Because of its specific nature, mining activity causes numerous negative impacts on the environment, both during the exploitation phase and after it has ended. An important source of income in the Jiu Valley is represented by the Lupeni Mining Exploitation. Like any mining activity, coal exploitation causes various negative effects on the environment. The subsidence phenomenon represents a significant issue associated with coal mining in the Jiu Valley. Underground extraction of mineral deposits induces displacement of the overburden strata. Such displacements result in ground subsidence and modifications of the surface topography. The larger the voids created following the exploitation of useful mineral deposits, the more they affect the surface of the land above the exploitation through sinking, displacement, deformation, and even cracks. Secondary deformations refer to post-mining surface movements induced by delayed rock mass adjustment, manifesting as ground collapse, localized subsoil failure, or uplift driven by groundwater rebound after drainage cessation. In this paper, we aim to study the subsidence phenomenon produced by coal mining at the Lupeni Mining Exploitation using the COMSOL simulation software and applying the Barcelona Basic Model (BBM) and Modified Cam-Clay (MCC) models. Following the simulation, the behavior of the rocks could be observed in order to improve prediction accuracy to support sustainable land management in post-mining areas.

The rapid evolution of energy systems and the increasing penetration of renewable energy sources have transformed traditional power grids into complex, dynamic smart grids (SG). Effective management of these systems requires integrated strategies that address technical, operational, and cyber-physical challenges. This paper presents a comprehensive framework for smart grid management that spans assessment, vulnerability analysis, blackout prevention, optimization, and resilience enhancement. The approach begins with multi-criteria assessment techniques to evaluate grid performance across reliability, efficiency, and sustainability dimensions. Advanced control algorithms and predictive analytics are then applied to optimize load balancing, energy storage, and distributed generation coordination. Finally, resilience strategies—encompassing cybersecurity measures, fault-tolerant architectures, and adaptive recovery mechanisms—are integrated to ensure stability under adverse conditions. The proposed framework underscores the importance of interoperability, data-driven decision-making, and cross-domain collaboration in achieving robust and sustainable smart grid operations.

Road traffic injuries remain a central challenge for sustainable transport, public health, and mobility governance. The task of monitoring these injuries requires indicators that jointly capture harm severity, road and environmental context, and patterns of vehicle involvement at scale. Using harmonised English-language Romanian police crash exports (2023–2025), we build 92,790 records with 36 variables and estimate two complementary k-means typologies: a severity partition based on the fatality, injury, and vehicle-count fields (a register proxy for involvement, not vehicle-type attributes) and a context partition based on the road, environment, mechanism, and cause fields with one-hot encoding and TruncatedSVD. Reported tables and figures reproduce the archived MiniBatch pipeline for replication; for context, full-batch k-means clustering on the same embedding is the recommended default when cross-year prevalence stability is required (train–test TVD 0.039 versus 0.569 under MiniBatch). We report silhouette-guided choices ($k=6$ severity, $k=4$ context), cross-seed stability, feature ablations, and a 2023–2024 versus 2025 prevalence comparison. A Pearson ${\chi }^2$ test on severity × context labels reveals strong statistical significance, yet Cramér’s V remains small—statistical association with limited practical coupling, consistent with complementary rather than redundant partitions. Limitations include police-reported injury counts; a coarse vehicle proxy; weak context geometry; and large MiniBatch context drift, which binds inference to within-year descriptive profiling unless analysts refit the model, add version labels, or adopt full-batch context clustering. The contribution is an integrated, reproducible profiling and governance workflow for dashboards and follow-on modelling—not a fixed multi-year cluster taxonomy.

This study explores integrated methods for managing Smart Grid (SG) with a focus on complex vulnerability analysis, blackout prevention and increasing the resilience of energy infrastructure. In the context of the transition to renewable sources and increasingly stringent sustainability requirements, SG are becoming essential for optimizing consumption, reducing losses and ensuring the continuity of electricity supply. The paper highlights the importance of systematic risk assessment, including both physical threats and cyber-attacks, and proposes real-time monitoring methodologies for rapid detection and isolation of faults. By integrating IoT (Internet of Things), AI (Artificial Intelligence) and advanced predictive analytics technologies, it aims to prevent blackouts and reduce their impact on consumers. It also discusses strategies for increasing the resilience of SG, such as the implementation of autonomous micro-grids, energy storage and optimization of energy flows. The study highlights those integrated approaches that not only improve grid security and reliability, but also significantly contribute to promoting energy sustainability practices, through efficient use of resources and reducing the carbon footprint. The results highlight that proactive planning, combined with advanced monitoring and control technologies, allows SG to respond adaptively to disruptions, minimize risks and ensure a more sustainable and resilient energy system.

In order to have a real picture of the exergetic performances of the compressors and highlight the contribution of each loss to the exergetic yield, we performed a series of experimental determinations “in site”, then based on a calculation model, we drew up the real hourly exergetic balances related to the KAESER compressor ESD 375. Based on the presented breviary, we approached a case study, used to justify energetically the feasibility of replacing old compressors, with many hours of operation, with compressors equipped with heat recuperators and compressed air dryers. In this way, the following were determined: the Carnot factor, the amount of heat given off, the exergy harnessed, the useful heat, the exergy supplied by the network, the increase in the efficiency of energy use, the annual operating time of the compressor, the annual energy economy, the production of GHG. In addition, the performed calculations have the role of justifying the installation of heat exchangers on compressors in pneumatic systems. In this work, the calculation breviary is presented on the basis of which the numerical calculation programs were made and then the effects of heat recovery in the two helical compressors with oil injection were quantified.