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This paper examines the association between the quality of management schools and sustainability and investigates whether ethical behavior of firms moderates relationship between the quality of management schools and sustainability.

The sample consists of 500 country-year observations over the period of 2014-2017. Sustainability is collected from the Global Sustainable Competiveness Index reports for 2014, 2015, 2016 and 2017, while the quality of management schools and ethical behavior of firms are collected from the Global Competiveness Report for the same years.

The findings of this study suggest that the quality of management schools is positively associated with sustainability. When testing for the moderating effect of ethical behavior of firms on the association between quality of management schools and sustainability, results show that the positive association becomes positive and more significant for countries where firms operate with high ethical behaviors, while the association becomes insignificant for settings where firms operate with low ethical behaviors. Findings also show that the quality of management schools and ethical behavior of firms play a complimentary role in improving sustainability.

The findings emphasize the role played by business schools and business ethics in improving sustainability. These results may have policy implications for governments aiming to improve sustainability by emphasizing on education for sustainable development in management schools’ 2019 programs enforcing standards dealing with business ethics and controlling firms’ 2019 compliance with them.

The findings of this study highlight the importance of education, as proxied by the quality of management schools, in the development of sustainable societies and economic systems. To the best of the author's knowledge, this is the first study that tries to empirically link business schools programs to sustainability efforts and how business ethics may affect this association.

The purpose of this study is to examine the effects of nitrogen (N) deposition on clonal growth in a rhizome clonal plant, Leymus chinensis (Trin.) Tzvel.

The study established seven N concentration gradients (0, 2, 4, 8, 16, 32 and 64 g N m⁻²) to simulate the continuous increase in N deposition for the cultivation of L. chinensis seedlings and assess the response mechanism of the cloned L. chinensis plant at different N levels by analyzing the aboveground and belowground plant appearance traits, parent ramets and daughter ramets of resource allocation and biomass allocation.

The results of this study showed that the different N treatment levels could promote clonal growth and had certain regularity under the seven treatments. The addition of N could significantly increase the ramet number, rhizome length, rhizome spacer length, biomass of mother ramets, daughter ramets and belowground L. chinensis population when the N addition was greater than 4 g m⁻²; however, the clonal growth ability of L. chinensis decreased and the rhizome length, ramet number, stem and leaf biomass of daughter ramets and stem biomass of mother ramets significantly decreased when the N addition was greater than 32 g N m⁻².

With global warming, atmospheric N deposition is increasing and it is of great significance to explore the response mechanism of different N levels for the growth of clone plants. This study provides basic data and a theoretical basis for the survival prediction of cloned plants under the background of a global climate change strategy and has important theoretical and practical significance for the scientific management of grasslands in the future.

The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations..

The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time.

The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates.

The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock.

To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

Forest as a vital natural resource in China plays an irreplaceable important role in safeguarding ecological security and human survival and development. Due to the vast territory, huge population and widespread forest landscape of China, forest management is a complex system involving massive data and various management activities. To effectively implement sustainable forest management, the big data technology has been utilized to analyze China’s forestry resources. Thus, the purpose of this paper is to clarify the role of big data technology in China’s forest management.

In this paper, the authors revisited the roles of big data in forest ecosystem monitoring, forestry management system development, and forest policy implementation.

It demonstrates that big data technology has a great potential in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection. However, to deepen the application of big data in forest management, several challenges still need to be tackled.

Thus, enhancing modern science and technology to improve big data, cloud computing, and information technologies and their combinations will contribute to tackle the challenges and achieve wisdom of forest management.

This study aims to empirically explore e-banking service quality and its impact on the e-banking loyalty through a mediating impact of e-banking satisfaction. The account holders of three domestic systemically important banks of Pakistan were surveyed during COVID-19 to examine the electronic services provided by these banks.

The data was collected through an adapted questionnaire by using emails and messaging applications. The database of a local marketing company in Pakistan was used, and 976 responses were included in the analysis. The structured equation modeling was used to test the propositions of study.

The findings delineate that reliability and website design proved to increase e-banking loyalty, particularly during COVID-19. The link between e-banking privacy and security and e-banking loyalty was proved as fully mediated by e-banking satisfaction; however, indirect effect of the reliability and website design with e-banking loyalty was partially mediated.

In strategic planning of e-banking mechanisms and the associated consumer behavior, the results of this study can be helpful for policymakers. In case of similar epidemics and natural calamities, consumers may depict similar behavior as shown during the lockdown and social distancing during COVID-19; hence, the study can help regulatory bodies in preparing their safety roadmap.

The mediating effect of e-banking satisfaction between privacy & Security and e-banking loyalty implies that customer give importance to secure e-banking platforms. There can be a variation in their loyalty because of privacy concerns. The application of cognitive–motivational–relational (CMR) theory in a relationship between e-banking service quality and e-banking loyalty with a mediating role of satisfaction is an emotional response to capture the behavioral changes during COVID-19. It enables researchers to understand the CMR theory concerning COVID-19 and e-banking perspective.

This paper aims to present a requirements analysis for the processing of water-based electrode dispersions in inkjet printing.

A detailed examination of the components and the associated properties of the electrode dispersions has been carried out. The requirements of the printing process and the resulting performance characteristics of the electrode dispersions were analyzed in a top–down approach. The product and process side were compared, and the target specifications of the dispersion components were derived.

Target ranges have been identified for the main component properties, balancing the partly conflicting goals between the product and the process requirements.

The findings are expected to assist with the formulation of electrode dispersions as printing inks.

Little knowledge is available regarding the particular requirements arising from the systematic qualification of aqueous electrode dispersions for inkjet printing. This paper addresses these requirements, covering both product and process specifications.

The COVID-19 pandemic caused global supply disruptions and shortages that resulted in countries battling over desperately needed (medical) supplies. In this mayhem, additive manufacturing (AM) provided relief to the strained healthcare systems and manufacturing environments by offering an alternative way to rapidly produce desired products. This study sheds light on how AM was used globally in response to the COVID-19 pandemic.

The study undertakes a systematic and content-centric review of 289 additively manufactured products made in response to the COVID-19 pandemic. Additionally, quantitative frequency-based text mining and various descriptive analyses were applied that support the investigation of the subject under regard.

Results show that AM was primarily used in the medical domain for the production of standard medical items, such as personal protective equipment (PPE) but also for non-obvious and new applications (e.g. swab simulator, rapid diagnostic kits, etc.). Also, certain paradigm shifts were observed, as the effective move to mass production and the mitigation of problems related to certification and standardization emerged as prominent management prospects. Nevertheless, various obstacles arose and remained in the path of lasting AM success, especially with respect to print quality, raw material supply and technological versatility.

Due to the actuality of the topic under investigation, no comparable study has so far been conducted. The systematic review provides a conclusive and precise foundation for further analysis and subsequent discussions. Additionally, no comparable study mapping such a wide array of different AM products exists today.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

Cloud computing originated in central data centers that are connected to the backbone of the Internet. The network transport to and from a distant data center incurs long latencies that hinder modern low-latency applications. In order to flexibly support the computing demands of users, cloud computing is evolving toward a continuum of cloud computing resources that are distributed between the end users and a distant data center. The purpose of this review paper is to concisely summarize the state-of-the-art in the evolving cloud computing field and to outline research imperatives.

The authors identify two main dimensions (or axes) of development of cloud computing: the trend toward flexibility of scaling computing resources, which the authors denote as Flex-Cloud, and the trend toward ubiquitous cloud computing, which the authors denote as Ubi-Cloud. Along these two axes of Flex-Cloud and Ubi-Cloud, the authors review the existing research and development and identify pressing open problems.

The authors find that extensive research and development efforts have addressed some Ubi-Cloud and Flex-Cloud challenges resulting in exciting advances to date. However, a wide array of research challenges remains open, thus providing a fertile field for future research and development.

This review paper is the first to define the concept of the Ubi-Flex-Cloud as the two-dimensional research and design space for cloud computing research and development. The Ubi-Flex-Cloud concept can serve as a foundation and reference framework for planning and positioning future cloud computing research and development efforts.