such as graphene into polylactic acid (PLA) has attracted significant attention for enhancing the performance of fused filament fabricated (FFF) components. Despite these advances, the electromechanical properties of PLA–graphene composites remain insufficient for many functional applications, particularly in electronics and other industrial sectors. To address this limitation, the present study investigates how a broad range of fused filament fabrication (FFF) process parameters, including print orientation, raster angle, infill pattern, layer height, infill density, and printing speed, collectively influence the electromechanical performance of PLA-graphene composites. Using a Taguchi L18 orthogonal array, the study systematically evaluates and optimizes tensile, flexural, impact, and electrical conductance responses. The results demonstrate that a cubic infill pattern yields superior mechanical and conductive properties. At the same time, flat printing orientation enhances tensile and impact strength, and on-edge orientation improves impact strength and conductance. Optimal infill densities of 70%, 80%, and 90% were identified for maximizing tensile, flexural, and impact strengths, respectively. The optimized specimen achieved a tensile strength of 51.91 MPa, flexural strength of 62.6 MPa, impact strength of 59.38 J/m, and conductance of 19.12 μS. Grey relational analysis further identified the most effective parameter combination for simultaneous multi-response optimization. Overall, this research provides a comprehensive understanding of process–property relationships in PLA–graphene composites and establishes a pathway for fabricating multifunctional FFF parts with improved electromechanical performance.

This research examines how agile project management affects economic outcomes, with agile leadership and strategic agility serving as mediators. Data were collected using a time-lagged approach (T1 = 304; T2 = 236) from managers at five major IT and telecommunications companies via a questionnaire, yielding 236 usable responses for analysis. The study uses structural equation modeling (Smart-PLS) to investigate these relationships. The findings suggest that agile leadership and strategic agility serve as mediators between agile project management and economic sustainability. This work contributes to the field by empirically examining the balance between control and flexibility in agile project management. It also expands the definition of strategic agility to include the ability to switch business partners and broadens agile leadership to encompass coordination between partners. The findings suggest that while agile leadership enhances customer value creation, strategic agility is crucial for achieving economic sustainability.

Circular Supply Chains (CSCs) are widely touted as the operational nexus for achieving both Responsible Production (SDG 12) and Climate Action (SDG 13). However, this alignment relies on the often-unverified assumption that material recovery is inherently carbon-efficient. This study challenges that axiom by investigating the operational conditions under which circularity strategies inadvertently undermine climate mitigation goals. Methodology:: We develop a closed-loop hybrid-manufacturing–remanufacturing model that integrates reverse logistics topology, carbon intensity, and emissions taxation. Using classical non-linear optimization, we derive closed-form analytical solutions and conduct sensitivity analysis. Findings:: The analysis identifies a structural “Distance Paradox”: while increasing remanufacturing intensity linearly advances SDG 12, it introduces a convex emission penalty via reverse logistics that can actively negate SDG 13. Our model determines the spatial threshold beyond which the marginal carbon cost of recovery exceeds the emissions avoided from virgin production, creating a ”tipping point” where circularity becomes net-carbon positive. Originality:: This is the first analytical framework to quantify the tripartite trade-offs among SDG 9, 12, and 13. It contributes to the sustainable-operations literature by offering a decision-support tool that decouples resource recovery from carbon generation, ensuring circular strategies are logistically coherent. Practical implications:: The findings provide a corrective to “blind circularity,” demonstrating that firms must co-optimize logistics infrastructure and recovery rates to avoid environmental trade-offs. We propose that managers prioritize “spatial clustering” for recovery and that policymakers shift from weight-based to carbon-adjusted Extended Producer Responsibility (EPR) frameworks.

Modern decision systems increasingly require uncertainty models that are both parameter-dependent and temporally adaptive. Although soft set theory provides a versatile means of working with imprecise data, classical soft sets are static and unable to reflect changes in time or context. Recent development of the dynamic soft sets has provided a route through which uncertainty in adaptive settings can be modeled, but any single algebraic matrix framework has not been developed yet. This research paper forms an extensive theory of the dynamic soft set based on time or context-indexed matrix form and operator-based aggregation ( max , min , avg ) using frequency matrix decision methods. We demonstrate closure, commutativity, and equilibrium theorems of dynamic soft matrix operations, defining strict structural stability with respect to time or context evolution. Computational experiments prove that soft matrix consistency values ( ϵ≤ 0.2 ) conserve more than 95

Agile project management (APM) has become a dominant approach for managing uncertainty in dynamic industries. Yet the mechanisms through which project-level agility is associated with broader strategic adaptation and sustainability-oriented outcomes remain insufficiently explained. Drawing on dynamic capability theory (DCT) and organizational information processing theory (OIPT), this study develops and tests a time-ordered model in which APM is positively associated with strategic agility (SA), which, in turn, is associated with the three distinct dimensions of sustainable performance: economic, social, and environmental. In addition, agile leadership (AL) is theorized as a potential boundary condition that may strengthen the APM-SA relationship by enabling faster, higher-quality information processing and decision execution in uncertain contexts. To address common concerns regarding temporal precedence and common method bias in agility research, the study adopts a three-wave time-lagged design, collecting data at three points in time: APM and AL at Time 1, SA at Time 2, and the three performance dimensions at Time 3. The structural model is tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that APM positively predicts SA, SA is positively associated with all three performance outcomes, and SA mediates the relationship between APM and sustainable performance. However, AL does not significantly moderate the APM-SA relationship. Integrating DCT and OIPT, this study provides evidence that project-level agile routines may contribute to SA, which is subsequently associated with economic, social, and environmental performance.

Internet of Things (IoT)-based devices are extensively utilized for data transmission to the cloud across various organizations. Nonetheless, there are notable limitations in the conventional approach, such as in critical situations, where transmitting sensitive data, secure communication across the cloud is not guaranteed, and the memory, processing power, and bandwidth constraints in these IoT devices present significant challenges. The suggested model employs a bespoke Convolutional Neural Network (CNN) to categorize sensitive and non-sensitive images on the device, utilizes Elliptic Curve Cryptography (ECC) for safe session key sharing, implements SHA-512 hashing for integrity verification, and applies the ChaCha20 stream cipher for rapid, random encryption for sensitive images. The mean entropy of the proposed technique is 7.9976, and the correlation coefficients approximate zero. The histogram distributions are balanced, rendering statistical attacks exceedingly difficult. This approach surpasses AES + RSA-1024, SPECK, and PRESENT by reducing the average encryption time by up to 99

In modern cybersecurity settings, prioritizing massive streams of incoming network traffic is challenging due to uncertainty and inherent hierarchical dependencies among features and threat indicators. Although intuitionistic fuzzy soft sets (IFSS) offer a powerful framework for handling uncertainty, existing approaches lack effective mechanisms for comparing structured domains while preserving order relations, which may lead to reduced consistency and interpretability of prioritization outcomes. To address this gap, this work presents an order-preserving structural framework for message prioritization in the IFSS paradigm. The study incorporates three key components: (i) a similarity mapping mechanism for comparing ordered IFSS structures with same-level and compatible-level structural comparisons, (ii) the notion of order-isomorphism to capture structural equivalence between domains, and (iii) a topological sorting–based approach to identify the optimal feature–threat correspondences while maintaining hierarchical dependencies. An integrated prioritization pipeline is designed and experimentally validated on the NSL-KDD dataset. Experimental results demonstrate that the proposed approach achieves competitive and balanced performance, attaining the highest test accuracy (0.8365) and F1-score (0.8383), along with improved recall (0.7444), which is important for reducing undetected attacks. The main empirical advantage is reflected in the improved detection of attack instances and the reduced number of false negatives, while statistical significance testing, sensitivity analysis, and perturbation analysis further support the robustness and reliability of the proposed prioritization mechanism. These findings highlight that the integration of order-preserving similarity, structural equivalence, and topological reasoning within IFSS leads to more reliable, consistent, and interpretable prioritization, thereby supporting its relevance in practical applications and decision-making scenarios.

Abstract Understanding bipolar information is crucial as it enables individuals to make informed decisions that consider both extremes of a spectrum, leading to more balanced and effective outcomes. Interval-valued bipolar fuzzy set (IVBFS) has already been introduced in the literature as a great decision-making tool that can capture interval-valued bipolar information to properly address uncertainty. In this article, we introduce a hybrid of Interval-valued bipolar fuzzy set (IVBFS) and bipolar hypersoft sets (BHSS) called interval-valued bipolar fuzzy hypersoft set $$(IVBF_{HSS})$$ , which merges the capabilities of IVBFS and BHSS. The rationale behind the design of the presented data structure is to manipulate and process information in decision-making scenarios when the data is bipolar, has multiple attributes that need to be addressed up to a sub-attributive level to get a proper representation of the data provided, and needs to be presented in the form of intervals. In $$(IVBF_{HSS})$$ , two hyper soft sets (HSSs) are used, one providing positive interval-valued membership information and the other providing negative interval-valued membership information. We outline the essential features and basic operations of $$(IVBF_{HSS})$$ in this paper, examining its commutative, associative, distributive, and De Morgan laws to ensure a comprehensive analysis. To demonstrate the significance of $$(IVBF_{HSS})$$ , we develop a preferential decision support algorithm for selecting the best alternative in e-learning, such as identifying the most suitable instructional method, which can effectively be formulated as a Multi-Attribute Decision-Making (MADM) problem. This approach allows for the systematic evaluation of various alternatives based on multiple parameters and sub-parameters, enabling a rational and well-informed decision. This algorithm helps select the best alternative from a given set of options, leveraging the versatile nature of $$(IVBF_{HSS})$$ . The presented study conducts both computation-based and structural comparisons to evaluate the adaptability and reliability of the proposed framework.

Closed-loop supply chains (CLSC) have become pivotal in advancing sustainability within industries by encompassing the entire lifecycle of products, from production to recycling or remanufacturing. This study examines the role of trade-in programs in CLSC, where customers are incentivized to return used products in exchange for value. These programs are vital for minimizing waste and reducing production costs while enhancing corporate sustainability initiatives. However, challenges persist, particularly in setting optimal pricing strategies for remanufactured products. The study contributes to highlighting the critical role of acquisition cost, i.e., payments made to customers for returning products, influencing the demand and profitability of trade-in programs. A numerical and sensitivity analysis is conducted to explore the impact of varying acquisition costs on overall supply chain performance. The findings offer valuable insights for industries aiming to balance economic viability with environmental responsibility.

This study investigates how firms’ economic performance (EP) influences social performance (SP) and environmental performance (EnP) through the mediating role of green image (GI) in Pakistan’s manufacturing sector, offering critical insights for achieving the UN Sustainable Development Goals (SDGs) in developing economies. While prior research predominantly explores how sustainability initiatives affect profitability, this work reverses the lens, examining whether economic strength enables socio-environmental investments. Grounded in the Natural Resource-Based View and dynamic capability theory, we posit that EP fosters GI, which subsequently drives SP and EnP. Data from 212 manufacturing managers, analyzed via partial least squares structural equation modeling (PLS-SEM), confirm that EP significantly strengthens GI, which in turn enhances EnP and SP. Direct positive effects of EP on EnP and SP are also observed. GI partially mediates these relationships, underscoring its strategic role in translating economic success into sustainability outcomes. The study contributes by reframing GI as a capability that bridges economic and sustainability goals, offering practical insights for firms in developing economies to align profit motives with socio-environmental responsibilities. Our findings extend the existing literature by confirming that EP fosters sustainability practices, particularly in developing countries.

BackgroundIndustrial emissions in Karachi contribute to poor ambient air quality and may adversely affect nearby residents’ respiratory health. This study assessed whether residential distance from industrial zones is associated with respiratory symptoms and chronic respiratory disease.MethodsWe conducted a comparative, community-based cross-sectional survey (March–August 2024) of 462 adults sampled equally around three industrial zones S. I. T. E., Korangi, and Landhi (n = 154 each). Residential distance to the nearest industrial zone was classified as within 5 km vs. more than 5 km (distance estimated from mapped home addresses). Respiratory outcomes (symptoms; chronic bronchitis; asthma) were obtained via a validated questionnaire, and multivariable logistic regression estimated adjusted associations. This was a cross-sectional, community-based study using self-reported data without clinical assessments.ResultsCompared with residents living more than 5 km away, those living within 5 km reported substantially higher prevalences of cough, phlegm, wheeze, and dyspnea. Living more than 5 km from an industrial zone was independently associated with markedly lower odds of chronic bronchitis (adjusted OR 0.09, 95% CI 0.02–0.72) and asthma (adjusted OR 0.14, 95% CI 0.03–0.67). Higher education was protective for both outcomes, and regular mask use was protective for chronic bronchitis; smoking and industrial employment were associated with greater respiratory morbidity.ConclusionIn Karachi, residing within 5 km of major industrial zones is linked to a higher burden of self-reported respiratory symptoms and chronic respiratory disease. These findings underscore the importance of early screening and preventive strategies for nearby communities and support zoning and urban-planning measures that increase residential buffers from industrial facilities.