Search Results (1,615)

The increasing discharge of cobalt-containing effluents from metallurgical, electroplating, and battery-related industries necessitates the development of efficient and stable separation technologies. In this study, a sodium dodecyl sulfate (SDS)-assisted micellar-enhanced ultrafiltration (MEUF) process was systematically evaluated for the removal of Co²⁺ from aqueous solutions using a flat-sheet polyethersulfone (PES) membrane operated under crossflow conditions. The effects of surfactant concentration, initial solution pH, cobalt concentration, background electrolyte, and extended filtration time were examined to assess process performance and operational stability. Direct ultrafiltration of 50 mg L⁻¹ Co²⁺ without surfactant resulted in limited rejection (~18%). The introduction of SDS markedly improved removal efficiency, achieving >99% rejection at and above 1 critical micelle concentration (CMC). An SDS dosage of 1 CMC provided an optimal balance between permeate flux (~155 L m⁻² h⁻¹) and cobalt removal (>99%). The system maintained high rejection efficiency across a pH range of 3–9, demonstrating robust cobalt–micelle interactions. Increasing the initial cobalt concentration from 10 to 50 mg L⁻¹ caused a moderate decline in flux but did not significantly affect rejection efficiency. In contrast, elevated ionic strength due to NaNO₃ addition reduced both flux and cobalt removal, highlighting the influence of competing ions on micelle-mediated separation. Long-term continuous operation for 40 h showed stable permeate flux and sustained cobalt rejection above 99%, indicating minimal fouling. FTIR and SEM–EDS analyses confirmed membrane chemical stability and negligible cobalt deposition. These findings demonstrate that SDS-based MEUF is an effective and operationally stable approach for cobalt removal from contaminated water systems. Full article

This study presents the design, development, and evaluation of an integrated solar-powered seed sowing and fertilizer-watering system to enhance planting efficiency, improve resource utilization, and reduce labor in small-scale agriculture. The prototype features a 600-watt photovoltaic panel, DC motors, and a manual mechanical dispensing mechanism, enabling automated seed placement, water distribution, and fertilizer application in off-grid farm environments. Development was guided by a product-based design approach using locally sourced materials to ensure cost-effectiveness, maintainability, and accessibility for rural users. Field simulations and performance trials assessed charging efficiency, seed sowing accuracy, irrigation flow rate, and fertilizer dispensing precision. Results showed high consistency in operational performance, including up to 99% seed placement accuracy, efficient water delivery, and reliable fertilizer timing, with solar energy providing adequate power storage during periods of peak irradiance. Expert evaluations using a standardized instrument demonstrated strong agreement on the system’s usability, material availability, ergonomic features, modularity, and overall functional design. Findings indicate that the system can minimize manual labor, reduce operational costs, and offer a practical transition toward clean-energy–assisted mechanization in agriculture. The study concludes that integrating renewable energy into essential farm operations can contribute to sustainable productivity and recommends future enhancements through sensor integration, increased battery capacity, and adaptive control mechanisms to support wider agricultural adoption. Full article

The present investigation was designed to assess how perinatal contact with the pyrethroid insecticide cyfluthrin (CY) influences cognitive performance in developing rat progeny and to clarify the contributing cellular events through examination of neuroinflammatory processes alongside pyroptotic and apoptotic pathways. An experimental framework involving CY administration during gestation was implemented using Sprague–Dawley (SD) dams, with subsequent monitoring of placental parameters and neonatal outcomes. Once offspring reached postnatal day twenty-one, their behavior was characterized via a battery consisting of the open field paradigm, novel object recognition task, and the Morris water navigation test. Hippocampal tissue architecture and fine structural details were visualized by employing hematoxylin–eosin (HE) staining and Nissl substance labeling. Protein and transcript abundances for pro-inflammatory mediators (TNF-α, IL-6), synaptic constituents (postsynaptic density protein-95, PSD-95; synaptophysin, SYP), and pyroptotic machinery components (NLRP3, GSDMD, Caspase-1) within hippocampal homogenates were quantified through immunoblotting and real-time quantitative PCR procedures, and the spatial distribution of these molecules was validated via immunohistochemical detection. Neuronal apoptosis was assessed by TUNEL staining. The results demonstrated that gestational CY exposure led to reduced placental weight and diameter, decreased blood sinus area in the labyrinth zone, lower offspring birth weight, and impaired catch-up growth. Behavioral tests revealed that CY-exposed offspring exhibited diminished spontaneous locomotor activity, impaired novel object recognition memory, and significant deficits in spatial learning and memory. Pathological analysis showed disorganized neuronal arrangement and reduced Nissl bodies in the hippocampal CA1 region. Compared to the control group, CY exposure markedly upregulated the protein expression of TNF-α and IL-6, downregulated PSD-95 and SYP, activated the NLRP3/GSDMD/Caspase-1-mediated pyroptotic pathway, and increased the expression of the apoptotic protein Caspase-3, culminating in a significant increase in hippocampal neuronal apoptosis. In conclusion, early-life exposure to cyfluthrin impairs cognitive function in offspring, an effect closely associated with the induction of hippocampal neuroinflammation and the activation of pyroptotic and apoptotic pathways. These findings provide novel toxicological evidence for a more comprehensive assessment of the potential health risks posed by CY exposure in human populations. Full article

The increasing integration of renewable energy introduces uncertainty in microgrid operation, making effective energy management more challenging. Rolling-horizon optimization is used to address this challenge by enabling periodic decision updates; however, most existing approaches rely on fixed optimization horizons and predetermined update frequencies. When prediction accuracy decay (PAD) is considered in adaptive rolling-horizon approaches, it is represented using a fixed decay value, not an online indicator that compares forecasted and actual renewable generation during operation. This leads to suboptimal re-optimization timing, unnecessary computational effort, excessive battery switching, or delayed corrective actions. To address these limitations, this paper proposes a PAD-driven adaptive rolling horizon (ARH) approach, in which re-optimization is triggered using an online PAD indicator computed from the percentage deviation between forecasted and realized renewable generation data. Re-optimization is activated when the PAD indicator exceeds a predefined threshold, enabling adaptive scheduling updates according to real-time forecasting degradation. The problem is formulated as a robust mixed-integer linear programming (MILP) model of a high renewable penetration microgrid, including battery degradation and switching penalties. The energy self-sufficiency ratio (SSR) is used as a key sustainability performance indicator to assess the extent to which local renewable generation and storage satisfy microgrid demand. The proposed approach is first compared with a fixed rolling-horizon approach using a fixed re-optimization interval of 1 h, where the results show a profit improvement of 10.5%. A sensitivity analysis tested the proposed approach under bounded renewable forecast uncertainty levels up to ±15 and different battery capacities. The results indicate that performance is strongly influenced by forecast accuracy and battery capacity, with higher economic gains under low uncertainty and more conservative operation under high uncertainty. Full article

The increasing integration of distributed energy resources (DERs), including photovoltaic systems, battery energy storage systems, electric vehicles, and flexible loads, is transforming modern power systems and creating new challenges for coordination, control, and cybersecurity. Conventional Virtual Power Plant (VPP) architectures typically rely on centralized energy management systems, which may face scalability limitations, communication bottlenecks, cybersecurity risks, and reduced resilience to failures. This paper presents a blockchain-enabled decentralized Virtual Power Plant framework for secure and autonomous coordination of distributed energy resources. The proposed architecture combines blockchain technology, smart contracts, IoT-based communication infrastructure, and decentralized energy management functions within a unified multi-layer coordination framework. Smart contracts automate energy scheduling, peer-to-peer transaction validation, and settlement processes, reducing dependence on centralized control entities. Lightweight blockchain consensus mechanisms are employed to improve scalability while limiting computational overhead. The effectiveness of the proposed framework is evaluated through simulation-based case studies involving decentralized DER coordination, peer-to-peer energy trading, and resilience assessment under node-failure conditions. Its performance is compared with that of a conventional centralized VPP architecture in terms of scalability, coordination reliability, communication overhead, transaction transparency, and fault tolerance. The results indicate that the decentralized framework improves operational resilience, coordination transparency, and scalability under increasing DER participation while maintaining satisfactory energy balancing performance. Although blockchain-based coordination introduces additional transaction latency, the proposed approach enhances cybersecurity, reduces dependence on centralized control structures, and provides a flexible foundation for future intelligent smart-grid applications. Full article

Although critical to modern logistics, heavy-duty commercial vehicles face mounting pressure to improve energy efficiency and reduce emissions. The aim of this study was to evaluate the techno-economic and environmental performance of four vehicle configurations: internal combustion engine (ICE) tractors and battery electric tractors (BETs), each respectively paired with either a conventional or an electrified trailer. To optimize energy utilization while proactively mitigating the longitudinal impact risks that trigger vehicle instability, a coordinated control strategy based on power decoupling and a real-time, efficiency-oriented torque distribution strategy were designed. Simulations under C-WTVC and CHTC-TT cycles revealed that electrified trailers substantially improved the system efficiency. Under fully loaded conditions, BETs paired with electrified trailers reduced the direct energy expenditures by 76.5% compared to conventional ICE vehicles. Notably, compared to pure electric tractors with conventional trailers, the addition of electrified trailers further reduced the energy consumption by 29.1%. Meanwhile, ICE tractors paired with electrified trailers achieved a 35.6% energy cost reduction. Furthermore, a fuel-cycle well-to-wheels (WTW) assessment of the use phase, based on a specified regional grid emission factor, demonstrated that the BETs and hybrid configurations reduced the operational greenhouse gas emissions by 64.9% and 29.3%, respectively, compared to the baseline. These findings indicate that trailer electrification offers consistent economic and environmental benefits under the simulated scenarios, thereby providing a robust theoretical foundation for the low-carbon transition, transportation sustainability, and selection of sustainable technologies in road freight. Full article

Executive functions (EFs) are key cognitive processes for behaviour. However, there is little information about interaction with the dimensions of health-related quality of life (HRQoL), therefore the objective of this study was to analyse the association between lifestyle habits (physical activity and screen time), sleep, HRQoL and EFs in children and adolescents. Specifically, this study aimed to identify the extent to which perceived well-being dimensions are associated with EFs and to determine the potential mediating role of HRQoL in the relationships between lifestyle habits and these cognitive domains, examining whether these direct and indirect pathways remain robust after adjusting for gender and age. A total of 943 children and adolescents (51.3% female) aged 10–17 years participated. Lifestyle parameters (PA Krece Plus, sleep duration and KIDSCREEN-10 questionnaire) and EFs (CogniFit neurocognitive assessment battery) were evaluated. The analysis of the individual KIDSCREEN-10 items revealed that perception of school performance presented the most consistent association with EFs, being positively related to attention (b = 16.39, p = 0.018), cognitive flexibility (b = 30.65, p = 0.005), inhibition (b = 24.66, p = 0.022), and working Memory (b = 42.33, p < 0.001). Furthermore, parental fairness reported a significant association for three out of four domains: attention (b = 13.89, p = 0.006), flexibility (b = 22.93, p = 0.003), and working Memory (b = 25.42, p < 0.001). Having enough time for self was also significantly related to attention performance (b = 12.60, p = 0.026). Regarding lifestyle habits, the composite lifestyle index (PA + ST) showed the most consistent positive association across all cognitive domains, while sleep duration was inversely associated with attention, cognitive flexibility, and working Memory. The mediation analysis revealed that global HRQoL significantly mediated the relationship between lifestyle habits and executive functions, accounting for 9.55% of the total effect on attention, 5.45% on cognitive flexibility, and 4.14% on working memory, whereas no mediation was observed for inhibition. In conclusion personal time, parental fairness, and school adjustment were positively associated with EFs. HRQoL and physical activity levels also showed consistent links with all EFs, whereas sleep duration was inversely related. Furthermore, mediation analysis revealed that global HRQoL acts as a critical indirect pathway, explaining a significant proportion of the lifestyle habits’ total effect on attention, cognitive flexibility, and working memory. Overall, these findings highlight the multifactorial and interrelated mechanisms shaping executive functioning in children and adolescents. Full article

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims to solve these problems by proposing a hybrid attention-based BiLSTM–RF model, which combines wavelet-based signal denoising, incremental capacity analysis (ICA)-based feature extraction, stacked Bidirectional Long Short-Term Memory (BiLSTM) networks, multi-head self-attention, principal component analysis (PCA)-based feature compression, and ensemble regression using a Random Forest (RF) model with adaptive weighted fusion. The proposed framework was tested on the NASA battery datasets (B0005, B0006, B0007 and B0018) and was further validated on the Oxford Battery Degradation Dataset using leave-one-battery-out cross validation conditions. Experimental results indicated that, in general, the proposed framework outperformed the evaluated benchmark models (CNN-LSTM, BiLSTM, and RF models) in terms of the prediction error, with a minimum RMSE value of 0.0229 for NASA battery B0007 and 0.0024 for Oxford Cell3. Ablation analysis also showed that the combination of wavelet denoising, PCA compression, temporal sequence learning and ensemble regression played a role in the overall SOH estimation performance. These results show that the proposed hybrid approach is effective and stable for SOH estimation in different battery degradation trajectories under the tested experimental conditions. Full article

Aim: This review summarizes the current evidence on cognitive impairment in patients with glioma, with particular emphasis on the underlying mechanisms, methods for objective assessment of cognitive deficits, and currently available therapeutic strategies. Methods: Relevant literature was identified through searches of the PubMed, Cochrane Library, and Google Scholar databases. Studies addressing the mechanisms, assessment, and management of cognitive impairment in patients with glioma published between January 2000 and February 2026 were reviewed to summarize current evidence and emerging therapeutic strategies. A structured literature search and study selection process was performed. Results: Cognitive deficits represent a heterogeneous clinical problem affecting the majority of patients diagnosed with gliomas. These impairments are multifactorial in origin, resulting both from direct infiltration of white matter by the tumor process and from the cumulative toxicity of treatment modalities, including radiotherapy, chemotherapy, corticosteroid therapy, and long-term use of antiseizure medications. Analysis of the current literature confirms that modern radiotherapy techniques (e.g., hippocampal-sparing approaches) and isocitrate dehydrogenase (IDH)-targeted therapies, such as vorasidenib, may significantly delay cognitive decline. Standardized neuropsychological batteries for the objective assessment of these disturbances remain the preferred tools recommended by the International Cognition and Cancer Task Force (ICCTF). Regarding therapeutic interventions, potential benefits have been demonstrated for supportive pharmacotherapy (including memantine), while individualized neuropsychological rehabilitation has also been shown to improve patients’ quality of life. Full article

Background: Preseason screening is widely used in handball to identify athletes at increased risk of injury, yet the prognostic value of different screening approaches remains unclear. The aim of this study was to systematically review the evidence on preseason screening tests and physical assessments in relation to subsequent injury outcomes in handball players. Methods: A systematic review was conducted according to PRISMA guidelines. PubMed, MEDLINE, CINAHL, and Scopus were searched on 14 March 2026. The first 100 results from Google Scholar were also screened, and backward citation searching was performed. Eligible studies included handball players and examined preseason or baseline screening, functional, musculoskeletal, or physical performance assessments in relation to prospectively recorded injury outcomes. Two independent reviewers performed study selection, data extraction, and risk-of-bias assessment using the QUIPS tool. Due to substantial heterogeneity in screening tools, injury outcomes, and follow-up procedures, meta-analysis was not performed. Results: Eight studies were included. Most were prospective cohorts involving adolescent, youth elite, or elite adult handball players. Shoulder-specific screening variables, particularly external rotation strength, strength imbalances, total rotational motion, and selected rotational adaptations, showed more consistent associations with subsequent shoulder-related outcomes. In contrast, broader movement-screening tools, including the Functional Movement Screen, the 9+ screening battery, and the upper quarter Y-Balance Test, generally showed limited associations with overall injury outcomes. Conclusions: Shoulder-specific preseason assessments may be more closely associated with subsequent shoulder-related outcomes than broader movement-screening tools, although the available evidence remains limited, heterogeneous, and derived exclusively from observational studies. Full article

Soccer performance is characterized by high motor and cognitive complexity, resulting from the interaction between, among others, physical and technical components. However, evidence regarding the relationships among physical performance, motor coordination and soccer-specific technical remains limited. Therefore, this cross-sectional study aimed to investigate the associations among these domains in youth soccer players. Forty-nine male U15 participants (age: 14.3 ± 0.5 years) underwent anthropometric assessments, physical fitness testing (10 m, 30 m sprint, CMJ, YYIRT1), a general motor coordination test (Harre Circuit Test), and soccer-specific technical evaluation (F-MARC test battery). Associations among variables were assessed using Spearman correlations and exploratory principal component analysis (PCA) based on a Spearman correlation matrix with oblimin rotation. Significant associations emerged between general motor coordination, physical performance variables, and several soccer-specific technical skills. The PCA identified three partially overlapping components, cumulatively explaining about 70% of the variance, highlighting the multidimensional and interconnected nature of soccer-related performance capacities. General motor coordination demonstrated relevant loadings in both coordinative/technical and physical-performance-oriented domains. These findings suggest that youth soccer performance should not be interpreted through isolated physical or technical characteristics, but rather as the result of interactions among coordinative, neuromuscular, and technical factors. Consequently, multidimensional and individualized training approaches integrating physical, coordinative, and technical stimuli may represent relevant strategies for youth soccer development. Full article

The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. In this study, we develop an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. We construct a multiloop coupled, heat pump-based integrated thermal management model, including a compressor, heat exchangers, expansion valves, and an electro-thermal battery sub-model. Bench and vehicle-level tests confirm that the model predicts the refrigerant mass flow rate and heating capacity with mean relative errors of 4.76% and 4.30%, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA-NMPC strategy is evaluated under NEDC and CLTC-P driving cycles. Under the investigated NEDC-based high-load assessment with representative operating conditions, the proposed strategy limits the cabin temperature overshoot to 0.35 °C and battery temperature fluctuation to 0.26 °C, while achieving a 6.31% energy saving under high-speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA-NMPC approach can improve thermal management performance under the investigated operating conditions. Full article

The recast Energy Performance of Buildings Directive (EPBD) accelerates the transition from nearly zero-energy buildings (nZEBs) to zero-emission buildings (ZEBs), requiring solar readiness and life-cycle Global Warming Potential (GWP) disclosure. Yet operational performance, future-climate adaptation and whole-life carbon (WLC) are still often assessed separately, limiting actionable evidence for residential ZEB design in northern climates. This study provides an integrated design-decision framework coupling annual IDA-ICE simulations under five weather scenarios, including Urban Heat Island (UHI)-adjusted present and 2080 RCP8.5 + UHI files, with an EN 15978/Level(s)-based WLC assessment in One Click LCA for twelve design cases of a Lithuanian dwelling. For the PV-equipped baseline, heating electricity decreases by 24% and cooling increases by 31% from present conditions to 2080 RCP8.5 + UHI. External shading and night purge provide the strongest annual cooling and operative-temperature-exceedance reductions. The ZEB baseline reduces WLC by 19.0% relative to A0; the biogenic-insulation green-roof case gives the lowest non-storage WLC (−25.2%); and battery-assisted cases provide the largest reductions under the static B6 electricity factor (up to −52.1%). The findings provide case-study evidence that EPBD-aligned residential ZEB design should evaluate passive cooling, PV/storage and material choices jointly, rather than sequentially, when developing future performance thresholds and design guidance. Full article

Thermal management remains a key challenge for lithium-ion batteries in electric vehicles, especially under transient driving and charging conditions. This study develops a coupled thermo-hydraulic model for a liquid-cooled battery thermal management system and uses it to compare four coolants with different thermophysical properties: water, ethylene glycol–water, propylene glycol–water, and Jet-A aviation fuel. Unlike studies that focus mainly on temperature reduction, the present work evaluates battery temperature, hydraulic pump power, and cooling load/heat rejection demand within the same framework. The coolants are tested under the FTP-75 driving cycle and a high-rate charging case while pump speed is varied between 1500 and 4500 rpm. Water provides the strongest cooling performance, reducing the battery temperature during FTP-75 from about 30 °C to 21.2 °C at 1500 rpm and 20.6–20.8 °C at 4500 rpm. During charging, water maintains the battery temperature near 23 °C at 1500 rpm, whereas ethylene glycol–water and Jet-A reach about 46–47 °C. Increasing pump speed improves thermal regulation, particularly for weaker-performing coolants, but it also increases auxiliary demand; for example, the RMS pump power of water during charging rises from 0.039 to 0.735 kW. Overall, the results show that coolant selection in liquid-cooled BTMS requires a balanced assessment of heat removal capability, pumping demand, and heat rejection requirements. Full article

This study analyzes the energy consumption of unmanned aerial vehicles (UAVs) under the influence of external environmental factors, including ambient temperature, wind speed, and turbulence, as well as their effect on battery performance and flight endurance. The aim of the study is to improve the understanding of UAV energy efficiency under different climatic and operational conditions relevant to agricultural and infrastructure-monitoring missions. The results show that external factors substantially affect both UAV power demand and operating time. In particular, wind and turbulence increase energy consumption because of additional aerodynamic drag and the need for repeated stabilization efforts. The proposed framework integrates aerodynamic, thermal, and battery-related factors into a unified energy-consumption model and introduces empirical correction coefficients to improve the applicability of the calculations under different operating conditions. The study also discusses practical approaches to energy-aware mission planning, including route-level considerations and adaptation of flight parameters to environmental conditions. The obtained results indicate that UAV energy efficiency should be assessed not only from nominal battery parameters, but also with account for environmental loading and mission geometry. The proposed framework is relevant for both agricultural and rural infrastructure applications and may support comparative endurance estimation, preliminary route planning, and further development of UAV energy-assessment methods for operation in challenging climatic and operational environments. Full article