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The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

The purpose of this paper is energy consumption and security. To extend the sensor’s life span, saving the energy in a sensor is important. In this paper, biosensors are implanted or suited on the human body, and then, transposition has been applied for biosensors for reducing the sensor distance from the sink node. After transposition path loss has been calculated, security is maintained and also compared the results with the existing strategies.

Nowadays, one of the most emergent technologies is wireless body area network (WBAN), which represents to improve the quality of life and also allow for monitoring the remote patient and other health-care applications. Traffic routing plays a main role together with the relay nodes, which is used to collect the biosensor’s information and send it towards the sink.

To calculate the distance and observe the position, Euclidean distance technique is used. Path loss is the main parameter, which is needed to reduce for making better data transmission and to make the network stability. Routing protocols can be designed, with the help of proposed values of sensors locations in the human body, which gives good stability of network and lifetime. It helps to achieve as the less deplete energy.

This scheme is compared with the two existing schemes and shows the result in terms of parameter path loss. Moreover, this paper evaluated a new method for improving the security in WBAN. The main goal of this research is to find the optimal sensor location on the body and select the biosensor positions where they can get less energy while transmitting the data to the sink node, increasing the life span in biosensors, decreasing memory space, giving security, controlling the packet complexity and buffer overflow and also fixing the damages in the existing system.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

“the purpose of this study/paper” or “this study/paper aims to” in the Purpose section of the Abstract. The integration of distributed generation (DG) to the utility grid is yet another approach to provide reliable and secured power.

The significant concern in this contemporary world are the day-to-day increasing power demand, lack of energy and increasing environmental pollution, which are threatening the existence of living things.

The research focus here is to adequacy and security in the grid-integrated hybrid distributed generation (DG) having photovoltaic (PV) and proton exchange membrane fuel cell.

PV system is a clean source of generation and suitable for many applications. Photovoltaic cell captures the energy from solar irradiation. To track the maximum power from PV, perturb and observe method is used. As it is intermittent in nature, integrating PV with fuel cell makes the hybrid source more reliable. Power electronic interfacing devices are used to integrate this hybrid DG source to microgrid. The simulation of this grid-connected hybrid DG is performed using Matlab/Simulink environment.

The purpose of this paper is to handle the anaphors through anaphora resolution in aspect-oriented sentiment analysis. Sentiment analysis is one of the predictive analytics of social media. In particular, the social media platform Twitter is an open platform to post the opinion by subscribers on contextual issues, events, products, individuals and organizations.

The sentiment polarity assessment is not deterministic to conclude the opinion of the target audience unless the polarity is assessed under diversified aspects. Hence, the aspect-oriented sentiment polarity assessment is a crucial objective of the opinion assessment over social media. However, the aspect-oriented sentiment polarity assessment often influences by the curse of anaphora resolution.

Focusing on these limitations, a scale to estimate the aspects oriented sentiment polarity under anaphors influence has been portrayed in this article. To assess the aspect-based sentiment polarity of the tweets, the anaphors of the tweets have been considered to assess the weightage of the tweets toward the sentiment polarity.

The experimental study presents the performance of the proposed model by comparing it with the contemporary models, which are estimating the sentiment polarity tweets under anaphors impact.

This paper aims to systematically review the literature on knowledge leakage through social networks in the past decade to find existing gaps, identify potential risk factors while, ultimately, proposing ways of mitigating these factors.

This study adopted Preferred Reporting Items for Systematic reviews and Meta-Analysis as guide for searching relevant scholarly publications. Subject-specific and -related research papers were obtained from three databases, namely, Scopus, Web of Science and EBSCOhost. The review data was generated from the search results while adopting specific criteria to either accept or reject a particular publication during the search process.

Technological, operational and human knowledge factors are some of the risks resulting from knowledge leakage. Highlights of the paper include strategies for mitigating these factors, including continuous training, creating awareness, banning social media usage at work and reinforcing nondisclosure policies. This study also found potential gaps from the literature, categorized as topical, geographical, industrial, theoretical, methodological and conceptual gaps while proposing ways of addressing these gaps using specific research questions. These questions set the direction for future studies on knowledge leakage and social networks.

Implications of the findings are laid out, particularly the idea of developing actionable managerial plans for preventing knowledge leakage from occurring in organizations in the first place. The systematic, rigorous, transparent and methodological procedures used throughout the entire research process strongly suggest that the findings and conclusions are legitimate. While the findings were not drawn arbitrarily, they potentially offer windows of opportunities for bridging the six potential gaps identified in this paper.

The purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.

The study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.

The results of the study present the researchers’ tendencies and preferences when designing their BSC network models.

The study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.

The study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.