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This study aims to examine the impacts of higher memory dependencies on a novel semiconductor material that exhibits generalized photo-piezo-thermo-elastic properties. Specifically, the research focuses on analyzing the behavior of the semiconductor under three distinct temperature models.

The study assumes a homogeneous and orthotropic piezo-semiconductor medium during photo-thermal excitation. The field equations have been devised to encompass higher order parameters, temporal delays and a specifically tailored kernel function to address the problem. The eigenmode technique is used to solve these equations and derive analytical expressions.

The research presents graphical representations of the physical field distribution across different temperatures, higher order plasma heat conduction models and time. The results reveal that the amplitude of the distribution profile is markedly affected by factors such as the memory effect, time, conductive temperature and spatial coordinates. These factors cannot be overlooked in the analysis and design of the semiconductor.

Specific cases are also discussed in detail, offering the potential to advance the creation of precise models and facilitate future simulations.

The research offers valuable information on the physical field distribution across various temperatures, allowing engineers and designers to optimize the design of semiconductor devices. Understanding the impact of memory effect, time, conductive temperature and spatial coordinates enables device performance and efficiency improvement.

This manuscript is the result of the joint efforts of the authors, who independently initiated and contributed equally to this study.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

Determining vulnerability and resilience is necessary to develop sustainable agribusiness. The purpose of this study is to clarify and understand the current condition and problems in the tea supply chain and to develop a framework on how to build a sustainable and resilient tea supply chain.

This study is a case study analysis which develops an integrated framework to build a resilient tea supply chain. It evaluates and extends the current knowledge of Javanese tea by applying business process analysis to understand the situation.

This paper develops an integrated and conceptual framework on how to build resilient supply chain by considering five broad factors: vulnerability analysis, assessment of assets, supply chain collaboration, control mechanism from government and outcome.

The framework provides a conceptual view but limited to field surveys in Central Java Province. This study could increase the general understanding of tea supply chain in Indonesia and its major problems and challenges.

The framework also highlights different stakeholder's organizational constraints and issues, especially during the COVID-19 pandemic.

The business process analysis and conceptual framework offer an expanded and in-depth explanation on how organizations respond to the changing conditions, especially during the COVID-19 pandemic.

Hoping to uncover the physical principles of the vibration of the functionally graded material (FGM) microplate, by which the authors can make contributions to the design and manufacturing process in factories like micro-electro-mechanical system (MEMS) and other industries.

The authors design a method by establishing a reasonable mathematical model of the physical microplate composed of a porous FGM.

The authors discover that the porosity, the distributions of porosity, the power law of the FGM and the length-to-thickness ratio all affect the natural frequency of the vibration of the microplate, but in different ways.

Originally proposed a model of the micro FGM plate considering the different distributions of the porosity and scale effect and analyzed the vibration frequency of it.

This empirical study aims to examine luxury fashion retailers' localised multiple channel distribution strategies in China.

Through case studies of 15 participating retailers, qualitative data were collected from 33 semi-structured interviews.

Strong impacts of internationalisation strategies, distribution strategies and channel length towards multiple channel retailing are revealed. Multi-channel retailing is widely employed by firms who have entered China and further developed their businesses through local partnerships and adopted a selective distribution strategy via relatively longer channels. Omni-channel retailing is only suitable for the few retailers using an exclusive distribution strategy through direct marketing and wholly owned customer relationship management. As a dynamic transformation from multi- to omni-channel retailing, cross-channel retailing is adopted by those who are withdrawing from local partnerships and shifting to wholly owned expansions and operations in host markets.

The results are potentially challenged by relatively small sample size.

Practitioners are suggested to adapt multiple channel retailing to their international expansion strategies, distribution strategies and channel length in the host markets.

This paper contributes to the literature in both multiple channel retailing and international retailing by offering insights into the motives, development patterns and suitability of multiple channel retailing in the international retail marketing context.

The purpose of this paper is to determine the relationship between the access mode of research articles [Open Access (OA) and Toll-Access (TA)] and their subsequent citation counts in Biological and Physical Sciences in three Impact factor zones (High, Medium and Low).

Three subjects each from Biological Sciences (Biochemistry, Cell Biology and Genetics) and Physical Sciences (Astronomy, Oceanography and Optics) were selected for the study. A comprehensive list of journals (TA and OA) in select subjects of Biological and Physical Sciences was prepared by consulting Journal Citation Report’s Master Journal List (for the compilation of both Open Access and Toll Access journal list) and Directory of Open Access Journals (for the compilation of Open Access journal list). For each journal, essential details like content language, format, year of publication, access mode (Open Access or Toll Access), etc. were obtained from Ulrich’s Periodical Directory. Web of Science (WoS) was used as citations indexing tool in this study. The data set was run on the WoS to collect the citation data.

The results of the study indicate that open mode of access is not a prerequisite for higher citation boost as in the majority of the cases in this study, TA articles have garnered a greater number of citations as compared to open access articles in different Impact factor zones in Biological and Physical Sciences.

A novel approach has been adopted to understand and compare the research impact of open access (OA) and toll access (TA) journal articles in the field of Biological and Physical Sciences at three Impact factor zone levels to reveal the citation metrics encompassing three parameters, i.e. citedness, average citation count and year wise distribution of citations in select subjects of Biological and Physical Sciences.

The peer review history for this article is available at: https://publons.com/publon/[DOI]/10.1108/OIR-01-2021-0029

Omni-channel shopping affords consumers a variety of delivery options to receive products based on their preferred times and locations. By considering consumers' contributions (physical, social and attentive efforts) in co-creating delivery services, this study investigates their preferences for parcel delivery.

A scenario-based questionnaire survey is conducted for data collection in Singapore (n = 483). Furthermore, a multinomial logistic regression is performed to assess consumers' choice mode of delivery among five alternatives, that is attended home delivery, unattended home delivery, automated self-collection locker, attended pickup point and click-and-collect.

Compared to attended home delivery, consumers who choose the alternatives are found to be more willing to contribute physical effort but less interested in responding attentively to informational updates. Efforts required for social interactions discourage consumers from choosing attended deliveries, prompting unattended alternatives (e.g. home delivery and self-collection) as more attractive choices. Additionally, socio-demographic factors and product value also influence consumers' preferences.

This study contributes to the literature by integrating the theoretical concept of consumer logistics into omni-channel studies, providing a new approach to examining consumers' channel behaviour. With detailed profiling that links product value and consumers' socio-demographics to their choice mode of delivery, the authors create practical insight into the optimal design of omni-channel distribution systems that best harness consumers' voluntary contributions.

Thermo-magnetic convective flow analysis under the impact of thermal radiation for heat and entropy generation phenomena is an active research field for understanding the efficiency of thermodynamic systems in various engineering sectors. This study aims to examine the characteristics of convective heat transport and entropy generation within an inverted T-shaped porous enclosure saturated with a hybrid nanofluid under the influence of thermal radiation and magnetic field.

The mathematical model incorporates the Darcy-Forchheimer-Brinkmann model and considers thermal radiation in the energy balance equation. The complete mathematical model has been numerically simulated through the penalty finite element approach at varying values of flow parameters, such as Rayleigh number (Ra), Hartmann number (Ha), Darcy number (Da), radiation parameter (Rd) and porosity value (e). Furthermore, the graphical results for energy variation have been monitored through the energy-flux vector, whereas the entropy generation along with its individual components, namely, entropy generation due to heat transfer, fluid friction and magnetic field, are also presented. Furthermore, the results of the Bejan number for each component are also discussed in detail. Additionally, the concept of ecological coefficient of performance (ECOP) has also been included to analyse the thermal efficiency of the model.

The graphical analysis of results indicates that higher values of Ra, Da, e and Rd enhance the convective heat transport and entropy generation phenomena more rapidly. However, increasing Ha values have a detrimental effect due to the increasing impact of magnetic forces. Furthermore, the ECOP result suggests that the rising value of Da, e and Rd at smaller Ra show a maximum thermal efficiency of the mathematical model, which further declines as the Ra increases. Conversely, the thermal efficiency of the model improves with increasing Ha value, showing an opposite trend in ECOP.

Such complex porous enclosures have practical applications in engineering and science, including areas like solar power collectors, heat exchangers and electronic equipment. Furthermore, the present study of entropy generation would play a vital role in optimizing system performance, improving energy efficiency and promoting sustainable engineering practices during the natural convection process.

To the best of the authors’ knowledge, this study is the first ever attempted detailed investigation of heat transfer and entropy generation phenomena flow parameter ranges in an inverted T-shaped porous enclosure under a uniform magnetic field and thermal radiation.

This study aims to understand why consumers continue to visit physical stores despite the rise in mobile shopping and online channels. Mobile shopping has changed how consumers shop, allowing them to easily switch between channels. However, physical stores continue to remain significant because some consumers still prefer them, challenging the belief that online markets always surpass offline markets. To serve their needs effectively, retailers must understand the motivations and behaviors of shoppers in both channels. Therefore, this study aims to explore why people cross the online channel to offline by examining their dissatisfaction with online shopping, using E-SERVQUAL variables.

This study uses a two-method approach that involves in-depth interviews to develop questions related to E-SERVQUAL variables and a survey to assess respondents’ likelihood of switching from online to offline. Data was collected from 203 participants.

The results indicate that dissatisfaction with the timeliness and condition of online shopping services is a significant factor driving consumers to switch to physical stores. This challenges the notion that online markets always surpass offline markets, emphasizing the continued significance of physical stores in the retail landscape.

This study recognizes the importance and relevance of physical stores in the retail environment while challenging the assumption that online markets always outperform brick-and-mortar markets. In terms of dissatisfaction and satisfaction, it is possible to identify under what circumstances dissatisfied consumers go from online to offline by considering the distribution channel migration phenomenon.