File transfer is always handled by a separate service, sometimes it is a third-party service in videoconferencing. When sending files during a video session, file data flow and video stream are independent of each other. Encryption is a mature method to ensure file security. However, it still has the chance to leave footprints on the intermediate forwarding machines. These footprints can indicate that a file once passed through, some protocol-related logs give clues to the hackers' later investigation. This work proposes a file-sending scheme through the video stream using blockchain and steganography. Blockchain is used as a file slicing and linkage mechanism. Steganography is applied to embed file pieces into video frames that are continuously generated during the session. The scheme merges files into the video stream with no file transfer protocol use and no extra bandwidth consumed by the file to provide trackless file transmission during the video communication.

At present, there is a need for an authentic and fast approach to certificate verification. Which verifies and authenticates the certificates to reduce the extent of duplicity and time. An academic certificate is significant for students, the government, universities, and employers. Academic credentials play a vital role in the career of students. A few people manipulate academic documents for their benefit. There are cases identified where people produced fake academic certificates for jobs or higher education admission. Various research works are developing a secure model to verify genuine academic credentials. This research article proposed a new security model which contains several security algorithms such as timestamps, hash function, digital signature, steganography, and blockchain. The proposed model issues secure digital academic certificates. It enhanced security measures and automated educational certificate verification using blockchain technology. The advantages of the proposed model are automated, cost-effective, secured, traceable, accurate, and time-saving.

In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application's needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image's security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.

Image encryption is a critical and attractive issue in digital image processing that has gained approval and interest of many researchers in the world. A proposed hybrid encryption method was implemented by using the combination of the Nahrain chaotic map with a well-known optimised algorithm namely the grey wolf optimisation (GWO). It was noted from analysing the results of the experiments conducted on the new hybrid algorithm, that it gave strong resistance against expected statistical invasion as well as brute force. Several statistical analyses were carried out and showed that the average entropy of the encrypted images is near to its ideal information entropy.

Screen-shooting will lead to information leakage. Anti-screen-shooting watermark, which can track the leaking sources and protect the copyrights of images, plays an important role in image information security. Due to the randomness of shooting distance and angle, more robust watermark algorithms are needed to resist the mixed attack generated by screen-shooting. A robust digital watermarking algorithm that is resistant to screen-shooting is proposed in this paper. We use improved Harris-Laplace algorithm to detect the image feature points and embed the watermark into the feature domain. In this paper, all test images are selected on the dataset USC-SIPI and six related common algorithms are used for performance comparison. The experimental results show that within a certain range of shooting distance and angle, this algorithm presented can not only extract the watermark effectively but also ensure the most basic invisibility of watermark. Therefore, the algorithm has good robustness for anti-screen-shooting.

The current study has outlined slow fashion (SF) research trends and created a future research agenda for this field. It is a thorough analysis of the literature on slow fashion. Numerous bibliometric features of slow fashion have been discussed in the paper. This study comprises 182 research articles from the Scopus database. The database was utilised for bibliometric analysis. To identify certain trends in the area of slow fashion, a bibliometric study is done. For bibliometric analysis, the study employed R-software (the Biblioshiny package). Here, VOSviewer software is used to determine the co-occurrence of authors, countries, sources, etc. The study has outlined the gap that still exists in the field of slow fashion. Here, the research outcome strengthens the domain of slow fashion for sustainable consumption. The study findings will be useful for policymakers, industry professionals, and researchers.

Access to quality healthcare is a fundamental right but unfortunately, India suffers from gender disparities in healthcare access. Telemedicine has the potential to improve access to healthcare services for women by eliminating traditional barriers. Therefore, our research aims to investigate the factors influencing the adoption of telemedicine among Indian women. This study has collected 442 responses and analysed them through structural equation modelling. The result indicates a strong and positive connection between the willingness to adopt telemedicine services and factors like performance expectancy, perceived benefits, e-health literacy, and perceived reliability. Notably, perceived reliability emerges as the most impactful predictor, closely followed by perceived benefits, while factors like effort expectancy and user resistance show no significant influence. This underscores the pivotal role of reliability and perceived benefits in shaping women's inclination toward adopting telemedicine. The study provides practical insights for telemedicine providers and policymakers to customise strategies and policies for effective promotion.

This study examines Form 10-K filing lags of US firms during the COVID-19 pandemic in 2020-2021. The findings suggest that filing lags relate negatively to firm size, profitability, hiring Big4 auditors, and filing status, but positively to ineffective internal control, ineffective disclosure control, and going concern opinion. Large accelerated and accelerated filers had shorter filing lags, and non-accelerated filers had longer filing lags in 2020-2021 than 2018-2019. Further analysis provides mild evidence that Big4 auditors contributed to the filing lag reduction in 2020-2021, echoing the view that adopting advanced audit technologies allows Big4 auditors to respond better to the external shocks brought by the pandemic.

This systematic literature study employed bibliometric analysis to identify workplace mindfulness-related methods and practices in literature published from 2000 to 2020 by leading nations, institutions, journals, authors, and keywords. We also assessed the impact of workplace mindfulness research papers. Scopus analysis tools provided a literature report for 638 Scopus articles used in the study. Using VOSviewer, leading nations, institutions, articles, authors, journals, and keyword co-occurrence network maps were constructed. PRISMA was used to identify 56 publications to recognise workplace mindfulness literature's significant achievements. The research's main contribution is a deep review of neurological mindfulness and psychological measuring tools as workplace mindfulness tool categories. The study is the first to use the PRISMA technique to capture the essential contributions of workplace mindfulness papers from 2000 to 2020.

The video game industry (VGI) has evolved considerably, transitioning from a niche market to a substantial sector. The VGI's magnitude and the societal implications tied to video game consumption have naturally piqued the interest of scholars in marketing and consumer behaviour. This research serves a dual purpose: firstly, it consolidates existing VG literature by evaluating articles, concepts, and methodologies, systematically tracing their evolution; secondly, it outlines potential directions and implications for forthcoming research. Within this literature, a predominant focus lies on articles investigating purchase decisions concerning VGs, followed by those exploring the integration of video game consumption into broader social contexts. Notably, a limited number of articles delve into player-game interactions and experiences within gaming worlds. This imbalance can be attributed to the fact that such inquiries are often suited to psychology and multidisciplinary journals, while the marketing discipline has predominantly addressed the VGI from a marketing management standpoint.

Although artificial intelligence provides a new method to gather, process, analyse data, generate insights, and offer customised solutions, such methods could change how marketers deal with customers, and there is a lack of literature to portray the application of artificial intelligence in marketing. This study aims to recognise and portray the use of artificial intelligence from a marketing standpoint, as well as to provide a conceptual framework for the application of artificial intelligence in marketing. This study uses a systematic literature review analysis as a research method to achieve the aims. Data from 142 articles were extracted from the Scopus database using relevant search terms for artificial intelligence and marketing. The systematic review identified significant usage of artificial intelligence in conversational artificial intelligence, content creation, audience segmentation, predictive analytics, personalisation, paid ads, sales forecasting, dynamic pricing, and recommendation engines and the bibliometric analysis produced the trend in co-authorship, citation, bibliographic coupling, and co-citation analysis. Practitioners and academics may use this study to decide on the marketing area in which artificial intelligence can be invested and used.

Supply chain management practices are vital for success and survival in today's competitive Indian retail market. The advent of COVID-19 pandemic necessitates a digital disruption in retail supply chain management centred on efficient technology like blockchain in order to enhance supply chain performance. The present research aims to decipher the nature of associations between supply chain management practices, blockchain technology adoption and supply chain performance in retail firms. The research is based on primary survey of specific food and grocery retailers operating on a supermarket format stores in two Indian cities. The findings pointed towards the presence of significant and positive association of all the constructs with each other. Moreover, the mediating role of blockchain technology adoption was also revealed, i.e., it partially mediates the effects of supply chain management practices on supply chain performance.

This study investigates the effects of social media marketing efforts (SMME) on smartphone brand identification, attachment, association, and willingness to pay premium prices. A survey of 320 smartphone users who followed official social media handles managed by smartphone companies was conducted for this purpose. PLS-SEM was used to analyse the collected data. The findings demonstrated importance of SMME dimensions. According to the study's findings, the smartphone brand's SMMEs had significant impact on brand identification, brand association, and brand attachment. The results revealed that SMMEs had significant impact on willingness to pay the premium price. The findings also show that brand identification, attachment, and association mediated the relationship between SMMEs and willingness to pay a premium price. The findings of this study will be useful in developing social media marketing strategies for smartphones. This study demonstrates the use of social media marketing to promote mobile phones, particularly in emerging markets.

Word of mouth (WOM) and electronic word of mouth (eWOM) are very effective and important communication tools to persuade consumers for purchasing the products/services. These become more significant with products that are difficult to assess before consumption, e.g., hospitality. The tourism industry is reviving, and the consumer is conscious when booking a particular destination. Thus, it is important to understand how WOM and eWOM are impacting the various factors in distinct ways while choosing the tourist destination. The seven factors identified, for the present study, are channel engagement, expertise, homophily, resource helpfulness, source credibility, tie-strength, and trustworthiness. The PLS-SEM was used to test the theoretical model of this study. The study shows that both WOM and eWOM impact an individual in different ways. The expertise of the reviewer is the most important factor in the case of WOM and channel engagement is the most significant factor for eWOM. Resource helpfulness is common for both WOM and eWOM.

The emergence of digital technology and the increasing prevalence of smartphones have promoted innovations in payment options available in finance and consumption markets. Banks providing mobile payment must ensure the information security. Inadequate security control leads to information leakage, which severely affects user rights and service providers' reputations. This study uses control objectives for Information and Related Technologies 4.1 as the mobile payment security control framework to examine the emergent field of mobile payment. A literature review is performed to compile studies on the safety risk, regulations, and operations of mobile payments. In addition, the Delphi questionnaire is distributed among experts to determine the practical perspectives, supplement research gaps in the literature, and revise the prototype framework. According to the experts' opinions, 59 control objectives from the four domains of COBIT 4.1 are selected. The plan and organise, acquire and implement, deliver and support, and monitor and evaluate four domains comprised 2, 5, 10, and 2 control objectives that had mean importance scores of > 4.50. Thus, these are considered the most important objectives by the experts, respectively. The results of this study can serve as a reference for banks to construct secure frameworks in mobile payment services.

Despite the considerable growth potential predicted for mobile games, little research explored what motivates users to be sticky and make purchases in the mobile game context. Drawing on uses and gratifications theory (UGT), this study evaluates the influencing effects of players' characteristics (i.e., individual gratification and individual situation) and the mobile game structure (i.e., presence and governance) on players' mobile game behaviour (i.e., stickiness and purchase intention). Specifically, the model was extended with factors of the individual situation and governance. After surveying 439 samples, the research model was examined using the Partial least squares structural equation modelling (PLS-SEM) approach. The results indicate that stickiness is a crucial antecedent for users' in-game purchase intention. The individual situation plays an essential role in influencing user gratification, and individual gratification is the most vital criterion affecting stickiness. Finally, except for incentives, presence, and integration positively affect stickiness. This study provides further insights into both mobile game design and governance strategies.

The prevalence of social media transforms e-business into social commerce and facilitates consumers' engagement in cross-cultural social commerce. However, social commerce operations encounter unpredictable challenges in cross-cultural business environment. It is vital to further investigate how contextual elements affect consumers' trust and their engagement when they are exposed to the complexity of cross-cultural business environment. The stimuli-organism-response paradigm is employed to examine how the two dimensions of social media usage influence consumers' engagement in cross-cultural social commerce. The current study surveyed 2,058 samples from 135 countries, and the regression analysis results illustrate the mechanism whereby informational and socialising usage of social media positively influences consumers' engagement in social commerce through consumers' trust toward social commerce websites. Additionally, the associations between two aspects of social media usage and consumers' trust towards social commerce are negatively moderated by cultural distance. Both theoretical and practical implications are also discussed.

A metaverse is a virtual shared space connected to the real world, an alternative reality that enables economic activities, exchanges, and transactions as well as formation of relationships between user avatars and non-player characters (NPCs). Initial experiences of the metaverse were not very satisfactory; new virtual world metaverses may or may not survive as information services or platforms. The purpose of this empirical study is to identify the characteristics of a virtual world metaverse and their effects on intention to continue usage of the platform. Considering the metaverse as a new type of user experience and a powerful mode of communication, we examine the mediating role of these characteristics according to Pine and Gilmore's (1998) experience economy theory, which enriches our understanding of the factors affecting the success of a metaverse. In addition, since social interaction is important in metaverses, we extend Pine and Gilmore's experience economy model by including Schmitt's (2011) relate experience for better understanding.

Drawing on the MOA framework, this study establishes a research model that explains the usage intention of robo-advisors. In the model, three predictors that consist of technology relative advantage, technology herding, and technology familiarity influence usage intention of robo-advisors directly and indirectly via the partial mediation of trust. At the same time, the effects of the three predictors on trust are hypothetically moderated by learning goal orientation and perceived performance risk respectively. Statistical analyses are provided using the data of working professionals from the insurance industry in Taiwan. Based on its empirical findings, this study discusses important theoretical and practical implications.

TCP BBR is one of the most powerful congestion control algorithms. In this article, we provide a comprehensive review of BBR analysis, expanding on existing knowledge across various fronts. Utilising ns3 simulations, we evaluate BBR's performance under diverse conditions, generating graphical representations. Our findings reveal flaws in the probe's RTT phase duration estimation and unequal bandwidth sharing between BBR and CUBIC protocols. Specifically, we demonstrated that the probe's RTT phase duration estimation algorithm is flawed and that BBR and CUBIC generally do not share bandwidth equally. Towards the end of the article, we propose a new improved version of TCP BBR which minimises these problems of inequity in bandwidth sharing and corrects the inaccuracies of the two key parameters RTprop and cwnd. Consequently, the BBR' protocol maintains very good fairness with the Cubic protocol, with an index that is almost equal to 0.98, and an equity index over 0.95.

The performance of a building's internal environment, which includes the air temperature, lighting and acoustics, is what determines the quality of the environment inside the building. We present a thermal model for achieving thermal comfort in buildings that makes use of a multimodal analytic framework as a solution to this challenge. In this study, a multimodal combination is used to evaluate several temperature and humidity sensors as well as an area image. Additionally, a CNN and LSTM combination is used to process the image and sensor data. The results show that heating setback and interior set point temperatures, as well as mechanical ventilation based on real people's presence and CO<SUB align=right>2 levels, are all consistently reduced when ICT-driven intelligent solutions are used. The CNN-LSTM model has a goodness of fit that is 0.7258 on average, which is much higher than both the CNN (0.5291) and LSTM (0.5949) models.

This paper presents a Scalable Video Coding (SVC) system over multicast Software-Defined Networking (SDN), which focuses on, transmission management for the sender-receiver model. Our approach reduces bandwidth usage by allowing the receiver to select various video resolutions in a multicast group, which helps avoid a video freezing issue during bandwidth congestion. Moreover, the SVC Multiple Sessions Transmission Bandwidth thresholds Quantised Level Balance (SVC-MST BWQLB) routes different layers of the SVC stream using distinct paths and reduces storage space and bandwidth congestion problems in different video resolutions. The experimental results show that the proposed model provides better display quality than the traditional Open Shortest Path First (OSPF) routing technique. Furthermore, it reduced transmission delays by up to 66.64% for grouped resolutions compared to SVC-Single Session Transmission (SVC-SST). Additionally, the modified Real-time Transport Protocol (RTP) header and the sorting buffer for SVC-MST are proposed to deal with the defragmentation problem.

Differential privacy is usually applied to location privacy protection scenarios, which confuses real data by adding interference noise to location points to achieve the purpose of protecting privacy. However, this method can result in a significant amount of redundant noisy data and impact the accuracy of the location. Considering the security and practicability of location data, an effective differential privacy protection method of location data based on perturbation loss constraint is proposed. After applying the Laplace mechanism under the condition of differential privacy to perturb the location data, the Savitzky-Golay filtering technology is used to correct the data with noise, and the data with large deviation and low availability is optimised. The introduction of Savitzky-Golay filtering mechanism in differential privacy can reduce the error caused by noise data while protecting user privacy. The experiments results indicate that the scheme improves the practicability of location data and is feasible.

Constrained programming is the concept used to select possible alternatives from an incredibly diverse range of candidates. This paper proposes an AI-assisted Backtracking Scheme (AI-BS) by integrating the generic backtracking algorithm with Artificial Intelligence (AI). The detailed study observes that the extreme dual ray associated with the infeasible linear program can be automatically extracted from minimum unfeasible sets. Constraints are used in artificial intelligence to list all possible values for a group of variables in a given universe. To put it another way, a solution is a way of assigning a value to each variable that these values satisfy all constraints. Furthermore, this helps the study reach a decreased search area for smart backtracking without paying high costs. The evaluation results exhibit that the IB-BC algorithm-based smart electricity schedule controller performs better electricity bill during the scheduled periods than comparison approaches such as binary backtracking and binary particle swarm optimiser.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the Multi-Agent Deep Deterministic Policy Gradient-based Traffic Cyclic Signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33 s and 27.18 m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.

To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

We introduce a unique inductive bias to improve the reconstruction quality of Neural Radiance Fields (NeRF), NeRF employs the Fourier transform to map 3D coordinates to a high-dimensional space, enhancing the representation of high-frequency information in scenes. However, this transformation often introduces significant noise, affecting NeRF's robustness. Our approach allocates attention effectively by segregating channels within NeRF using attention-based gating units. We conducted experiments on an open-source data set to demonstrate the effectiveness of our method, which leads to significant improvements in the quality of synthesised new-view images compared to state-of-the-art methods. Notably, we achieve an average PSNR increase of 0.17 compared to the original NeRF. Furthermore, our method is implemented through a carefully designed special Multi-Layer Perceptron (MLP) architecture, ensuring compatibility with most existing NeRF-based methods.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

5G mobile communication systems are an in-depth fusion of multi-radio access technologies characterised with frequent handover between cells. Handover management is a particularly challenging issue for 5G networks development. In this article, a novel optimised handover framework is proposed to find the optimal network to connect with a good quality of service in accordance with the user's preferences. This framework is based on an extension of IEEE 802.21 standard with new components and new service primitives for seamless handover. Moreover, the proposed vertical handover process is based on an adaptive heuristic model aimed at achieving an optimised network during the decision-making stage. Simulation results demonstrate that, compared to other existing works, the proposed framework is capable of selecting the best network candidate accurately based on the quality-of-service requirements of the application, network conditions, mobile terminal conditions and user preferences. It significantly reduces the handover delay, handover blocking probability and packet loss rate.

We propose that technological, service-dominant and design logics must interplay for an IT artefact to succeed. Based on data from a project aiming at a B2B platform for manufacturing small and medium enterprises (SMEs) in Europe, we explore these three logics in an agile software design context. By using an inductive approach, we theorise about what is needed for the alignment of the three logics. We contribute with a novel theoretical lens, the Framework for Adaptive Space. We offer insights into the importance of continuously reflecting on all three logics during the agile software design process to ensure mutual understanding among the agile team and the B2B platform end-users involved.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

The spread of COVID-19 results in disruption, uncertainty, complexity, and ambiguity in all businesses. Employees help companies achieve their aims. To manage human resources sustainably, analyse organisational strategy. This thorough research study attempts to find previously unidentified challenges, cutting-edge techniques, and surprising decisions in human resource management outside of healthcare organisations during the COVID-19 pandemic. The narrative review examined corporate human resource management measures to mitigate COVID-19. Fifteen publications were selected for the study after removing duplicates and applying the inclusion and exclusion criteria. This article examines HR's COVID-19 response. Human resource management's response to economic and financial crises has been extensively studied, but the COVID-19 pandemic has not. This paper reviewed the literature to reach its goal. The results followed the AMO framework for human resource policies and procedures and the HR management system. This document suggests COVID-19 pandemic-related changes to human resource management system architecture, policies, and practises. The study created a COVID-19 pandemic human resource management framework based on the literature. The COVID-19 pandemic had several negative effects, including social and behavioural changes, economic shock, and organisational disruption.

The banking sector has experienced a substantial technological shift that has opened up new and better opportunities for its customers. Based on their technological expenditures, the study assessed the two biggest public Indian banks and the two biggest private Indian banks. The most crucial statistical techniques used to demonstrate the aims are realistic are bivariate correlations and ordinary least squares. This work aims to establish a connection between research and a technology index that serves as a proxy for operational efficiency. The results show that for both public and private banks, the technology index positively influences operational efficiency metrics like IT costs, marketing costs, and compensation costs. This suggests that when the technology index increases, so do IT, marketing, and compensation costs, even though it has been shown that the technology index favourably improves operational efficiency measures like depreciation and printing. This means that the cost to banks is high despite greater investment in technology for these activities.

The proliferation of technology and communication has resulted in increased digitalisation that includes digital payments. This study is aimed at unravelling the relationship between awareness of individuals about the digital payment system and customer satisfaction with digital payments. Two models were developed in this study. First model considers awareness → usage pattern → customer satisfaction. Second model considers usage pattern → customer satisfaction → perception of digital payments. These two alternative models were tested by collecting data from 507 respondents in southern India was analysed using the structural equation modelling. The results indicate that usage pattern acted as a mediator between awareness and satisfaction, and satisfaction acted as a mediator between usage pattern and consumers' perception of digital payments. The implications for theory and practice are discussed.

Numerous Indian economic sectors have been impacted by the COVID-19 epidemic, with many being forced to the verge of extinction. As a result, this essay analyses the importance of supply chains for grapes and the manufactured goods made from them, including beverages and currants, in a specific state that happens to be India's top grape-producing region. In order to identify the sites of rupture brought on by the pandemic and to recommend policy changes to create a resilient system, a value chain analysis is performed. Value chain management has emerged as one of the key strategies businesses use today to boost productivity and costs when they are up against greater rivalry in the marketplace, however, with several new challenges, such as concerns over security, environmental protection, compensation, and business accountability. According to the value chain study, the level of value addition for intermediary agents, such as pre-harvest contractors, has increased after COVID-19 at the expense of farmers. Various policy approaches are explained to enhance the grape value chain using the knowledge gained from Porter's value chain results and supply and demand shocks.

While digitalisation reforms aiming to enhance the quality of public services were put in place, most stakeholders in developing countries still use paper-based-tendering processes, which are associated with increased costs. To overcome these problems, calls to adopt e-bidding have recently emerged. This study aims to explore the readiness of Moroccan SMEs to adopt e-bidding. To achieve this goal, we proposed an integrated framework combining the TAM and UTAUT models to examine the predictors of SMEs' intention to adopt e-bidding. We empirically tested the conceptual model using a partial least squares (PLS) estimation based on data from 210 SMEs. Our results suggest that effort expectancy, facilitating conditions, and social influence as the key factors influencing SMEs intention to adopt e-bidding. We also suggest firm size as a significant moderator. This will help in improving SMEs' user experience and will also allow a better implementation of e-bidding in Morocco and similar contexts.

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

In the era of big data, enterprises have accumulated a large amount of research and development data. Effective management of their precipitated data and safe sharing of data can improve the collaboration efficiency of research and development personnel, which has become the top priority of enterprise development. This paper proposes to use blockchain technology to assist the collaboration efficiency of enterprise R&D personnel. Firstly, the multi-chain blockchain platform is used to realise the data sharing of internal data of enterprise R&D data department, project internal data and enterprise data centre, and then the process of construction of multi-chain structure and data sharing is analysed. Finally, searchable encryption was introduced to achieve data retrieval and secure sharing, improving the collaboration efficiency of enterprise research and development personnel and maximising the value of data assets. Through the experimental verification, the multi-chain structure improves the collaboration efficiency of researchers and data security sharing.

The utilisation of big data presents significant opportunities for businesses to create value and gain a competitive edge. This capability enables firms to anticipate and uncover information quickly and intelligently. The author introduces a human resource scheduling optimisation strategy using a parallel network fusion structure model. The author's approach involves designing a set of network structures based on parallel networks and streaming media, enabling the macro implementation of the enterprise parallel network fusion structure. Furthermore, the author proposes a human resource scheduling optimisation method based on a parallel deep learning network fusion structure. It combines convolutional neural networks and transformer networks to fuse streaming media features, thereby achieving comprehensive identification of the effectiveness of the current human resource scheduling in enterprises. The result shows that the macro and deep learning methods achieve a recognition rate of 87.53%, making it feasible to assess the current state of human resource scheduling in enterprises.