The profound era of cloud computing (CC) is revolutionizing Industry 5.0in which users have online access to network services including better, transparent user management and the capacity to gather and analyze data. The services of the cloud paradigm have been adopted by academia, industries, healthcare, smart homes, and other areas due to cost-efficient and on-demand resources for IoT applications, but security and privacy of patient data is a major issue with cloud paradigm. For intrusion detection systems (IDS), adversarial machine learning (AML) has been promising to secure individual IoT devices from various modern threats. Network traffic anomalies brought on by both well-known assaults and recently discovered attacks are typically detected by adversarial machine learning-based intrusion detection systems (AML-IDS) in real time. This research proposes a novel technique in cloud security enhancement based on consumer IoT utilizing AML techniques in smart healthcare. The security of cloud networks is enhanced using trust-based encryption cryptographic analysis. The healthcare data is analyzed using structure-based Markov sparse Bayesian neural networks in smart healthcare. The experimental results are improved up to 9% in terms of data transmission ratio, specificity, training accuracy, validation accuracy, and security analysis.

Designing implantable bioelectronic systems that continuously monitor physiological functions and simultaneously provide personalized therapeutic solutions for patients remains a persistent challenge across many applications ranging from neural systems to bioelectronic organs. Closed-loop systems typically consist of three functional blocks, namely, sensors, signal processors and actuators. An effective system, that can provide the necessary therapeutics, tailored to individual physiological factors requires a distributed network of sensors and actuators. While significant progress has been made, closed-loop systems still face many challenges before they can truly be considered as long-term solutions for many diseases. In this review, we consider three important criteria where materials play a critical role to enable implantable closed-loop systems: Specificity, Biocompatibility and Connectivity. We look at the progress made in each of these fields with respect to a specific application and outline the challenges in creating bioelectronic technologies for the future.

Purpose: The main purpose of this research is to understand how the sudden shift to work from home (WFH) after the onset of the COVID-19 pandemic has caught several sales organizations underprepared and ill-equipped to combat emergent challenges. In this research, the authors provide initial evidence into how the WFH arrangement impacts salespeople and sales organizations. Specifically, this research is guided by two objectives: to understand how the shift to WFH environment is affecting salespeople, and to explore how organizations can mitigate dysfunctional effects of the shift to WFH practices and enhance salespeople’s commitment toward this new reality. Design/methodology/approach: The authors did preliminary in-depth interviews with 13 executives operating in the business-to-business (B2B) space to identify themes that reflected the reality faced by B2B sales organizations when transitioning to WFH. The authors then conducted a quantitative study involving a survey with 130 B2B salespeople. Findings: The findings from the qualitative research suggested that the WFH situation is quite different from the more traditional remote selling situations that B2B salespeople are used to. More specifically, salespeople experienced more anxiety because of the WFH situations. This finding was supported in the empirical study done by the authors where stress associated with WFH and job insecurity had a significant impact on salesperson anxiety. Research limitations/implications: The study primarily used subjective responses of salespeople with no objective measures. Furthermore, this study is cross-sectional in nature. Future research should build on the present work to understand the long-term consequences of WFH and factor in customer responses to the same. The impact of increased use of technology in the sales process will need further attention, including the sales management implication for the same. Originality/value: Given the unforeseen nature of the COVID pandemic and how unprepared salespeople and sales organizations were to deal with it, this study is one of the first studies that documents the impact of WFH situations on salespeople.

The amalgamation of wearable sensor technologies and artificial intelligence (AI) presents a transformative paradigm for optimising athletic performance in real time. This paper explores the integration of cutting-edge sensors - including bioimpedance sensors, accelerometers, and gyroscopes - with advanced AI algorithms such as machine learning and decision support systems. By capturing diverse physiological, biomechanical, and environmental data, the proposed framework aims to offer personalized, actionable insights for athletes. This research synthesizes the current landscape of wearable sensor technology in sports and highlights the evolving role of AI in interpreting data for enhancing athletic performance. It delineates an innovative framework designed for real-time analysis, personalized feedback, and training optimization. The seamless interaction between sensors and AI models empowers athletes and coaches to make informed decisions, optimizing training regimens and minimizing injury risks. The paper discusses the practical implications, challenges, and ethical considerations associated with this integration, emphasizing its potential benefits in diverse sports disciplines. Results from real-world trials underscore the efficacy of the proposed framework in providing dynamic guidance to athletes, thereby augmenting their performance through tailored interventions.