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A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

In the new digital age, enterprises are facing an increasing global competition. In this paper, we first examine how Information Technology (IT) can play an important role in giving significant competitive advantage in the modern enterprises. The business value of IT is examined, as well as the limitations and the trade-offs that its applicability faces. Next, we present the basic principles for a successful IT strategy, considering the development of a long-term IT renovation plan, the strategic alignment of IT with the business strategy, and the adoption of an integrated, distributed, and interoperable IT platform. Finally, we examine how a highly functional and efficient IT organization can be developed.

Our methodological approach was based to the answers of the following questions: 1. Does IT still matter? 2. What is the business value created by IT along with the corresponding limitations and trade-offs? 3. How could a successful IT Strategy be build up? 4. How could an effective? T planning aligned with the business strategy be build up? 5. How could a homogenized and distributed corporate IT platform be developed? and finally, 6. How could a high-performance IT-enabled enterprise be build up?

The enterprises in order to succeed in the new digital area need to: 1. synchronize their IT strategy with their business strategy, 2. formulate a long-term IT strategy, 3. adopt IT systems and solutions that are implemented with elasticity, interoperability, distribution, and service-orientation. 4. keep a strategic direction towards the creation of an exceptional organization based on IT.

This paper is original with respect to the integrated approach the overall problem is examined. There is a prototype combined investigation of all perspectives for an effective enforcement of IT in a way that causes acceleration in competitive advantage when conducting business.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.

Amidst the COVID-19 pandemic, education has shifted to online teaching and learning. Interactivity is a crucial tool used to make online education effective. This study empirically examines the role of interactivity in higher education and its influence on students' behavioral outcomes, specifically focusing on soft skills and personality upgradation.

A quasi-experimental research design was carried out for post-graduate students undergoing a business communication course from four major institutions. For analysis, t-test, confirmatory factor analysis (CFA) and partial least squares structural equation modeling (PLS-SEM) have been employed. Experimental research has established the causal relationship between interactivity, personality and soft skill upgradation (SSU).

It was found that the theoretical structural model has a rational model-fit validity. Resultantly, practitioners may use prior knowledge of virtual community (VC) members to enhance web interactivity, thereby increasing social identity and social bonds in a group for more meaningful and effective delivery of online courses.

The major limitations lie in its context-dependent nature, predominantly influenced by the pandemic-induced mandatory online learning. The study's cross-sectional design also inhibits its ability to assess goal-directed behaviors over time, necessitating further longitudinal research.

The study is one of the pioneering pieces of research that examines the role of pre-defined grouping and enhanced web interactivity in VCs in the context of online learning, especially during the COVID-19 pandemic. Integrating theories of web interactivity, social bond theory (SBT) and social identity theory (SIT) provides a novel understanding of cognitive and social influences that drive meaningful online discussions and their impacts on knowledge enhancement and personality development. Its findings have implications for the design of effective online learning environments and e-learning pedagogy, contributing to the growing domain of information and communication technology (ICT)-enabled education.

This editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.

Utilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.

The conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.

The paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.

Bridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.

This study aims to explore differences in the usability of the Google Drive application based on demographic characteristics, computer skills and use frequency of Google Drive among public library users of Lahore.

The study adopted a quantitative approach and survey-based research method to achieve the study's objectives. The cloud usability model (user perspective) has been used as a theoretical lens to guide the study objectives. It comprises five dimensions of usability – capable, personal, reliable, valuable and secure.

The findings of the study revealed that the usability of Google Drive varied statistically significantly on the basis of depending on the respondents gender, age, academic qualification, computer skills and Google Drive use frequency.

It is a valuable study since it and adds knowledge to existing literature and has implications for practice.

The findings might be helpful for cloud support teams including Google Drive as they can notice the demographic and other differences among users' perceived usability of Google Drive and can enhance certain features of usability which leads attributes to increase its usage among users.

The authors aim to contribute to the understanding of the enduring underrepresentation of women in the IT industry by analysing media discourse triggered by a campaign intended to encourage women to join the IT industry.

Internet media coverage of the Little Miss Geek campaign in the UK was analysed as qualitative data to reveal systematic and coherent patterns contributing to the social construction of the role of women with respect to the IT industry and IT employment.

While ostensibly supporting women's empowerment, the discourse framed women's participation in the IT industry as difficult to achieve, focused on women's presumed “feminine” essential features (thus, effectively implying that they are less suitable for IT employment than men), and tasked women with overcoming the barrier via individual efforts (thus, implicitly blaming them for the imbalance). In these ways, the discourse worked against the broader aims of the campaign.

Campaigns and organisations that promote women's participation should work to establish new frames, rather than allowing the discourse to be shaped by the established frames.

The authors interpret the framing in the discourse using Bourdieu's perspective on symbolic power: the symbolic power behind the existing patriarchal order expressed itself via framing, thus contributing to the maintenance of that order. By demonstrating the relevance of Bourdieu's symbolic power, the authors offer a novel understanding of how underrepresentation of women in the IT sector is produced and maintained.

This study aims to develop a methodology for predicting the resilience of individual firms to economic crisis, using historical government data to optimize one of the most important and costly interventions that governments undertake, the huge economic stimulus programs that governments implement for mitigating the consequences of economic crises, by making them more focused on the less resilient and more vulnerable firms to the crisis, which have the highest need for government assistance and support.

The authors are leveraging existing firm-level data for economic crisis periods from government agencies having competencies/responsibilities in the domain of economy, such as Ministries of Finance and Statistical Authorities, to construct prediction models of the resilience of individual firms to the economic crisis based on firms’ characteristics (such as human resources, technology, strategies, processes and structure), using artificial intelligence (AI) techniques from the area of machine learning (ML).

The methodology has been applied using data from the Greek Ministry of Finance and Statistical Authority about 363 firms for the Greek economic crisis period 2009–2014 and has provided a satisfactory prediction of a measure of the resilience of individual firms to an economic crisis.

The authors’ study opens up new research directions concerning the exploitation of AI/ML in government for a critical government activity/intervention of high importance that mobilizes/spends huge financial resources. The main limitation is that the abovementioned first application of the proposed methodology has been based on a rather small data set from a single national context (Greece), so it is necessary to proceed to further application of this methodology using larger data sets and different national contexts.

The proposed methodology enables government agencies responsible for the implementation of such economic stimulus programs to proceed to radical transformations of them by predicting the resilience to economic crisis of the firms applying for government assistance and then directing/focusing the scarce available financial resources to/on the ones predicted to be more vulnerable, increasing substantially the effectiveness of these programs and the economic/social value they generate.

To the best of the authors’ knowledge, this study is the first application of AI/ML in government that leverages existing data for economic crisis periods to optimize and increase the effectiveness of the largest and most important and costly economic intervention that governments repeatedly have to make: the economic stimulus programs for mitigating the consequences of economic crises.

This study aims to investigate how organizations from different sectors interpret the meaning of business continuity management (BCM) in the light of the COVID-19 pandemic.

A survey was conducted to capture the views of organizations across five different sectors. The sample consisted of ten senior managers; two from the banking sector; two from the supply chain sector (agricultural and food supply chains); two from the tourism sector; two from the services sector; and two from the higher education sector. An interviewer-administered questionnaire was used to collect the data. One manager from each sector represented a local business/enterprise and the other represented an international business/enterprise.

It was found that different organizations/businesses understood BCM differently. Therefore, a variety of interpretations have been obtained.

This study sheds light on how different organizations understand BCM in times of crisis, such as the COVID-19 pandemic. By understanding the different interpretations, it becomes clearer whether or not these organizations have applicable business continuity plans in place.

This is the first study to investigate the different interpretations of the meaning of BCM across different business sectors. The majority of the existing studies on BCM discuss the process from the perspective of a single business or sector. The study was conducted during the COVID-19 pandemic, a period that witnessed prolonged and critical disruptions facing almost all businesses and organizations and which threatened the survival of some of them.

Bangladesh's ready-made garment (RMG) industry plays a vital role in the economic growth of this country. As the global trend in the fashion market has introduced a high-mix, low-volume ordering style, manufacturers are facing an increased number of changeovers in their production systems. However, most of the Bangladeshi RMG manufacturers are not yet ready to respond to such small orders and to improve the flexibility of their production systems. Consequently, the industry is falling behind in global market competition. Thus, this study aims to advance the current performance of RMG manufacturing operations to respond to the fast-fashion industry's challenges effectively using quick changeover.

In this study, a Single-Minute Exchange of Dies (SMED) is applied to attain quick changeover following the best practices of lean manufacturing.

This study examined the performance of the SMED technique to reduce changeover time in two case organisations. The changeover time was reduced by 70.76% from 434.56 min to 127.08 min and 42.12% from 2,664 min to 1,542 min for the case organisations, respectively. The results of this study show that companies require improved changeover times to address the demand for high-mix, low-volume orders.

This study will certainly guide practitioners of the RMG industry to adopt SMED to reduce changeover time to meet small batch production.