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Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.

This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study explores how ML techniques optimize maintenance by predicting faults, estimating remaining useful life (RUL), and reducing operational downtime. Sixty peer-reviewed articles published between 2020 and 2024 were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and were analyzed based on industrial sector, ML techniques, datasets, evaluation metrics, and implementation challenges. Results show that combining ML with diverse sensor data enhances predictive performance under varying operational conditions across manufacturing, energy, healthcare, and transportation. Frequently used open datasets include the commercial modular aero-propulsion system simulation (CMAPSS), the malfunctioning industrial machine investigation and inspection (MIMII), and the semiconductor manufacturing process (SECOM) datasets, though data heterogeneity and imbalance remain major barriers. Emerging paradigms such as hybrid modeling, digital twins, and physics-informed learning show promise but face issues like computational cost, interpretability, and limited scalability. The findings highlight future research needs in model generalizability, real-world validation, and explainable artificial intelligence (AI) to bridge gaps between ML innovations and industrial practice.

This paper highlights the crucial role of wireless sensor networks (WSNs) in the surveillance and administration of critical infrastructures (CIs), contributing to their reliability, security, and operational efficiency. It starts by detailing the international significance and structural aspects of these infrastructures, mentions the market tension in recent years in the gradual development of wireless networks for industrial applications, and proceeds to categorize WSNs and examine the protocols and standards of WSNs in demanding environments like critical infrastructures, drawing on the recent literature. This review concentrates on the protocols and standards utilized in WSNs for critical infrastructures, and it concludes by identifying a notable gap in the literature concerning quality standards for equipment used in such infrastructures.

Local energy systems (LESs) are becoming larger and more heterogeneous as distributed energy resources, electrified loads, and active prosumers proliferate, increasing the need for reliable coordination of operation, markets, and community governance. This Perspective synthesizes recent literature to map how artificial intelligence (AI) supports forecasting and situational awareness, optimization, and real-time control of distributed assets, and community-oriented markets and engagement, while arguing that adoption is limited by system-level credibility rather than model accuracy alone. The analysis highlights interlocking deployment barriers, such as governance-integrated explainability, distributional equity, privacy and data governance, robustness under non-stationarity, and the computational footprint of AI. Building on this diagnosis, the paper proposes principles-as-constraints for sustainable, trustworthy LES AI and a deployment-oriented validation and reporting framework. It recommends evaluating LES AI with deployment-ready evidence, including stress testing under shift and rare events, calibrated uncertainty, constraint-violation and safe-fallback behavior, distributional impact metrics, audit-ready documentation, edge feasibility, and transparent energy/carbon accounting. Progress should be judged by measurable system benefits delivered under verifiable safeguards.

Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and fault tolerance, security and trust, time synchronisation, energy harvesting and power management, media access control (MAC) and scheduling, interoperability, routing and topology control, and real-world validation, within a unified comparative framework. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Scopus search identified 60 primary publications published between 2022 and 2025. The analysis shows a clear shift from reactive designs to predictive approaches that incorporate learning methods and energy considerations. Fault detection now relies on deep learning (DL) and statistical modelling, security incorporates trust and intrusion detection, and new synchronisation and MAC schemes approach wired levels of determinism. Regarding applied contributions, the analysis notes that routing and energy harvesting advances extend network lifetime. However, gaps remain in mobility support, interoperability across protocol layers, and field validation. The present work outlines these open issues and highlights research directions needed to mature IWSANs into robust infrastructure for Industry 4.0 and the emerging Industry 5.0 vision.

This study explores public perceptions, involvement, and barriers to energy communities in Romania, a country where decentralized renewable energy initiatives are still in their infancy. Data were collected through a nationwide online survey based on a quantitative research design, involving 118 respondents from all four macro-regions in Romania. The survey assessed awareness of the concept of energy communities, perceived benefits, technological and regulatory challenges, and willingness to participate or invest. The results show that the perception of energy communities is generally positive, with solar and environmental benefits being the most important. However, significant barriers remain, particularly in terms of financing, institutional support, and regulatory complexity. Urban, involved, and female respondents consistently rated benefits higher and identified more barriers than rural, non-participants and male respondents. Statistical differences between groups were confirmed using the Mann–Whitney U-test. These results highlight the importance of targeted communication, improved policy frameworks, and educational initiatives to ensure broader public involvement and inclusive development of renewable energy systems in Romania.

The high-cost energy provided by operating centralized power plants and their related infrastructures, immerse researchers to find other means of fulfilling energy requirements. Solar photovoltaic (PV) technology is an appropriate and cost-effective source of electricity for many applications, bringing basic services and facilities in an environmentally friendly manner. Photovoltaic systems involve an interdisciplinary approach in ranging fields, namely reliability, motor drives, controls, inverters, switched mode converters, battery chargers etc. With this as the area of interest, focus of paper is in the reliability of switched mode power converters area, which supports advancement and dissemination of the PV application technology domain. The PV applications involve a switched mode power converter that drives a physical system that is either a mechanical system or an electro-mechanical system. [PUBLICATION ABSTRACT]

A significant percent of all global deaths are caused by cardiovascular diseases (CVD). The diagnostic of the electrocardiogram (ECG) is a clinical practice widely adopted to evaluate the heart condition and identify CVD. For longterm ECG monitoring, a biopotential acquisition system integrated in common clothing is a viable solution for telemedicine. The electrodes and wires play a major role in the comfort and signal quality acquired from the patient. The paper presents a technical solution, where stainless steel yarn was used to create a Lead I Einthoven system consisting of 3 dry electrodes embroidered on a sports shirt. There are novel electrode materials and techniques that push further the stateof-the-art in ECG acquisition, but the authors focused on the currently available materials that are low-cost, widely available and easily integrable into common clothing, in order to seek a simple yet fully functional solution with the potential to become a truly ubiquitous ECG monitoring system.

Take into account the entrance equivalent impedance of gyrators, by connecting a condenser to output is possible simulate a lossless inductor, which can be grounded inductor or floating inductor. The gyrators behavior of a class of two-ports structured on operational amplifiers and resistors is studied. The paper presents some aspects regarding performances characteristics of gyrators with different circuit topology. In order to verify the theoretical predictions, the analytical inductivities are linked with the experimental one numerical by the means of a PSpice simulation. [PUBLICATION ABSTRACT]

This paper provides a comprehensive analysis of the development, implementation, and evolution of the circular economy indicator (CEI) in the context of hydroelectric turbine refurbishment over the past five decades. By systematically examining publications indexed in the Web of Science database between 1975 and 2025, the study traces the conceptual origins of the CEI, highlights methodological advances, and analyzes practical applications. The analysis focuses on key aspects such as material circularity, energy efficiency, including the share of renewable sources, and the extension of operational lifetime achieved through refurbishment. The paper also identifies persistent methodological gaps, in particular regarding the integration of social and governance dimensions, as well as the lack of standardization across projects, proposing strategies to increase the reliability and applicability of the indicator. The results provide guidance for integrating circular economy principles into hydroelectric refurbishment processes, outline good practices, and set priorities for future research oriented towards more holistic and multidimensional assessments of circularity.