Search Results (2,159)

In response to the urgent need for decarbonising the transport sector, this paper analyses the thermal performance of a battery electric minibus under cold ambient conditions. Thermal simulation models of the vehicle cabin and its electric heating circuits for both driver and passenger areas were developed using Modelica and validated with measurement data at −7 °C and 0 °C. The model showed good agreement with the measurements, with cabin temperature deviations within ±1.6 K and heating power deviations below 6%. Results show that the existing electric-only heating system is, in the automatic heating mode selected, insufficient to reach the target cabin temperature of 23 °C, as the optional fuel-powered heater was omitted to ensure fully zero-emission operation. To address this, an extended heating system with an additional heat exchanger was implemented in the simulation, which improved the overall cabin temperature level and also its spatial variation. However, it also increased the heating power demand by 43% at −7 °C (from 4.8 kW to 6.8 kW) and by 17% at 0 °C (from 4.8 kW to 5.6 kW). An additional heat loss analysis revealed that approx. 65–75% of all thermal losses occur through the window areas. Future improvements should therefore focus on optimising the heating strategy and enhancing cabin and heating system insulation to reduce energy demand while maintaining or even improving passenger comfort. Full article

The growing interest in using edible flowers as functional ingredients has increased the demand for reliable and sustainable strategies to recover and characterize their bioactive compounds. Torch ginger is a tropical species rich in anthocyanins. In this study, an ultrasound-assisted extraction (UAE) method was developed, optimized, and validated for the efficient recovery of anthocyanins from torch ginger flowers, with a clear focus on food-related applications. A Box–Behnken experimental design was applied to evaluate the influence of solvent composition, temperature, solvent-to-sample ratio, and pH on anthocyanin yield, using chromatographic responses. Solvent composition and solvent-to-sample ratio were identified as the most influential parameters, and effective extraction was achieved under mild temperature and pH conditions. The optimized conditions consisted of 84% methanol in water as the extraction solvent, a temperature of 30 °C, a solvent-to-sample ratio of 20:1 (mL g⁻¹), and a pH of 5.6. Kinetic studies revealed that a 5 min extraction time maximized recovery while preventing compound degradation. The method was successfully applied to different torch ginger varieties, revealing a strong correlation between flower color and anthocyanin concentration. This research provides a fast, reliable, and environmentally friendly approach for assessing anthocyanin content in torch ginger flowers. The results support the valorization of this edible flower as a potential source of natural colorants and bioactive ingredients, contributing to ingredient selection, quality control, and the future development of functional foods and clean-label products. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

As demand for rare earth elements (REEs) rises and environmental concerns about the extraction of primary resources grow, biological methods for removing these elements have gained significant attention as eco-friendly alternatives. This study assessed the ability of the green macroalga Ulva lactuca to remove europium (Eu) from aqueous solutions, evaluated the cellular partition of this element and investigated the toxicological effects of Eu exposure on its biochemical performance. U. lactuca was exposed to variable concentrations of Eu (ranging from 0.5 to 50 mg/L), and the amount of Eu in both the solution and algal biomass was analyzed after 72 h. The results showed that U. lactuca successfully removed 85 to 95% of Eu at low exposure concentrations (0.5–5.0 mg/L), with removal efficiencies of 75% and 47% at 10 and 50 mg/L, respectively. Europium accumulated in algal biomass in a concentration-dependent manner, reaching up to 22 mg/g dry weight (DW) at 50 mg/L. The distribution of Eu between extracellular and intracellular fractions of U. lactuca demonstrated that at higher concentrations (5.0–50 mg/L), 93–97% of Eu remained bound to the extracellular fraction, whereas intracellular uptake accounted for approximately 20% at the lowest concentration (0.5 mg/L). Biochemical analyses showed significant modulation of antioxidant defenses. Superoxide dismutase activity increased at 10 and 50 mg/L, while catalase and glutathione peroxidase activities were enhanced at lower concentrations (0.5–1.0 mg/L) and inhibited at higher exposures. Lipid peroxidation levels remained similar to controls at most concentrations, with no evidence of severe membrane damage except at the highest Eu level. Overall, the results demonstrate that U. lactuca is an efficient and resilient biological system for Eu removal, combining high sorption capacity with controlled biochemical responses. These findings highlight its potential application in environmentally sustainable remediation strategies for REE-contaminated waters, while also providing insights into Eu toxicity and cellular partitioning mechanisms in marine macroalgae. Full article

The sustainability of the beef and dairy industry requires a systems approach that integrates environmental stewardship, social responsibility, and economic viability. Over the past two decades, global genetics consortia have advanced data-driven germplasm programs (breeding and conservation programs focusing on genetic resources) to enhance sustainability across cattle systems. These initiatives employ multi-trait selection indices aligned with consumer demands and supply chain trends, targeting production, longevity, health, and reproduction, with outcomes including greenhouse gas mitigation, improved resource efficiency and operational safety, and optimized animal welfare. This study analyzes strategic initiatives, germplasm portfolios, and data platforms from leading genetics companies in the USA, Europe, and Brazil. US programs combine genomic selection with reproductive technologies such as sexed semen and in vitro fertilization to accelerate genetic progress. European efforts emphasize resource efficiency, welfare, and environmental impacts, while Brazilian strategies focus on adaptability to tropical conditions, heat tolerance, and disease resistance. Furthermore, mathematical models and decision support tools are increasingly used to balance profitability with environmental goals, reducing sustainability trade-offs through data-driven resource allocation. Industry-wide collaboration among stakeholders and regulatory bodies underscores a rapid shift toward sustainability-oriented cattle management strategies, positioning genetics and technology as key drivers of genetically resilient and sustainable breeding systems. Full article

Background: The profound heterogeneity of glioblastoma and the often-limited efficacy of conventional treatments, including arsenic trioxide (ATO), underscore the urgent and critical demand for innovative combination strategies specifically designed to overcome treatment resistance. Methods: We evaluated the therapeutic effects of ATO as a single agent and in combination with the MNK1 inhibitor AUM001 across patient-derived xenograft (PDX) models and investigated molecular determinants of sensitivity and synergy. Our results demonstrated that GBM models resistant to ATO, particularly those of the mesenchymal subtype, are more likely to show synergistic cytotoxicity when AUM001 is added. The combination significantly reduces the frequency of glioblastoma stem cells (GSCs) compared to either drug alone, especially in ATO-resistant models. Results: These observations suggest that targeting the MNK1 pathway in conjunction with ATO is a promising strategy to specifically eradicate GSCs, which are major drivers of GBM recurrence and therapeutic failure. Transcriptomic analyses revealed that ATO sensitivity correlated with activated translation-related pathways and cell cycle processes, while synergistic responses to the combination were driven by distinct molecular signatures in different GBM subtypes. Overall, synergistic response to the combination therapy is more associated with cellular organization, amino acid transmembrane transporter activity, ion channels, extracellular matrix organization and collagen formation. Conclusions: Our findings highlight that specific molecular pathways and their activities, including those involving translation, cell cycle and ion transport, appear to modulate the synergistic efficacy of the ATO and AUM001 combination, thereby offering potential biomarkers for improved patient stratification in future GBM clinical trials of such ATO-based treatments. Full article

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with excessive fertilizer use. Variable-rate application (VRA) offers a precision-based approach to matching N inputs with crop demand, yet winter wheat responses to reduced N rates are still underexplored. This study evaluated winter wheat (Triticum aestivum L.) performance under variable and uniform N application strategies using canopy greenness (NDVI), grain yield, plant N content, nitrogen use efficiency (NUE), and fertilizer costs as indicators. Reduced N treatments (40% and 60% VRA rates) were compared with a uniform (100%) application. Canopy greenness increased across all treatments over time, with NDVI values ranging from 0.855 early in the season to approximately 0.94 at later growth stages, and statistically significant among N rates (p < 0.05). Grain yield was highest under the low N rate (1676.81 kg ha⁻¹), although yield differences among treatments were not statistically significant (p > 0.05). Similarly, plant N content varied slightly across treatments, ranging from 1.73% to 1.82%, with no significant differences. In contrast, NUE declined sharply with increasing N rates, decreasing from 71% under the lower rate to 28% under the uniform rate. Overall, variable-rate treatments used just over half the fertilizer input and cost of the uniform rate while supporting comparable yield and plant N status. These results prove that VRA can improve nitrogen efficiency and reduce input costs without compromising winter wheat productivity, supporting its practical value for sustainable fertilizer management. Full article

Multifunctional wound dressings integrating moisture retention, antibacterial activity, and bioactive delivery are in demand, yet balancing structural stability and functional synergy in polysaccharide hydrogels remains a challenge. This study focused on developing such advanced dressings. Poria cocos glucan (PCG) hydrogels were fabricated via annealing, with PCG-4 (4 wt.%) identified as the optimal matrix. PCG-tannic acid (TA) composite hydrogels were subsequently prepared via TA loading, followed by systematic property characterization and in vivo wound healing evaluation in a rat full-thickness wound model. The composite hydrogel exhibited balanced porosity (56.7 ± 3.4%) and swelling (705.5 ± 11.3%), along with enhanced mechanical rigidity. It enabled temperature-responsive TA release, coupled with high antioxidant activity and antibacterial efficacy. Additionally, it showed excellent biocompatibility (hemolysis rate <2%; NIH-3T3 cell viability >98%) and accelerated rat wound closure with enhanced collagen deposition, suggesting a beneficial combined effect of the composite’s components. PCG-TA holds promise as an advanced wound dressing, and the scalable annealing fabrication strategy supports its translational application potential. Full article

The cosmetic industry is undergoing a deep transformation driven by rapid innovation, evolving consumer expectations, and increasing demands for sustainability. Formulators are required to design products that combine functional efficacy, stability, and appealing sensory properties while adopting environmentally responsible strategies. Traditional empirical and sensory-based approaches, though valuable, are often limited by high costs, time, subjectivity and lack of reproducibility. In this context, instrumental techniques provide an objective and predictive means to optimize product performance. Rheology, texture analysis, and tribology offer complementary insights into the structure, mechanical behavior, and interfacial phenomena of cosmetic formulations, all of which are closely linked to application behavior and sensory perception. Their integration enables a quantitative correlation between formulation composition, process conditions, and tactile performance. This review critically examines recent advances in the integrated use of rheology, texture analysis and tribology in cosmetic science, highlighting their role in sensory prediction, stability assessment, scale-up and eco-design. Together, these instrumental approaches support a more data-driven and innovation-oriented formulation paradigm, enabling database development and predictive modeling. Future research should prioritize database expansion, in vivo validation and machine learning integration to further improve sensory prediction and accelerate the design of advanced cosmetic formulations. Full article

Efficient operation of electric vehicle (EV) charging stations is vital in the development of green transportation infrastructure. To address the challenge of balancing profitability, resource utilization, user behavior, and grid stability, this paper proposes a multi-objective dynamic pricing optimization framework based on a chaotic genetic algorithm (CGA). The model jointly maximizes operator profit and charging pile utilization while incorporating price-responsive user demand and grid load constraints. By integrating chaotic mapping into population initialization, the algorithm enhances diversity and global search capability, effectively avoiding premature convergence. Empirical results show that the proposed strategy significantly outperforms conventional methods: profits are 41% higher than with fixed pricing and 40% higher than with traditional time-of-use optimization, while charging pile utilization is 32.27% higher. These results demonstrate that the proposed CGA-based framework can efficiently balance multiple objectives, improve operational profitability, and enhance grid stability, offering a practical solution for next-generation charging station management. Full article

The widespread adoption of electricity market trading platforms has enhanced the standardization and transparency of trading processes. As markets become more liberalized, regulatory policies are phasing out protective electricity pricing mechanisms, leaving retailers exposed to price volatility risks. In response, demand for risk management tools has grown significantly. Futures contracts serve as a core instrument for managing risks in the energy sector. This paper proposes a futures-based risk hedging model grounded in electricity price forecasting. A price prediction model is constructed using historical data from electricity markets and energy futures, with SHAP values used to analyze the transmission effects of energy futures prices on monthly electricity trading prices. The Monte Carlo simulation method, combined with a t-GARCH model, is applied to calculate CVaR and determine optimal portfolio weights for futures products. This approach captures the volatility clustering and fat-tailed characteristics typical of energy futures returns. To validate the model’s effectiveness, an empirical analysis is conducted using actual market data. By forecasting electricity price trends and formulating futures strategies, the study evaluates the hedging and profitability performance of futures trading under different market conditions. Results show that the proposed model effectively mitigates risks in volatile market environments. Full article

High renewable penetration in microgrids makes low-carbon economic dispatch under uncertainty challenging, and single-agent deep reinforcement learning (DRL) often yields unstable cost–emission trade-offs. This study proposes a dual-agent DRL framework that explicitly balances operational economy and environmental sustainability. A Proximal Policy Optimization (PPO) agent focuses on minimizing operating cost, while a Soft Actor–Critic (SAC) agent targets carbon emission reduction; their actions are combined through an adaptive weighting strategy. The framework is supported by carbon emission flow (CEF) theory, which enables network-level tracing of carbon flows, and a stepped carbon pricing mechanism that internalizes dynamic carbon costs. Demand response (DR) is incorporated to enhance operational flexibility. The dispatch problem is formulated as a Markov Decision Process, allowing the dual-agent system to learn policies through interaction with the environment. Case studies on a modified PJM 5-bus test system show that, compared with a Deep Deterministic Policy Gradient (DDPG) baseline, the proposed method reduces total operating cost, carbon emissions, and wind curtailment by 16.8%, 11.3%, and 15.2%, respectively. These results demonstrate that the proposed framework is an effective solution for economical and low-carbon operation in renewable-rich power systems. Full article

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

The building sector accounts for approximately 30% of global energy use. The demand for energy-efficient, high-performance buildings is increasing given the increasing awareness of the climate crisis. The building envelope greatly influences overall building energy performance. Considering the broad shift from passive to adaptive systems, smart window technologies are attracting attention. Despite their potential, few scholars have examined occupant comfort in spaces with smart windows. This gap is addressed herein by comparatively analyzing occupants’ responses to thermal and visual environments in a room with a smart window (RoomSW) and a room with a conventional window (RoomCW) in a residential building in winter. The smart window is operated via a glare-prevention tint control strategy. The results reveal that under thermal conditions comparable to those in an actual dwelling, wintertime smart window tinting for glare prevention does not decrease occupants’ thermal sensation or satisfaction. Regarding visual comfort, conditions in RoomSW and RoomCW satisfy the minimum illuminance requirement of 200 lx, but glare occurs in RoomCW with a mean New Daylight Glare Index (DGI_N) of 24.1, compared to 9.6 in RoomSW. Questionnaire results indicate greater satisfaction with the luminous environment in RoomSW relative to RoomCW, with scores of +1.4 and +0.2, respectively. Full article