Search results

The objective of this work is to investigate the rate of entropy generation of a hybrid nanoliquid (Cu-Ag/Water) flowing on a stretching sheet in the presence of convective boundary conditions, heat generation/absorption, double stratification and Stefan blowing. At present, the capability of interchange of thermal energy is not concerned only with an estimation of the amplification in the rate of heat exchange but also depends on profitable and obliging contemplation. Acknowledging the demands, researchers have been associated with the refinement of the performance of a heat exchange, which is referred to as an intensification of the interchange of heat.

By using a similarity transformation, the system of governing partial differential equations (PDEs) is transformed into the system of nonlinear ordinary differential equations (ODEs). The rebuilt ordinary differential equations are then solved by applying the homotopy analysis method. After computing the temperature, concentration and velocity profiles for a range of relevant study parameters, the resulting results are examined and discussed.

Elevating the Stefan blowing parameter values enhances the temperature profile. Conversely, it diminishes with increasing concentration stratification, thermal stratification and heat generation/absorption coefficient. The rate of entropy generation rises with increasing diffusion parameter, Brinkman number and concentration difference parameter. Stronger viscous forces between the sheet and the fluid flow cause skin friction to increase as fw and Sϕ increase. The local Nusselt number falls as fw and ω rise. However, as Sθ rises, the local Nusselt number gets higher.

The transmission of mass and heat is the basis of the current study, which is useful in a number of industrial and technological domains.

The paper investigates entropy production and heat transmission in a hybrid nanoliquid flow over a stretching sheet, incorporating factors such as heat generation/absorption, convective boundary conditions, Stefan blowing and double stratification. The research highlights a gap in the existing literature, indicating that this specific combination of factors has not been previously explored.

This paper examines the transformation of York College Library’s services through automation before, during and after the COVID-19 pandemic. This paper aims to evaluate how automation tools such as Integrated Library Systems (ILS) and e-resource platforms facilitated the transition to remote services, while identifying challenges related to staff training, accessibility and digital engagement. By analyzing user feedback, operational data and automation trends, the study provides actionable insights for optimizing academic library services. The findings offer scalable recommendations to enhance service delivery, ensure inclusivity and strengthen the resilience of libraries in adapting to evolving educational demands in a post-pandemic landscape.

This study uses a mixed-methods approach to analyze York College Library’s automation trends across three phases: pre-pandemic, during the pandemic and post-pandemic. Quantitative data was collected through surveys and library usage statistics, measuring e-resource downloads and virtual reference inquiries. Qualitative insights were obtained via interviews with library staff and users, exploring challenges and the impact of automation systems on service delivery. The combined analysis provides a comprehensive understanding of the library’s transition to remote services, identifying key strengths and areas for improvement to inform future strategies for academic library operations.

This study revealed that York College Library’s pre-pandemic investment in automation tools, such as ILS and e-resource platforms, enabled a rapid shift to remote services during the COVID-19 pandemic. Virtual reference inquiries surged by 200%, and e-resource usage increased by 45%, demonstrating high user reliance on digital services. However, gaps in staff training and accessibility highlighted areas for improvement. Post-pandemic, the library adopted a hybrid model that blends physical and virtual services to meet diverse user needs. The findings underscore the importance of continuous professional development, robust digital infrastructure and accessibility-focused strategies to ensure resilient and inclusive library operations.

This paper provides a unique case study of York College Library’s transition to remote services during the COVID-19 pandemic, offering insights into how automation tools were leveraged to maintain academic support. It highlights the dual role of technology in enabling service continuity and exposing gaps in training and accessibility. By integrating user feedback, operational data and automation trends, the study offers actionable recommendations for libraries navigating post-pandemic challenges. Its focus on scalable solutions for hybrid service models, staff development and digital equity adds significant value to the evolving discourse on academic library automation and resilience in higher education.

To evaluate the efficacy of a PRECEDE model-based educational program in enhancing awareness, attitudes and preventive behaviors against diabetes complications among rural patients with type 2 diabetes mellitus (T2DM).

This quasi-experimental study included 160 T2DM patients from rural health centers in Andimeshk, Iran (2020–2021). Participants were randomly assigned to intervention (n = 80) or control (n = 80) groups. The intervention group received a six-session in-person educational program, supplemented by weekly lifestyle videos via WhatsApp over six weeks, guided by PRECEDE model constructs. A validated questionnaire assessed knowledge, attitudes, enabling and reinforcing factors, and preventive behaviors pre- and post-intervention.

Post-intervention, the intervention group demonstrated significantly (p < 0.001) higher mean scores in knowledge, attitudes, enabling factors, reinforcing factors and preventive behaviors than controls. Fasting blood sugar (FBS) levels were significantly (p < 0.001) reduced in the intervention group at three-month follow-up.

The findings demonstrate that a comprehensive program grounded in the PRECEDE model can significantly enhance patients’ awareness, strengthen positive attitudes, increase enabling and reinforcing factors and promote preventive lifestyle behaviors. Importantly, the intervention led to improved glycemic control, as evidenced by reduced fasting blood sugar levels at three-month follow-up. These results suggest that tailored, theory-driven educational initiatives have the potential to empower rural patients with type 2 diabetes to make sustainable lifestyle changes and better manage their condition. The study provides a model for developing and implementing effective diabetes self-management programs in underserved rural communities, which can ultimately contribute to reducing diabetes-related complications and improving overall health outcomes in this population.

This study has demonstrated the effectiveness of the Internet of Things (IoT)-based technology using ANNs for localising AE sources in rail sections, providing a promising avenue for future research and practical applications.

The paper’s main focus is to develop an IoT based, energy efficient and smart sensor-based system that can detect AE sources accurately and effectively. In this study, the AE sensor is attached to an Arduino board for wireless data transmission. As the AE simulation process in this paper is an non-destructive testing (NDT) technique, pencil lead break (PLB) is done on the top flange (TF), side of top flange (STF), web and bottom flange (BF) to simulate artificial AE sources in the rail section. The generated AE signal due to PLB is collected by mounting the AE sensor over the rail web portion. It is found from the literature that optimal placement of the AE sensor is at the web portion of the rail section. Therefore, the good-quality signal from every segment (i.e. TF, web and BF) of the rail section can be captured from the web part. After capturing the signals wirelessly, the AE features like amplitude, energy, duration, etc. are used to find the AE source location using an artificial neural network (ANN). The developed ANN model is giving very promising results in terms of localisation of the AE source. The main challenge of this paper is transmitting AE signals wirelessly to DACs. As the ANN model is running perfectly, it can be said the Arduino can transmit the full packet AE signal to DAC through the Internet.

The experiment conducted to localise AE sources in a rail section using the developed ANN model has yielded very promising results. PLB is applied at 10 mm intervals up to 1,200 mm on the top flange, side of the top flange, web and bottom flange of the rail section. The MATLAB NNTOOL was used to develop the ANN model, which accurately detected AE sources in the rail section. The AE sensor mounted at the web provided excellent localisation of AE sources, with PLB at the TF, STF, web and BF detected without error. The percentage of error was found to be less than 1%, which is a highly promising result. Consequently, the developed ANN model has significant potential for use in detecting and localising damage in railway systems, which could ultimately improve the safety and reliability of railway transportation.

This study has shown that the use of ANNs for damage detection and localisation and the use of IoT in railway systems is more practical and can lead to significant improvements in efficiency and safety. With further research and development, the developed ANN model could become an essential tool for maintaining the rail section’s infrastructure, ultimately enhancing the railway system’s overall performance and reliability.

This study aims to investigate redundant information in mandatory non-financial reports (NFRs) demanded by regulators, focusing primarily on overlapping disclosures in a new Indian sustainability reporting (SR) framework.

The study sample comprised NIFTY100 listed entities that published SR voluntarily during 2021–2022. The authors used content analysis and cosine similarity techniques to conceptually compare redundancy in SR disclosures with non-financial disclosures.

The findings reveal an information overlap in SR disclosure with other NFRs disclosures. The disclosures of Directors’ Report have higher cosine similarity scores at the firm level with SR, followed by the Management Discussion and Analysis report, Corporate Governance report and Corporate Social Responsibility report. The additional analysis reveals that qualitative disclosures and disclosures comprising governance factors overlap more in SR.

Policymakers should look to establish relevant disclosure guidelines in the SR system, and thereby, shed light on fundamental issues to enhance future SR framework reforms.

The study highlight the need for integration and amendment in the disclosure guidelines of NFRs to improve the overall transparency of the reports.

Previous studies have examined the redundancy in annual reports and SRs from the point of view of overlapping information. To the best author’s knowledge, this is possibly among the first studies to offer insights into the repetition of disclosures required by regulators in statutory NFRs based on environmental, social, and governance factors through the lenses of the institutional theory.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

This research aims to investigate the interplay between flexibilities and strategic orientations in the context of supply chain agility (SCA), particularly in the medical equipment supply chain. The study seeks to identify key internal and external flexibility factors, along with the firm’s strategic orientations, and understand why and how these factors are interrelated and contribute to the development or enhancement of SCA.

The study adopts an inductive exploratory multiple case study design to empirically identify and examine the underlying flexibility and strategic orientation factors and their link to SCA. Data collection tools included semi-structured interviews and access to key company documentation and archives from six major medical equipment suppliers in Jordan.

The research findings lead to the proposal of an emerging theoretical model describing the nature of relationships among internal/external flexibility factors, strategic orientations and SCA, with underlying research propositions that can later be subjected to deductive testing and empirical quantitative validation.

This research advances the theoretical understanding of SCA by investigating its strategic antecedents, including various orientations and their direct and indirect effects. Second, it provides a comprehensive insight into the combined impact of internal and external flexibilities on SCA, an aspect relatively underexplored in previous literature.

This study aims to examine the impact of supply chain digitalization (SCD) on supply chain performance (SCP) with the integration of supply chain agility (SCA) and supply chain resilience (SCR) in manufacturing firms. Additionally, it investigates the moderating role of environmental dynamism (ED) in these relationships in the context of manufacturing firms.

A quantitative research design was employed, utilizing a structured questionnaire to collect data. The sample consisted of 374 valid responses from various managerial levels within manufacturing firms in Jordan. A purposive sampling technique was used to ensure that participants had relevant expertise in supply chain management. The data were analyzed using structural equation modeling (SEM) through Smart PLS software to test the proposed hypotheses.

The study found that SCD positively impacts SCP by enhancing both SCA and SCR. Digital technologies were shown to improve supply chain visibility, speed of decision-making and resource efficiency, which are essential for maintaining performance under uncertain conditions. Furthermore, ED significantly moderates the relationship between digitalization and SCP, indicating that the benefits of digitalization on SCA and SCR are more pronounced in highly dynamic environments.

This study contributes to the literature by offering an investigation on the role of SCD in influencing SCA, SCR and ultimately SCP in manufacturing firms. It also sheds light on the moderating effect of ED, providing a deeper understanding of the conditions under which digital transformation in supply chains is most beneficial. The study was based on three theoretical foundations: the resource-based view (RBV), the dynamic capabilities theory (DCT) and the contingency theory (CT).