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This study examines the progress, challenges, and successes in implementing Sustainable Development Goal 12 (SDG12), focusing on responsible consumption and production, using Qatar as a case study. The State has integrated Sustainable Consumption and Production (SCP) into national policies, established coordination mechanisms, and implemented action plans aligned with SDG12 targets. Achievements include renewable energy adoption, waste management reforms, and sustainable public procurement, though challenges persist in rationalizing fossil fuel subsidies, addressing data gaps, and enhancing corporate sustainability reporting. Efforts to reduce food loss and waste through redistribution programs highlight the country’s resilience, despite logistical obstacles. The nation has also advanced hazardous waste management, environmental awareness, and sustainable tourism policies, though gaps in data systems and policy coherence remain. Qatar’s approach provides a valuable local-to-global example of balancing resource-dependent economies with sustainability goals. Its strategies and lessons offer potential adaptability for other nations, especially those facing similar challenges in achieving SDG12. By strengthening data systems, enhancing policy integration, and fostering regional and international cooperation, Qatar’s efforts underscore the importance of aligning economic growth with environmental stewardship, serving as a blueprint for global sustainability initiatives. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article

Objective: This study aimed to improve the understanding of the lives of patients with chronic neuropathic pain planned for invasive motor cortex stimulation (iMCS) and assess their expectations towards this intervention and its impact. Methods: Semi-structured face-to-face interviews were conducted until saturation of data was reached. Patients were recruited from one university medical center in the Netherlands. All interviews were audio-recorded, transcribed verbatim, and subjected to thematic analysis using iterative and inductive coding by two researchers independently. Results: Fifteen patients were included (11 females; mean age 63 ± 9.4 yrs). Analysis of the coded interviews revealed seven themes: (1) the consequences of living with chronic neuropathic pain; (2) loss of autonomy and performing usual activities; (3) balancing energy and mood; (4) intimacy; (5) feeling understood and accepted; (6) meaning of life; and (7) the expectations of iMCS treatment. Conclusions: This is the first qualitative study that describes the suffering of patients with chronic neuropathic pain, and their expectations prior to invasive brain stimulation. Significant themes in the lives of patients with chronic pain have been brought to light. The findings strengthen communication between physicians, caregivers, and patients. Practice Implications: The insights gathered from the interviews create a structured framework for comprehending the values and expectations of patients living with central pain and reveal the impact of symptoms due to the central pain. This knowledge improves the communication between physicians and caregivers on one side and the patient on the other side. Furthermore, the framework enhances the capacity for shared decision-making, particularly in managing expectations related to iMCS. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

Solid biomass (agro-residue) is the most important source of renewable energy. The accelerating impacts of climate change and global population growth contribute to air pollution through the use of fossil fuels. These processes increase the demand for energy. The European Union has adopted a climate action plan to address the above challenges. The main aim of this study was to assess the economic performance of the producers of biomass for energy generation in Poland. The detailed objectives were to determine land resources in the studied agricultural farms and to determine the value of fixed and current assets in the analyzed farms. We used questionnaires as the main method to collect data. Purposive sampling was used to choose the farms. We conducted various tests to analyze the revenues from biomass sales and their normality, such as the Dornik–Hansen test, the Shapiro–Wilk test, the Liliefors test, and the Jargue–Berra statistical test. Moreover, we conducted regression analysis to find factors that are the basis for the economic performance (incomes) of farms that sell biomass. Results: This study demonstrated that biomass sales had a minor impact on the performance of agricultural farms, but they enabled farmers to maintain their position on the market. The economic analysis was carried out on a representative group of Polish agricultural farms, taking into account fixed and current assets, land use, production structure, and employment. The findings indicate that a higher income from biomass sales was generally associated with better economic results per farm and per employee, although not always per hectare of land. This suggests that capital intensity and strategic resource management play a crucial role in the profitability of bioenergy-oriented agricultural production. Conclusions: We concluded that biomass sales had a negligible influence on farm income. But a small income from biomass sales could affect a farm’s economic viability. Full article

Atrial fibrillation remains the most frequent sustained arrhythmia, particularly in the elderly population, and is associated with increased risks of stroke, heart failure, and reduced quality of life. While catheter ablation is widely used for rhythm control, its efficacy is limited in persistent and long-standing atrial fibrillation. Over the past two decades, minimally invasive surgical strategies have emerged as effective alternatives, aiming to replicate the success of the Cox-Maze procedure while reducing surgical trauma. This overview critically summarizes the current minimally invasive techniques available for atrial fibrillation treatment, including mini-thoracotomy ablation, thoracoscopic ablation, and hybrid procedures such as the convergent approach. These methods offer the potential for durable sinus rhythm restoration by enabling direct visualization, transmural lesion creation, and left atrial appendage exclusion, with lower perioperative morbidity compared to traditional open surgery. The choice of energy source plays a key role in lesion efficacy and safety. Particular attention is given to the technical steps of each procedure, patient selection criteria, and the role of left atrial appendage closure in stroke prevention. Hybrid strategies, which combine epicardial surgical ablation with endocardial catheter-based procedures, have shown encouraging outcomes in patients with refractory or long-standing atrial fibrillation. Despite the steep learning curve, minimally invasive techniques provide significant benefits in terms of recovery time, reduced hospital stay, and fewer complications. As evidence continues to evolve, these approaches represent a key advancement in the surgical management of atrial fibrillation, deserving integration into contemporary treatment algorithms and multidisciplinary heart team planning. Full article

This article presents a comprehensive review of methods used for forecasting electricity consumption. The studies analyzed by the authors encompass both classical statistical models and modern approaches based on artificial intelligence, including machine-learning and deep-learning techniques. Electricity load forecasting is categorized into four time horizons: very short term, short term, medium term, and long term. The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. The article emphasizes the growing importance of accurate forecasts in the context of the energy transition, integration of renewable energy sources, and the management of the evolving electricity system, shaped by decentralization, renewable integration, and data-intensive forecasting demands. In conclusion, the authors highlight the lack of a universal forecasting approach and the need for further research on hybrid models that combine interpretability with high predictive accuracy. This review can serve as a valuable resource for decision-makers, grid operators, and researchers involved in energy system planning. Full article

Smart farming has emerged as a promising solution to address challenges such as climate change, population growth, and limited agricultural infrastructure. To enhance the operational efficiency of smart farms, this paper proposes an integrated system that combines Recurrent Neural Networks (RNNs) and Unmanned Aerial Vehicles (UAVs). The proposed framework forecasts future resource shortages using an RNN model and recent environmental data collected from the field. Based on these forecasts, the system schedules a resource supply plan and determines the UAV path by considering both dynamic energy consumption and priority levels, aiming to maximize the efficiency of the resource supply. Experimental results show that the proposed integrated smart farm framework achieves an average reduction of 81.08% in the supply miss rate. This paper demonstrates the potential of an integrated AI- and UAV-based smart farm management system in achieving both environmental responsiveness and operational optimization. Full article

As the construction industry continues to evolve, energy consumption of buildings, particularly CO₂ emissions, has become a critical focus for sustainable development. The need for effective design decisions regarding the selection of materials throughout the project life cycle is apparent, yet the link between specifications and CO₂ emissions has not been set yet. This study presents a comprehensive life cycle assessment (LCA) of CO₂ emissions across various Construction Specifications Institute (CSI) categories, aiming to identify the carbon footprint of different building systems and materials. The methodology focuses on using 3D building model case studies to evaluate the design decisions versus their impact on global warming potential (GWP). The results of this study emphasize that within CSI categories, concrete divisions consistently emerge as the predominant contributors to GWP, exceeding 75% in several instances. Following closely, metals contribute approximately 50% in multiple projects. The study also explores sustainable design options across CSI divisions to provide insights into building components contributing most to a building’s overall carbon footprint. This deeper understanding of sustainable design principles regarding CSI divisions and their impact on carbon footprint reduction will help sustainable designers and construction managers to implement carbon-conscious material choices and design strategies early in the planning phase. Full article

Photovoltaic panels have become a promising solution for generating renewable energy and reducing our reliance on fossil fuels by capturing solar energy and converting it into electricity. The effectiveness of this conversion depends on several factors, such as the quality of the solar panels and the amount of solar radiation received in a specific region. This makes accurate solar irradiance forecasting essential for planning and managing efficient solar power systems. This study examines the application of machine learning (ML) models for accurately predicting global horizontal irradiance (GHI) using a three-year dataset from six distinct photovoltaic stations: NELHA, ULL, HSU, RaZON+, UNLV, and NWTC. The primary aim is to identify optimal shared features for GHI prediction across multiple sites using a 30 min time shift based on autocorrelation analysis. Key features identified for accurate GHI prediction include direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and solar panel temperatures. The predictions were performed using tree-based algorithms and ensemble learners, achieving $R^2$ values exceeding 95% at most stations, with NWTC reaching 99%. Gradient Boosting Regression (GBR) performed best at NELHA, NWTC, and RaZON, while Multi-Layer Perceptron (MLP) excelled at ULL and UNLV. CatBoost was optimal for HSU. The impact of time-shifting values on performance was also examined, revealing that larger shifts led to performance deterioration, though MLP performed well under these conditions. The study further proposes a stacking ensemble approach to enhance model generalizability, integrating the strengths of various models for more robust GHI prediction. Full article

Island electrical networks often face stability and resilience issues due to their weakly meshed structure, which lowers system inertia and compromises supply continuity. This challenge is further intensified by the increasing integration of renewable energy sources, promoted by decarbonization goals, whose intermittent and variable nature complicates grid stability management. To address this, Red Eléctrica de España—the transmission system operator of Spain—has planned several improvements in the Canary Islands, including the installation of new wind farms and a second transmission circuit on the island of La Palma. This new infrastructure will complement the existing one and ensure system stability in the event of N-1 contingencies. This article evaluates the stability of the island’s electrical network through dynamic simulations conducted in PSS^®E, analyzing four distinct fault scenarios across three different grid configurations (current, short-term upgrade and long-term upgrade with wind integration). Generator models are based on standard dynamic parameters (WECC) and calibrated load factors using real data from the day of peak demand in 2021. Results confirm that the planned developments ensure stable system operation under severe contingencies, while the integration of wind power leads to a 33% reduction in diesel generation, contributing to improved environmental and operational performance. Full article

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article