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.
Water networks are critical infrastructure components, ensuring the continuous supply of high-quality drinking water to consumers. To secure such water supply, regular maintenance, including the replacement of deteriorating pipelines, is essential. In this study, a methodology has been developed for determining optimal pipeline replacement solutions in water supply systems at water utilities with limited data availability. Hydraulic analysis has been conducted on the segment of 25 km of the water supply network using the free software EPANET (Environmental Protection Agency, NETwork) Applying water network optimization, eight pipeline replacement projects according to 13 pre-defined criteria have been identified and evaluated. The paper outlines the methods for evaluating the criteria, including defining specific quantitative limits. The Analytical Hierarchical Process (AHP) method was used in the paper to determine the weights of the criteria. The reason for applying this method refers to problems that involve a set of criteria with a mixed structure, including both quantitative and qualitative aspects. Also, the paper describes the steps of the multi-criteria optimization method VIKOR (Serbian language – VIšekriterijumsko KOmpromisno Rangiranje), used to select the optimal project. The obtained results were also confirmed by the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) multi-criteria optimization method. This paper, considered as a case study, describes a method, i.e., application of a new principle and an innovative way to solve a problem for developing countries.
Rolling bearings, as key components of rotating machinery, play a crucial role in the reliable operation of equipment. Over time, rolling bearings inevitably experience wear and fatigue, leading to damage. Accurate prediction of their Remaining Useful Life (RUL) is of paramount importance. This paper proposes an RUL prediction model based on the Multi-Scale Temporal Convolutional Network (MSTCN). The model effectively integrates both time-domain and frequency-domain information from bearing vibration signals through a multi-scale feature extraction module, enabling it to capture feature representations at different time scales. Additionally, the MSTCN's powerful temporal modeling capabilities allow it to capture long-term dependencies and short-term fluctuations in the bearing degradation process. Experimental results show that, compared to traditional methods, the proposed MSTCN model significantly improves the accuracy and stability of RUL predictions on the PHM2012 bearing dataset, demonstrating the effectiveness of the method in predicting the RUL of rolling bearings.
Enhancing the sharpness of blurred images continues to be a critical and persistent issue in the domain of image restoration and processing, requiring precise techniques to recover lost details and enhance visual clarity. This study proposes a novel model combines the strengths of fuzzy systems with mathematical transformations to address the complexities of blurred image restoration. The model operates through a multi-stage framework, beginning with pixel coordinate transformations and corrections to account for geometric distortions caused by blurring. Fuzzy logic is employed to handle uncertainties in blur estimation, utilizing membership functions to categorize blur levels and a rule-based system to dynamically adapt corrective actions. The fusion of fuzzy logic and mathematical transformations ensures localized and adaptive corrections, effectively restoring sharpness in blurred regions while the preservation of regions with minimal distortion. Additionally, fuzzy edge enhancement is introduced to emphasize edges and suppress noise, further improving image quality. The final restoration process includes normalization and structural constraints to ensure the output aligns with the original unblurred image. Experimental results showcase the performance and reliability of the developed framework to restore clarity, preserve fine details, and minimize artifacts, making it a robust solution for diverse blurring scenarios. The proposed approach offers a significant advancement in blurred image restoration, combining the adaptability of fuzzy logic with the precision of mathematical computations to achieve superior results.
Diabetic foot ulcers (DFUs), often exacerbated by secondary bacterial infections, are a major complication of diabetes and a leading cause of morbidity. Understanding the spectrum of bacterial pathogens and their profiles of antibiotic resistance is essential for developing effective treatment strategies. This study aimed to identify bacterial isolates from the DFUs and evaluate their susceptibility to commonly used antibiotics. A total of 186 patients with the DFUs were examined at Hayatabad Medical Complex of Peshawar in Pakistan over a three-month period. Samples were collected from infected ulcer sites and cultured with standard microbiological techniques. Bacterial identification was performed with conventional methods, and antibiotic susceptibility testing was then conducted by using the Kirby-Bauer disk diffusion method. Gram-Negative bacteria were predominant, with Pseudomonas aeruginosa, Proteus mirabilis, Acinetobacter spp., Escherichia coli, Klebsiella spp., Enterobacter spp., and Streptococcus pyogenes being the most commonly isolated organisms. Gram-Positive isolates including Staphylococcus aureus and Staphylococcus epidermidis, P. aeruginosa, and Enterobacter spp. showed high sensitivity to Gentamicin, Meropenem, and Imipenem. In contrast, Acinetobacter spp. and Klebsiella spp. exhibited significant resistance, particularly to carbapenems. Staph. aureus was generally sensitive to first-line antibiotics, such as Vancomycin and Rifampicin whereas Staph. epidermidis demonstrated multidrug resistance including pan-drug resistance in some cases. These findings highlighted the complex and resistant microbial profiles of diabetic foot infections, thus emphasizing the importance of the culture-guided antibiotic therapy. The emergence of carbapenem-resistant strains underlined the requisites for continuous surveillance, judicious antibiotic use, and improved infection control strategies to aid the recovery of patients.
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.
The growing reliance on air conditioning (AC) systems in residential and commercial buildings has led to significant increases in energy consumption and associated greenhouse gas emissions, underscoring the need for cost-effective and sustainable cooling technologies. In this study, the feasibility and performance of a 1-horsepower (1 HP) non-inverter split-unit AC system assembled entirely from locally sourced components were evaluated under controlled residential conditions. Essential parts, including copper tubing, aluminum fins, compressor units, and refrigerant gases, were procured from regional suppliers and integrated following standard Heating, Ventilation, and Air Conditioning (HVAC) design protocols. Performance tests were conducted across five rooms in a residential apartment-comprising a lounge (largest), masters bedroom, and three additional bedrooms of decreasing size-to assess cooling effectiveness. Using an infrared thermometer (IR8895), temperature metrics including saturation temperature, cooling rate, and peak cooling temperature were recorded. Initial room temperatures ranged from 23.5${ }^{\circ} \mathrm{C}$ to 26.2${ }^{\circ} \mathrm{C}$, while final cooling temperatures ranged from 16.1${ }^{\circ} \mathrm{C}$ to 16.9${ }^{\circ} \mathrm{C}$. Cooling time increased progressively with room size, extending from 10 to 100 minutes. Corresponding saturation temperatures were observed at 24.9${ }^{\circ} \mathrm{C}$ to 26.6${ }^{\circ} \mathrm{C}$, with saturation times between 3.24 and 5.43 minutes, and peak temperatures consistent with the final cooling levels. Calculated cooling loads were 28.8 W (small rooms), 47.0 W (medium rooms), and 65.93 W (large rooms), with respective power consumption values of 85.5 W, 142.6 W, and 199.6 W. The Energy Efficiency Ratio (EER) and Coefficient of Performance (COP) were determined to be 9.25 and 2.7, respectively, across room types. The results indicated that the locally assembled split-unit AC system delivered competitive cooling performance relative to commercial equivalents, particularly in terms of thermal regulation, response time, and energy efficiency. The use of indigenous materials and components did not compromise operational reliability or compliance with HVAC standards. These findings support the viability of locally fabricated AC systems as a sustainable alternative for effective residential cooling in resource-constrained settings.
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.
Although methodological innovations have reshaped many aspects of scientific writing, literature reviews remain one of its most structurally underdeveloped and conceptually inconsistent components. Existing approaches often fail to communicate the functional role of individual sources within the research argument, leaving both readers and reviewers without a transparent sense of contribution, coherence, or originality. This paper introduces the Pyramid of Contribution Review (PCR), a novel framework that visually and functionally maps references according to their role in the manuscript (Introduction, Methodology, Results, Discussion, Gap) and their level of relevance. Through a mixed-methods validation process, including expert-based Delphi design (n = 28) and a large-scale evaluation survey (n = 118), the method was rigorously tested across disciplines. Statistical analyses reveal that manuscripts perceived to employ the PCR model are 3.45 times more likely to be rated as publishable compared to those relying on conventional narrative reviews. Experts overwhelmingly endorsed the model for its clarity, strategic value, and pedagogical utility. This study positions the PCR framework not only as a solution to a long-standing structural gap in scientific writing but as a forward-looking standard for literature organization in high-impact research. The future of scholarly communication requires not just citation density but citation precision, exactly what the PCR model provides.
Open Access
