This study comprehensively evaluates the impact of remote work intensity on employee well-being, productivity, and sustainable practices within the IT sector, utilizing a cross-sectional online survey of 1003 employees. Findings reveal that remote work consistently boosts self-rated health, enhances perceived productivity, and promotes the adoption of sustainable workplace practices, with these benefits largely consistent across gender and most age groups. However, its effect on perceived stress is complex and significantly age-dependent, showing increased stress for younger employees (under 25) while mid-career professionals (26–35) experience stress reduction. Perceived stress did not emerge as a statistically significant mediator in the remote work-productivity relationship, suggesting that positive effects on productivity are primarily driven by direct mechanisms such as increased autonomy and flexibility. This research contributes to the Job Demands-Resources and Self-Determination Theory by illuminating how digital work demands and psychological needs are experienced heterogeneously across demographics in the remote context. Practical implications emphasize the need for differentiated stress management strategies tailored to younger employees, as well as a broader promotion of remote work, to enhance sustainable behavior within organizations. Methodologically, the study highlights the value of utilizing large, non-probability datasets, along with carefully constructed proxy scales, and proposes the future integration of AI-powered analytics for deeper insights.

The smart environment hides numerous security challenges that need to be addressed promptly. Smart cities have emerged as a novel concept, integrating emerging technologies and data-driven solutions to improve urban living conditions. Traffic surveillance cameras at intersections enable continuous traffic monitoring and rapid incident detection, optimizing signal timing to improve road safety and reduce traffic congestion and travel delay. These cities present new challenges for the police force, forcing them to blend into the environment. The paper proposes novel hierarchical Police Internet of Things (PIoT) concepts that should enable and secure timely, high-priority policing forecasting and decision-making processes in smart cities. Hierarchical edge, fog, and cloud computing were presented according to the police decision-making process. This concept is carefully developed to improve the timeliness of predictive policing, planning, management, and decision-making using artificial intelligence and fuzzy logic. The proposed vertical PIoT concept is supported by vertical data processing. In hierarchical computing, machine learning models for time series prediction and fuzzy-logic-based decision-making are applied to enable comprehensive analysis in a smart environment. Two case studies dealing with crime and traffic issues are presented in detail.

Intelligent automation is a core component of Industry 4.0, enabling artificial intelligence (AI) systems to support or execute operational and managerial decisions in real time. In high-risk industrial environments such as mining and metallurgy, real-time decision-making improves efficiency but also raises critical challenges related to trust, explainability, human oversight, and institutional accountability. This study proposes PRIME–INSPECT, a two-layer socio-technical framework designed to support trustworthy AI-driven real-time decision-making. The PRIME (predict, regulate, interpret, mitigate, execute) layer formalizes the operational decision flow, embedding control mechanisms, uncertainty quantification, and explainability into the automation pipeline. The INSPECT (integrity, navigability, supervisory control, policy maturity, ethical compliance, collaboration, trust calibration) layer defines the organizational and governance conditions required for safe deployment. The framework is conceptually developed through a structured literature synthesis and supported by exploratory empirical grounding through stakeholder perceptions from IT and top management participants, alongside an illustrative industrial use case intended to demonstrate conceptual applicability rather than engineering performance validation. The findings highlight the importance of aligning operational AI processes with institutional safeguards to support calibrated trust and responsible automation. The empirical component is intended to provide conceptual and organizational grounding of framework dimensions rather than quantitative validation of predictive performance. PRIME–INSPECT provides a structured architecture for designing and governing AI-enabled real-time decision systems in high-risk industrial contexts.

AI-native decision systems — in which generative artificial intelligence generates, executes, and adapts decisions in real time — are shifting the centre of gravity of analytical effort. Whereas the principal challenge used to be the extraction of insight, it is now becoming the validation of AI outputs while action is still being taken upon them. Existing approaches address this problem only partially: explainable artificial intelligence and governance frameworks treat validation as an ex post layer; reinforcement learning reduces it to a single scalar reward; control theory and the broader cybernetic tradition formalise the closed loop but neglect its sociotechnical constraints; while the PRIME–INSPECT framework establishes a governance foundation, yet leaves real-time validation implicit. This paper proposes a validation-centric architecture that links the GAVA decision loop (Generate–Act–Validate– Adapt), introduced here, with the PRIME–INSPECT framework and elevates validation to a central analytical function comprising four subdimensions: statistical, operational, cognitive, and governance-related. The proposed architecture is empirically examined on a dual sample of IT professionals and top-management representatives, using descriptive statistics, multiple regression, mediation, moderation, and structural equation modelling. The results confirm the central role of trust, the negative effect of perceived risk, and the importance of top management support, while robustness checks separated by the IT and TMT subsamples further strengthen the structural model. Taken as a whole, the findings support the view that validation should be treated as a first-order organisational stage in AI-native decision systems.

National electronic voting systems require robust auditability, non-repudiation, forward secrecy, and fault tolerance under predictable but highly variable workloads. Existing protocols within permissioned blockchain networks usually favor low latency or long-lasting identities of business entities, which are assumptions that do not fully correspond to adversarial election environments. This paper presents a feasibility study of a time-slotbased permissioned blockchain architecture designed for state elections involving semi-trusted but mutually adversarial validators. The basic mechanism of the system is a block authentication scheme with advanced security, based on ephemeral key pairs: validators publish the public key for block N+1 within block N, after which they use the corresponding private key once to sign the next block and immediately delete it permanently. As part of the research, a multi-node prototype was implemented as a customized infrastructure of permissioned blockchain in the Node.js environment, with the application of Ed25519 digital signatures, explicit block finalization, peer-to-peer communication between validators and full signature verification by all participants. Empirical evaluation in a simulated wide-area network (WAN) environment shows a stable throughput of approximately 274 transactions per second, with a median finalization (p50) of 10.7 seconds and a 99th percentile (p99) of under 21 seconds. A projection on an election scenario involving six million voters during the 13:00 voting period indicates an average safety factor load of approximately 2.1. The presented results of the proposed model should be interpreted as a proof of concept obtained in a controlled prototype environment, and not as a complete validation of the production capacity on a geographically distributed election infrastructure. The findings indicate technical feasibility under defined assumptions and demonstrate how domain-specific transaction clustering, ephemeral key authentication, and explicit validator signatures can support an auditable consensus layer for electronic voting. Out of scope for this current study are client-side privacy protection, coercion resistance, and full end-to-end ballot secrecy. Our structural architecture explicitly hardens the consensus and ledger layers instead.