The retail sector is increasingly confronted with challenges arising from digital disruption and shifts in consumer behaviour. Amidst this transformation, the integration of augmented reality (AR) has been identified as a promising avenue to revitalise the in-store shopping experience, offering a means to engage customers more effectively and enhance competitiveness. This study investigates the extent to which AR applications can improve the shopping experience in physical retail settings, with particular emphasis on their capacity to foster customer flow states. A survey of 239 participants, comprising both general consumers and retail professionals, was conducted to explore the impact of AR on the shopping process. The findings suggest that AR significantly enhances the shopping experience, contributing to heightened customer engagement and immersion. However, while AR is found to influence flow states, the flow experience itself does not mediate the relationship between AR use and the shopping experience. These results offer important insights into the application of AR in brick-and-mortar retail environments, providing a management-oriented perspective on how its strategic implementation can generate sustainable competitive advantages. Moreover, the study contributes to existing AR literature by extending the understanding of its role in traditional retail, highlighting practical considerations for retailers aiming to adopt such technologies. The evidence also underscores the potential of AR in fostering behaviours and experiences that are essential for maintaining the competitiveness of physical stores in the digital age. Therefore, the adoption of AR technologies is not only recommended for enhancing the customer experience but also for driving innovation within the retail industry.
The adoption of electronic documents (e-documents) in logistics has emerged as a critical component for enhancing efficiency, reducing operational costs, and contributing to environmental sustainability. However, despite its numerous advantages, the transition from traditional paper-based systems to e-documents has been sluggish, hindered by a range of barriers including legal and regulatory constraints, lack of standardization, and insufficient system interoperability. This study aims to identify and analyze these barriers, propose relevant policy measures to mitigate them, and evaluate the most effective policy for promoting widespread adoption. Four primary policy strategies were proposed to address the challenges of e-documents in logistics. These policies were assessed using multi-criteria analysis, incorporating fuzzy Step-wise Weight Assessment Ratio Analysis (SWARA) and Axial-Distance-Based Aggregated Measurement (ADAM) methods, to rank their effectiveness in overcoming adoption barriers. The results indicate that the policy ensuring full compliance with regulatory and documentation requirements, through a harmonized approach, offers the most significant potential for driving the adoption of e-documents. This policy emphasizes standardization and mandates compliance, fostering a more robust and efficient transition to digital systems. The findings provide a comprehensive understanding of the policy measures that can most effectively support the expansion of e-documents in logistics, thereby contributing to the long-term sustainability and operational excellence of the sector.
Urban poverty remains a critical challenge globally, with Malaysia serving as a prominent example of the pervasive struggles faced by the urban poor. These populations are particularly burdened by unaffordable housing, limited access to stable employment opportunities, and inadequate digital and public services. Despite the implementation of policies such as the National Housing Policy and the National Urbanization Policy, these issues persist, exacerbated by the escalating costs of living and the lack of effective support systems. This study presents a comprehensive model aimed at improving the urban poor's quality of life (QOL) in Malaysia by integrating key elements of sustainable urban development. A quantitative research methodology was employed to collect data, focusing on the critical factors of employment, affordable housing, transportation, healthcare, education, and digital access. The findings underscore the importance of a holistic approach to urban poverty alleviation, which prioritizes the availability of affordable housing located near essential amenities, coupled with reliable transportation, accessible healthcare, and educational services. Furthermore, it was identified that community participation plays a pivotal role in enhancing housing outcomes, with increased engagement linked to better planning and the development of more inclusive and livable urban environments. Key contributors to improved housing participation (HP) were found to include the provision of affordable housing (AH), the development of accessible transportation systems (AT), the availability of essential facilities (AF), environmental initiatives (EI), and heightened public awareness (AD). These factors collectively demonstrate that improvements in infrastructure, access to essential services, and community involvement are critical to achieving sustainable urban development. This model offers a framework that can be applied not only in Malaysia but also in other urban contexts globally, providing a pathway to reduce urban poverty and improve the well-being of urban populations.
Linear systems often involve coefficients that are uncertain or imprecise due to inherent variability and vagueness in the data. In scenarios where only approximate or vague knowledge of the system parameters is available, traditional fuzzy logic is commonly employed. However, conventional fuzzy logic may be inadequate when defining a membership degree with a single, precise value proves difficult. In such cases, Single-Valued Trapezoidal Neutrosophic Numbers (SVTrNNs) offer a more suitable framework, as they account for indeterminacy, alongside truth and falsity. The solution of Single-Valued Trapezoidal Neutrosophic Linear Equations (SVTrNLEs) was explored in this study using an embedding approach. The approach reformulates the SVTrNLEs into an equivalent crisp linear system, enabling the application of conventional solution methods. The solution was then obtained using either the matrix inversion method or the gradient descent optimization algorithm implemented in PyTorch. The robustness and adaptability of gradient-based optimization techniques were thoroughly assessed. The learning process minimizes the residual error iteratively, with convergence behaviour and numerical stability analyzed across various parameter configurations. The results demonstrate rapid convergence, proximity to exact solutions, and significant robustness to parameter variability, highlighting the efficacy of gradient descent for solving uncertain linear systems. These findings provide a foundation for the extension of gradient-based methods to more complex systems and broader applications. Furthermore, the existence and uniqueness of the neutrosophic solution to an $n\times n$ linear system were rigorously analyzed, with numerical examples provided to assess the reliability and efficiency of the proposed methods.
The strategic positioning of distribution, sales, and service facilities plays a critical role in ensuring the efficiency, reliability, and cost-effectiveness of supply chains. In particular, the location of such facilities within the transshipment network significantly influences both operational costs and consumer satisfaction by affecting delivery times and service quality. This study introduces a mixed-integer linear programming (MILP) model designed to optimize the layout of a postal supply chain network. The model aims to minimize the key cost components, including transportation, facility location, and holding costs, within a four-echelon supply chain consisting of suppliers, warehouses, retailers, and recipients. Parcels are initially collected by suppliers and delivered to regional warehouses, which then allocate them to selected retail locations. The selection of optimal retail locations is based on a cost minimization criterion, after which parcels are transported to the final delivery points—post offices situated in various cities. A distinctive feature of the proposed model is the assumption that demand at the recipient level is determined at the supplier level, thereby facilitating more centralized demand management and reducing uncertainties in the planning process. The model incorporates several constraints, such as flow balance, capacity limitations, and retailer selection. The optimization problem is solved using LINGO 16 software, and a comprehensive analysis is conducted to identify the optimal configuration of retailer locations and parcel flow distribution. A numerical example is provided to demonstrate the practical application of the model, and sensitivity analysis is performed to assess the impact of key parameters—such as retailer capacity and initial inventory levels—on the overall cost. The results indicate that increasing retailer capacity leads to a reduction in total supply chain costs, highlighting the benefits of economies of scale and parcel consolidation. However, an increase in the initial quantity of parcels results in higher costs due to elevated transportation and holding expenses. These findings offer valuable insights for decision-makers seeking to optimize postal supply chains, balancing the need for cost efficiency with the provision of high-quality service.
Accurate traffic prediction is essential for optimizing urban mobility and mitigating congestion. Traditional deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), struggle to capture complex spatiotemporal dependencies and dynamic traffic variations across urban networks. To address these challenges, this study introduces DSTGN-ExpertNet, a novel Deep Spatio-Temporal Graph Neural Network (DSTGNN) framework that integrates Graph Neural Networks (GNNs) for spatial modeling and advanced deep learning techniques for temporal dynamics. The framework employs a Mixture of Experts (MoE) approach, where specialized expert models are dynamically assigned to distinct traffic patterns through a gating network, optimizing both prediction accuracy and interpretability. The proposed model is evaluated on large-scale real-world traffic datasets from Beijing and New York, demonstrating superior performance over conventional methods, including Spatio-Temporal Graph Convolutional Networks (ST-GCN) and attention-based models. With a mean absolute error (MAE) of 1.97 on the BikeNYC dataset and 9.70 on the TaxiBJ dataset, DSTGN-ExpertNet achieves state-of-the-art accuracy. These findings highlight the potential of GNN-based frameworks in revolutionizing traffic forecasting and intelligent transportation systems (ITS).
This study investigates the recognition of seven primary human emotions—contempt, anger, disgust, surprise, fear, happiness, and sadness—based on facial expressions. A transfer learning approach was employed, utilizing three pre-trained convolutional neural network (CNN) architectures: AlexNet, VGG16, and ResNet50. The system was structured to perform facial expression recognition (FER) by incorporating three key stages: face detection, feature extraction, and emotion classification using a multiclass classifier. The proposed methodology was designed to enhance pattern recognition accuracy through a carefully structured training pipeline. Furthermore, the performance of the transfer learning models was compared using a multiclass support vector machine (SVM) classifier, and extensive testing was planned on large-scale datasets to further evaluate detection accuracy. This study addresses the challenge of spontaneous FER, a critical research area in human-computer interaction, security, and healthcare. A key contribution of this study is the development of an efficient feature extraction method, which facilitates FER with minimal reliance on extensive datasets. The proposed system demonstrates notable improvements in recognition accuracy compared to traditional approaches, significantly reducing misclassification rates. It is also shown to require less computational time and resources, thereby enhancing its scalability and applicability to real-world scenarios. The approach outperforms conventional techniques, including SVMs with handcrafted features, by leveraging the robust feature extraction capabilities of transfer learning. This framework offers a scalable and reliable solution for FER tasks, with potential applications in healthcare, security, and human-computer interaction. Additionally, the system’s ability to function effectively in the absence of a caregiver provides significant assistance to individuals with disabilities in expressing their emotional needs. This research contributes to the growing body of work on facial emotion recognition and paves the way for future advancements in artificial intelligence-driven emotion detection systems.
Drought, a complex natural phenomenon with profound global impacts, including the depletion of water resources, reduced agricultural productivity, and ecological disruption, has become a critical challenge in the context of climate change. Effective drought prediction models are essential for mitigating these adverse effects. This study investigates the contribution of various data preprocessing steps—specifically class imbalance handling and dimensionality reduction techniques—to the performance of machine learning models for drought prediction. Synthetic Minority Over-sampling Technique (SMOTE) and near miss sampling methods were employed to address class imbalances within the dataset. Additionally, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were applied for dimensionality reduction, aiming to improve computational efficiency while retaining essential features. Decision tree algorithms were trained on the preprocessed data to assess the impact of these preprocessing techniques on model accuracy, precision, recall, and F1-score. The results indicate that the SMOTE-based sampling approach significantly enhances the overall performance of the drought prediction model, particularly in terms of accuracy and robustness. Furthermore, the combination of SMOTE, PCA, and LDA demonstrates a substantial improvement in model reliability and generalizability. These findings underscore the critical importance of carefully selecting and applying appropriate data preprocessing techniques to address class imbalances and reduce feature space, thus optimizing the performance of machine learning models in drought prediction. This study highlights the potential of preprocessing strategies in improving the predictive capabilities of models, providing valuable insights for future research in climate-related prediction tasks.
Foggy road conditions present significant challenges for road monitoring systems and autonomous driving, as conventional defogging techniques often fail to accurately recover fine details of road structures, particularly under dense fog conditions, and may introduce undesirable artifacts. Furthermore, these methods typically lack the ability to dynamically adjust transmission maps, leading to imprecise differentiation between foggy and clear areas. To address these limitations, a novel approach to image dehazing is proposed, which combines an entropy-weighted Gaussian Mixture Model (EW-GMM) with Pythagorean fuzzy aggregation (PFA) and a level set refinement technique. The method enhances the performance of existing models by adaptively adjusting the influence of each Gaussian component based on entropy, with greater emphasis placed on regions exhibiting higher uncertainty, thereby enabling more accurate restoration of foggy images. The EW-GMM is further refined using PFA, which integrates fuzzy membership functions with entropy-based weights to improve the distinction between foggy and clear regions. A level set method is subsequently applied to smooth the transmission map, reducing noise and preserving critical image details. This process is guided by an energy functional that accounts for spatial smoothness, entropy-weighted components, and observed pixel intensities, ensuring a more robust and accurate dehazing effect. Experimental results demonstrate that the proposed model outperforms conventional methods in terms of feature similarity, image quality, and cross-correlation, while significantly reducing execution time. The results highlight the efficiency and robustness of the proposed approach, making it a promising solution for real-time image processing applications, particularly in the context of road monitoring and autonomous driving systems.
The impact of artificial hailstones on G300 steel sheets with varying thicknesses has been systematically investigated to evaluate the resulting dent depths. Two distinct methods for producing simulated hailstones were employed: one utilizing polyvinyl alcohol (PVA) adhesive and the other incorporating liquid nitrogen. Comparative analyses of these techniques revealed that the liquid nitrogen method, in conjunction with demineralized water, yielded more accurate results than the PVA adhesive-based method. Experimental findings were cross-referenced with theoretical predictions and finite element simulations, with model accuracy being validated against existing research in the field. The study focused on three hailstone diameters—38mm, 45mm, and 50mm—across various sheet thicknesses. Results indicate that dent depth is primarily influenced by the impact energy, sheet metal thickness, and hailstone diameter. Notably, the momentum of the hailstone plays a critical role, with smaller, higher-momentum hailstones inducing permanent deformations comparable to those of larger, lower-momentum hailstones, even when the impact energies are equivalent. The findings suggest that variations in hailstone momentum can lead to similar deformation patterns across different sizes, emphasizing the importance of momentum in the design of steel sheet materials for enhanced hailstone impact resistance. This study contributes valuable insights for the development of more resilient materials in industries subject to dynamic impact loading, such as automotive and aerospace engineering.
This systematic review seeks to synthesize the existing literature on the integration of blockchain technology into sustainable finance, with a particular focus on its role in enhancing transparency and accountability. A bibliometric analysis was conducted using the PRISMA methodology, incorporating a meta-analysis of scholarly articles published between 2018 and 2023. The analysis was based on data extracted from databases such as Springer Link, Dimensions, and Google Scholar, using the search terms "blockchain," "sustainable," "finance," "transparency," and "accountability." Open-access articles from reputable, peer-reviewed journals were selected to ensure the reliability of the data. Research questions were framed following the PICo method, addressing the specific impacts of blockchain technology on sustainable finance systems. The review highlights that blockchain has the potential to significantly enhance transparency and accountability in sustainable finance by providing robust mechanisms for transaction traceability and verification. Notably, blockchain technology has been applied to improve carbon market management, facilitate green bond issuance, and support the disclosure of Environmental, Social, and Governance (ESG) data. Despite these promising applications, several challenges remain, including regulatory uncertainties, technological limitations, and integration complexities, which could hinder its widespread adoption. To facilitate the global integration of blockchain in sustainable finance, it is recommended that financial institutions invest in technological infrastructure and training. Furthermore, policymakers should work towards harmonizing regulatory frameworks, while researchers are urged to pursue interdisciplinary, empirical studies to address the potential and limitations of blockchain technology. A shift in academic curricula to include blockchain’s implications in finance and sustainability is also recommended to better prepare future professionals. In conclusion, while blockchain holds significant promise for improving transparency and accountability, its broader adoption will require addressing technological, regulatory, and socio-economic barriers.
This study investigates the structural response of a hub steering knuckle from a Toyota Camry LE under typical in-service loading conditions, with a focus on material performance comparison. Aluminium alloy A356.0-T6 and grey cast iron were selected as candidate materials for the analysis. A three-dimensional (3D) model of the hub steering knuckle was generated using SolidWorks 2018, while static structural simulations were conducted with ANSYS Workbench R15.0 (2019 version). The factor of safety (FOS) was varied between 2.293 and 15 to account for the diverse operational scenarios. The applied loading conditions were derived from the cumulative forces acting on the four tyres of the vehicle, with a total force of 3938.715 N in the Z-direction. The steering moment was calculated to be 5400 N·mm at a perpendicular distance of 108 mm, while the braking force amounted to 3964.63 N·mm, with a corresponding braking moment of 277,524.73 N·mm, all determined using standard analytical formulas. A solid mesh type was employed for the finite element analysis (FEA), with a blended curvature-based meshing technique applied. The results of the analysis showed that, for A356.0-T6, the maximum equivalent Von Mises stress (VMS), maximum equivalent elastic strain, maximum principal stress, and maximum shear stress were 36.079 MPa, 0.00018393 mm/mm, 44.587 MPa, and 19.871 MPa, respectively. In comparison, grey cast iron exhibited values of 24.016 MPa, 0.00013104 mm/mm, 41.214 MPa, and 18.625 MPa, respectively. The maximum directional deformations along the Z-axis for A356.0-T6 and grey cast iron were 0.010135 mm and 0.007275 mm, respectively. The maximum total deformations were recorded at 0.069036 mm and 0.048725 mm for A356.0-T6 and grey cast iron, respectively. These findings suggest that both materials are suitable for use in hub steering knuckles, with grey cast iron being preferable when impact resistance is a priority, whereas A356.0-T6 is more suitable for applications requiring lightweight and corrosion resistance. The results contribute to the understanding of material selection for automotive components, considering both mechanical performance and operational demands.
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