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.

Faced with numerous challenges raised by the requirements of the modern industries for higher power and higher energy, supercapacitors study started playing an important role in offering viable solutions for some of these requirements. This paper presents the surface redox reactions based modeling in order to study the origin of high capacity of EDLC (electrical double-layer capacitor) for better understanding the working principles of supercapacitors. Some application-dependent sizing methods are also presented since proper sizing can increase the efficiency and the life cycle of the supercapacitor based systems

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.

In an ever changing business environment, in order to bring value, security risk management must keep engaged at pace with the company, by following the enterprise goals and using the same methodologies as core business units. This paper analyses how information security risk management can be automated and interlinked with the processes in a software development company, using an Agile approach with Kanban. The methodology used has been tested (Proof of Concept) applying relevant information security risks for an e-commerce business, the results showing an increase in efficiency of the risk management team, better business response and improvements of the defined risk management SLAs (Service Level Agreement).