Search Results (2,116)

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

Accurate short-term forecasting for energy consumption is crucial in modern energy network management, especially for cities such as Tetouan, where considerable climate variability and diverse usage patterns present significant challenges when it comes to making short-term forecasts. This paper proposes a hybrid residual learning framework that combines a long short-term memory (LSTM) network with eXtreme Gradient Boosting (XGBoost) to improve short-term load forecasting for the Tetouan electricity network. The novelty of the proposed approach lies in coupling temporal sequence modeling with residual error correction driven by exogenous meteorological and calendar-related information. The proposed model is validated using real electricity consumption data from Zone 2 of Tetouan City, with further validation across all three available zones confirming the model’s generalizability. The proposed model achieves a coefficient of determination (R²) of 0.984, an RMSE of 687.21 kWh, and a MAPE of 2.41%, representing a 121.3 kWh RMSE improvement over the standalone LSTM baseline. These results confirm that the hybrid model is better at tracking periods of high demand compared to conventional machine learning approaches and standalone deep learning models. Full article

Short-term water demand forecasting is a key issue for the management of smart water networks, particularly in the context of remote control and active regulation. This study analyses a real-world dataset of water demand coefficients, collected at 15 min intervals, from a municipality in Southern Italy serving approximately 73,000 inhabitants. The proposed model, based on Gaussian Process Regression (GPR) with a Rational Quadratic kernel (RQ), is compared with a statistical benchmark constructed using average patterns for each time slot by the application of the Gauss Distribution. The results show a reduction in RMSE and MAE and a better ability to track the daily dynamics of demand using the GPR approach. Full article

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

Brown trout (Salmo trutta L.) is a bioindicator species of ecological integrity in mountain rivers of northern Portugal. Habitat loss and recreational fishing justify sustainable management to balance the conservation and exploitation of these fish populations. In fact, salmonid streams in NE Portugal are low productive watercourses and fish stocking has been continuously demanded by fishermen. However, this most common management action must be analyzed carefully and determined the effective increase for local fisheries, taking into consideration the potential dispersal of stocked fish. The objective of the present study, developed in River Sabor, was to determine short- and medium-term movement and dispersal patterns and habitat preferences of wild and stocked Brown trout, using radio telemetry, during a weekly monitoring 4-month period (October to February 2026; n = 18). Fish was sampled by electrofishing at beginning and the end of the experiment. Twenty-four adult Brown trout, equally distributed by two salmonid sections, and three groups, (1) wild resident (River Sabor) (213–270 mm TL); (2) wild non-resident (from contiguous basin, River Baceiro) (200–375 mm TL) and (3) rear-captivity (Castrelos Fishfarms, ICNF) (227–365 mm TL) fish, were surgically implanted with radio transmitters. Significant differences (KW-H (2;24) = 4.67; p = 0.09) were observed for the dispersal distances, considering fish detected at least in five sampling events, ranging from 120–1437 m for the wild resident stationary group to 192–14,150 m for the stocked mobile group. Moreover, wild non-resident fish displayed higher movement in the upstream direction, in opposition to the downstream movement of stocked individuals. Wild resident and non-resident trout tended to display increased movements during November and December, probably related to spawning activity, showing preferences by riffle and run habitats. Stocked fish were detected in pool habitats (mainly weir reservoirs), exhibiting significantly lower growth rates, and increased movement during January and February, particularly during flood events. These findings are valuable information for managers related to movement patterns, habitat use and stocking management. Full article

Perishable products are usually priced in real-time to volatile market environments, thereby optimizing inventory control, minimizing resource wastage, and maximizing corporate profitability. Based on the public dataset from the 2023 Higher Education Press Cup National College Students Mathematical Modeling Competition, this paper addresses the challenge of multi-product joint pricing for perishable fresh produce and proposes an attention-augmented proximal policy optimization algorithm (termed ATT-PPO), which embeds an attention mechanism into the proximal policy optimization (PPO) framework. The integrated attention mechanism confers three core advantages to the model: first, it dynamically captures inter-product interdependencies, enabling an accurate reflection of cross-price elasticity and demand correlations; second, it reduces feature redundancy and computational overhead in multi-product collaborative pricing strategies; third, it enhances both the interpretability and computational efficiency of the model. Experimental results demonstrate that in the scenario of multi-product pricing, the ATT-PPO algorithm achieves competitive performance compared to PPO, DDPG (Deep Deterministic Policy Gradient), SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient), with the key advantage lying in its ability to provide interpretable attention weights that reveal dynamic cross-product dependencies in pricing decisions. This study not only expands the applicability of DRL (Deep Reinforcement Learning) to practical economic problems in the fresh produce sector but also provides valuable theoretical insights that can be generalized to other short-lifecycle product domains, including fashion apparel and consumer electronics. Full article

Recirculating organic byproducts like food waste, wastewater and manure efficiently and at scale in a circular bioeconomy will be critical to ensuring future food security, energy security, climate resilience, water security and environmental health. Ultimately, we will not be able to live within the safe operating space of our planetary boundaries if we do not stop our wasteful and inefficient habits. Our food, waste, energy and water sectors are starting to transform towards circularity, driven by a diverse range of drivers, from net zero emissions targets, to food waste policies, and to rising fertiliser prices and geopolitical risks. However, these sectors are often not transforming in a coordinated manner, risking unintended consequences like competition between end-uses, technology lock-in, the prevention of scalability, or failure to achieve key sustainability targets, causing rebound effects. For example, society’s organic waste is being earmarked for the production of bioenergy, sustainable aviation fuels, biomaterials, and biofertilisers; however, it is not clear if there will be a sufficient supply of organic waste to meet these diverse demands. Phosphorus flow analyses indicate that we will need to secure almost all of the nutrients in organic waste as fertiliser raw material to produce food. There are some existing pockets of innovation within sectors related to food waste, water and wastewater, fertilisers and agriculture, and bioenergy. However, many initiatives are being driven by short-term challenges, are not operating at scale, or are not sufficiently integrated across sectors. In this paper, we provide examples of innovations and challenges from around the world, including Italy, Australia, Sri Lanka, the UK, Japan, and Malawi. This paper identifies a pathway to navigate tensions to achieve co-existing sustainability goals, including key enablers and barriers, ranging from overcoming regulatory fragmentation to a lack of capital investments. Creating a truly viable circular economy for organic byproducts requires the integration of policies, markets, technologies and people. This means engaging diverse stakeholders, from local councils and private waste contractors, farmers, and fertiliser companies to energy retailers and wastewater utilities, NGOs, informal collectors, and environmental regulators and policy-makers. Full article

Against the backdrop of global decarbonization in energy-intensive industries, the primary aluminum sector has become a critical field for deep industrial decarbonization due to its high electricity consumption, large share of indirect carbon emissions, and complex mitigation pathways. This challenge is particularly salient in regions endowed with abundant renewable resources while hosting concentrated industrial electricity demand, where coordinated mitigation across technological upgrading and energy system transformation has broad practical relevance. Using Xining in Qinghai Province, China, a renewable-rich region, as an illustrative case, this study systematically examines the major carbon mitigation pathways in the primary aluminum industry, including mining, alumina production, electrolytic cell retrofitting, power system coordination, and carbon capture, utilization, and storage (CCUS). A multi-objective optimization model is developed to minimize marginal abatement costs (MAC) while maximizing technological application performance, and the sequential unconstrained minimization technique (SUMT) is employed to optimize mitigation pathways under short-, medium-, and long-term scenarios. The results show that, in the short term (before 2030), emission reduction mainly relies on improvements in electrolysis efficiency, leading to a mitigation pattern dominated by reductions in electricity consumption per unit of output. In the medium term (before 2035), the pathway shifts from isolated process optimization to a coordinated strategy combining process upgrading with power decarbonization, exhibiting a structural mitigation pattern driven by synergy between the production side and the energy side. In the long term (before 2060), the pathway evolves toward a stage dominated by energy system reconfiguration and carbon utilization. With high shares of renewable electricity integration, DC power supply configurations, and energy storage support, primary aluminum production is expected to achieve deep decarbonization on the power side. This study provides a transferable analytical framework and policy-relevant insights for the low-carbon transition of energy-intensive industries in renewable-rich regions. Full article

The paper presents a comparative analysis of the Differential Transform Method (DTM) with respect to classical numerical approaches, such as the Euler and Runge–Kutta methods, using the nonlinear Duffing equation as a representative example. This equation, being a prototypical model of dynamical systems exhibiting chaotic behavior, provides a demanding test environment for techniques used to approximate solutions of ordinary differential equations. The aim of the study is to assess the accuracy, stability, and computational efficiency of the considered methods as functions of the system parameters. The DTM approach, based on differential transform and a series representation of the solution, was compared with classical discretization schemes. In the DTM framework, the symmetry of the system is not imposed explicitly, but emerges from the initial conditions and the recursive structure used to determine the series coefficients. However, the preservation of this symmetry may be disrupted, leading to asymmetry due to truncation of the series expansion and the propagation of numerical errors. The obtained results indicate that DTM can serve as a competitive alternative to conventional methods, particularly in short-term simulations of nonlinear dynamical systems, offering high accuracy at a relatively low computational cost. Full article

The building sector accounts for nearly 30% of global energy use and 28% of CO₂ emissions, with residential buildings in Canada contributing about 17% of national energy demand. In cold regions such as Labrador, approximately 82% of this consumption is associated with space heating and domestic hot water, making heating the dominant residential load, while fossil-fuel furnaces and electric baseboard heaters remain common. These conditions highlight the need for efficient and sustainable heating alternatives for cold-climate residential buildings. This study examines the design and performance of a hybrid solar photovoltaic (PV) and geothermal heat pump (GTHP) system for a typical detached home in L’Anse-au-Loup, Labrador, Newfoundland and Labrador, Canada (51.52° N, 56.84° W), with the goal of improving energy efficiency and reducing dependence on the electrical grid. Heating and cooling loads were developed using the Hourly Analysis Program (HAP 6.1), while system operation and economic performance were assessed through the Hybrid Optimization Model for Electric Renewables (HOMER Pro 3.18.3). The proposed design combines a rooftop PV array, a ground-source heat pump, and second-life lithium-ion batteries repurposed from retired electric vehicles to lower costs and support short-term energy storage. The system is modelled under grid-connected conditions to reflect realistic operation for northern households. Results show that the hybrid system can meet annual electrical and thermal needs while reducing grid consumption by more than half. Annual carbon emissions decrease by roughly 4–5 tonnes, and repurposed batteries offer a cost-effective alternative to new storage. Overall, the study demonstrates that PV–GTHP systems can provide reliable, efficient, and practical energy solutions for cold-climate homes. Full article

The high-rate biological contactor (HRBC) is an enhanced-primary, biosorption-based, carbon-diversion wastewater treatment process with short hydraulic retention time (HRT), short solids retention time (SRT), low dissolved oxygen (DO), and high food-to-microorganism ratio (F/M). This paper presents modifications to a commercial full-plant wastewater biodegradation model using extracellular polymeric substances (EPS) in waste activated sludge (WAS) to simulate pilot test biosorption data. Bench-scale HRBC tests found that each mg of EPS as COD (COD_EPS) biosorbed 1.02 mg sCOD contained in raw wastewater. The fraction of AS organics identified as EPS in terms of COD was 37% in a conventional AS (CAS), 33% in a trickling filter-solids contact (TF/SC), and 18% in a membrane bioreactor (MBR). The modeling process used stoichiometry equations to convert EPS from its constituent concentrations (carbohydrates, proteins, humic acids, uronic acids) into COD. The conversion did not alter the finding that the normalized total EPS showed a positive relationship with soluble chemical oxygen demand sCOD biosorption with a 0.91 coefficient of determination. The modified commercial biodegradation model gave a maximum error of −12.6% when simulating pilot-scale results, and 80% of all data points were less than ±10% error. The modified model predicted 16% sCOD biosorption by EPS using the design data for a full-scale HRBC facility currently under construction. Full article

Massive Internet of Things (IoT) and sensor-network services in 5G/6G systems increasingly rely on network slicing to support large-scale sensing, monitoring, and mission-critical applications. In such sliced infrastructures, Proportional Fair (PF) allocation assigns resources according to slice-reported demands. This reliance on trusted demand reporting makes coexisting slices, including mMTC-based IoT sensor slices, vulnerable to resource exhaustion attacks, where a malicious slice inflates its demand to monopolize shared resources and induce Service Level Agreement (SLA) violations. Existing unsupervised defenses mainly focus on anomaly detection, while the translation of detection results into resource-level mitigation remains insufficiently addressed. To bridge this gap, this paper proposes AutoGuard-Hybrid, an unsupervised detection-to-mitigation framework that combines complementary anomaly detectors with allocation-aware mitigation policies to preserve slice-level service availability. Unlike prior detection-only approaches, AutoGuard-Hybrid converts unsupervised anomaly evidence into allocation-aware demand purification before PF scheduling. Its key design is a closed-loop integration of Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) as spatial and temporal front-end detectors with Adaptive Clipping and a Safety Cap, which translate anomaly scores into demand purification actions. Experiments show that AutoGuard-Hybrid remains comparable to Isolation Forest under Continuous attacks and improves the mean system-wide SLA violation rate by 27.6% under Adaptive Probing attacks. Stage activation analysis further shows that LSTM-AE activations increase from 9.3 under Continuous attacks to 29.4 under Adaptive Probing attacks. Ablation results show that Adaptive Clipping alone reduces the system-wide SLA violation rate by 75.0%, while the full mitigation pipeline achieves an 84.6% total reduction. AutoGuard-Hybrid operates within the 1 ms Transmission Time Interval (TTI) constraint and provides a practical defense framework for next-generation network slicing-enabled IoT and sensor-network services. Full article

Industry 5.0, deploying artificial intelligence (AI) at its core, reframes industrial evolution from a predominantly technology- and efficiency-driven innovation model toward a virtuously human-centric, sustainable, and resilient model of value creation by organizations. This review paper, based on an interdisciplinary literature review, explores how AI, within the Industry 5.0 paradigm, reshapes economic logics, the understanding of information asymmetry, and sustainability trajectories, and the implications for entrepreneurial strategy and business model innovation, which demand the development of a new form of organizational intelligence. While the literature suggests that AI, when deployed within a mature Industry 5.0 framework, could generate synergistic economic and sustainability values through circular, human-centered, and digitally augmented systems, human–AI co-intelligence gains are contingent on insights that address systems quality, reskilling, ethics, and reorienting resources from overly short-term profit maximization toward wisdom for long-term socio-ecological, climate resilience, and ESG performance. This study introduces a framework for tackling organizational sustainability dynamics, anticipating the emergence of new industries and the retransformation of enduring ones amid creative destruction in the AI era. Future studies to fill knowledge gaps and implications for human competencies that will enhance organizational intelligence are articulated. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article