Search Results (24,659)

MXenes are relatively new 2D materials made up of carbides and/or nitrides of transition metals with a chemical formula M_n+1X_nT_x. They are usually fabricated by chemically etching a ceramic phase. MXenes possess tunable catalytic, optical, and electronic properties, which have attracted significant research interest, primarily in energy storage and biosensing applications. Since their first fabrication in 2011, there has been a rapid increase in studies investigating the use of MXenes in a wide range of applications. In this review, the synthesis methods of MXenes are discussed. Then, the potential application of MXenes in cancer treatment is highlighted based on current research. The ability of MXene to convert light, usually NIR (I and II), to heat with improved conversion efficiencies makes it a competitive candidate for photothermal cancer therapy. Moreover, the surface of MXenes can be modified with drugs or nanoparticles, thereby achieving synergistic photo/chemo/, and sonodynamic therapy. This review also examines the available research on the biocompatibility and cytotoxicity of MXenes. Full article

This narrative review examines how ecological and economic perspectives can be integrated to support ecosystem services management and biodiversity conservation. We synthesize core valuation approaches (accounting-based exchange values versus welfare-based measures), discuss their appropriate uses and limitations, and illustrate implications through selected cases in watershed protection, protected areas, and forest carbon. We then review design features of Payments for Ecosystem Services (PES) with attention to additionality, leakage, and equity, and distill lessons for policy mixes that combine market-based instruments with regulatory and informational tools. Finally, we outline opportunities and risks in applying artificial intelligence to ecological–economic analysis, emphasizing accuracy–energy trade-offs and responsible data practices. Across topics, we prioritize mechanism-focused interpretation, triangulate findings from representative studies, and highlight decision-relevant takeaways rather than comprehensive coverage. We conclude with practical recommendations for analysts and policymakers: align valuation method with decision context; pair PES with targeting and monitoring; embed price-based instruments in adaptive policy mixes; and adopt transparent, efficiency-aware analytic workflows—especially when using computationally intensive methods. Full article

Global warming is a critical issue today, largely due to the widespread use of fossil fuels in everyday life. One promising solution to reduce reliance on conventional energy sources is to promote the use of renewable power. In particular, to encourage the use of renewable energy in industrial sectors which involve development and manufacture of the industrial artifacts, there is continuous demand for tracing energy sources within the production processes. However, given a sophisticated industrial product that involves diverse and extensive components and their suppliers, the traceability analysis across its production is a critical challenge for ensuring the full utilization of renewable energy. To alleviate this issue, this paper presents a functional framework to support tracing the usage of renewable energy by integrating the Large Language Models (LLMs) and logic programming across supply chain networks. Specifically, the proposed framework contains the following components: (1) adopting graph-based models to process and manage the extensive information within supply chain networks; (2) using the Retrieval-Augmented Generation (RAG) techniques to support the LLM for processing the information related to supply chain networks and generating relevant responses with structured representations; and (3) presenting a logic programming-based solution to support the traceability analysis of renewable energy regarding the responses from the LLM. As a case study, we use a public dataset to evaluate the proposed framework by comparing it to the RAG-based LLM and its variant. Compared to baseline methods solely relying on LLMs, the experiments show that the proposed framework achieves significant improvement. Full article

Obtaining evapotranspiration (ET) estimates at high spatiotemporal resolution is a fundamental prerequisite for clarifying the patterns and controlling factors of agricultural water consumption in drylands. However, most existing ET products are provided at daily or coarser spatial–temporal scales, which limits the ability to capture short-term variations in crop water use. This study developed a novel hourly 10-m ET estimation method that combines remote sensing with machine learning techniques. The approach was evaluated using agricultural sites in two arid regions: the Heihe River Basin in China and the Bekaa Valley in Lebanon. By integrating hourly eddy covariance measurements, Sentinel-2 reflectance data, and ERA5-Land reanalysis meteorological variables, we constructed an XGBoost-based modeling framework for hourly ET estimation, and incorporated the SHapley Additive exPlanations (SHAP) method for model interpretability analysis. Results demonstrated that the model achieved strong performance across all sites (R² = 0.86–0.91, RMSE = 0.04–0.05 mm·h⁻¹). Additional metrics, including the Nash–Sutcliffe efficiency coefficient (NSE) and percent bias (PBIAS), further confirmed the model’s robustness. Interpreting the model with SHAP highlighted net radiation (Rn), 2-m temperature (t2m), and near-infrared reflectance of vegetation (NIRv) as the dominant factors controlling hourly ET variations. Significant interaction effects, such as Rn × NIRv and Rn × t2m, were also identified, revealing the modulation mechanism of energy, vegetation status and temperature coupling on hourly ET. The study offers a practical workflow and an interpretable framework for generating high-resolution ET maps, thereby supporting regional water accounting and land–atmosphere interaction research. Full article

Background: Multiple sclerosis (MS) is a chronic auto-immune neuroinflammatory disorder presenting as a range of systemic and neurological symptoms, including cognitive impairment. Emerging evidence suggests that diets targeting brain health—such as the Mediterranean (MED) and Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diets—may improve cognitive function; however, studies examining their role in people living with MS are limited. Methods: We examined cross-sectional associations between diet and cognition data from 967 participants in the United Kingdom Multiple Sclerosis Register (UKMSR). Dietary pattern scores (alternate Mediterranean; aMED, and MIND) were derived from the 130-item EPIC-Norfolk food frequency questionnaire. Cognition was assessed using the MS-specific Cognitron-MS (C-MS) battery (13 tasks) and summarised as overall cognition (global G factor) and four domains (object memory, problem solving, information processing speed [IPS], and words memory). Cognitive outcomes were expressed as Deviation-from-Expected (DfE) scores standardised to demographic and device characteristics using external regression-based norms. Linear models were adjusted for total energy intake, MS phenotype, disease duration since diagnosis, and current disease-modifying therapy (DMT) use. Interactions tested moderation by MS phenotype (relapsing vs. progressive MS) and current DMT use (yes vs. no). Sensitivity analyses included within-domain multiple-comparison control, rank-based inverse-normal transformation, and winsorisation. Results: Greater alignment with aMED and MIND dietary patterns were associated with higher scores in specific cognitive domains but not in overall cognition. Higher aMED scores were associated most consistently with better IPS, while higher MIND scores were additionally associated with better words memory. In categorical models, participants with the middle or highest tertiles of aMED or MIND scores performed up to ~0.4 SD better on tasks of Verbal Analogies, Word Definitions, Simple Reaction Time, Words Memory Immediate, or Words Memory Delays compared with those in the lowest tertile. These findings were robust across sensitivity analyses. Stratified analyses showed differential cognitive performance and diet-cognition associations by MS phenotype and DMT use. Conclusions: Mediterranean and MIND dietary patterns showed modest cross-sectional associations with specific cognition domains, with differential cognitive performance in different subgroups according to MS phenotype and DMT use. Although causal inference is not possible, our findings indicate future MS-related dietary studies (longitudinal and/or randomised controlled trials) examining cognitive function domains across different MS subgroups are warranted. Full article

The large-scale integration of renewable energy sources has made power generation highly susceptible to climate variability, increasing operational risks within power systems. The growing frequency of extreme weather events has further intensified uncertainty and stochasticity, thereby elevating risks to supply security. To enhance the operational resilience of modern power systems under extreme weather conditions, this study proposes a multi-temporal and multi-spatial power supply planning model that explicitly incorporates the impacts of such events. First, the effects of extreme weather on the source–grid–load framework are analyzed, and a radiation attenuation model for the rainy season as well as a spatiotemporal evolution model for hurricanes are developed. Subsequently, a climate-dependent power output model is established, employing the Finkelstein–Schafer statistical method to construct a Typical Meteorological Year, which serves as input for the reliable power source modeling. Furthermore, a two-stage power supply planning model based on generation adequacy was established to optimize the location and capacity of various types of backup power sources. Case studies conducted on the IEEE 24-bus system demonstrate that optimized planning of thermal power units and energy storage systems can mitigate the overall power shortfall during extreme weather events, thereby improving the system’s ability to maintain a reliable electricity supply under adverse climate conditions. Full article

Background: Soccer’s varied physical demands require meticulous load monitoring, which is now being advanced by combining GPS for external metrics and NMR-based metabolomics for internal metabolic profiling. This study aimed to investigate how player position influences the metabolomic profile (as a marker of internal load) under known match effort (external load). Methods: This was a longitudinal observational descriptive study involving 12 professional soccer players from the U-20 São Paulo Football Club, enrolled in the 2022 São Paulo State Under-20 Football Championship. Players were monitored across six matches during the season, culminating in a total of 49 individual match observations from those players (4-2-3-1 formation: Central Defenders [CD], n = 9; Full Backs [FB], n = 9; Central Midfielders [CM], n = 14; Wide Midfielders [WM], n = 12; Forwards [F], n = 5). Internal load was assessed via urinary metabolomics, with urine samples collected 24 h post-match. A non-targeted, global metabolomics approach was employed using nuclear magnetic resonance (NMR) spectroscopy. External load was monitored using GPS tracking devices. Multivariate analyses included partial least squares discriminant analysis (PLS-DA), and heat maps. Results: Metabolomic analysis identified 38 metabolites with a Variable Importance in Projection (VIP) score > 1.0, revealing perturbations in carbohydrate metabolism and the tricarboxylic acid (TCA) cycle, amino acid and peptide metabolism, pyrimidine metabolism, and ketone body pathways, and effectively discriminating post-match recovery metabolic profiles. External load metrics varied significantly by player position: CMs covered greater distances below 20 km/h (8702.93 ± 1271.89 m), exhibited higher relative distance (114.29 ± 7.67 m/min), total distance (9193.21 ± 1261.35 m), and player load (945.71 ± 135.82 a.u.); CDs achieved higher peak speeds (31.78 ± 1.20 m/s); and WMs performed greater sprint distances (168.11 ± 91.69 m). Metabolomic profiles indicated that CMs showed stronger associations with markers of muscle damage and inflammation, whereas CDs and WMs were more closely linked to energy metabolism and oxidative stress. Conclusions: These results highlight the importance of a personalized approach to training load monitoring and recovery strategies, considering the distinct physiological and metabolic demands associated with each player position. Full article

The objective of person re-identification (Re-ID) is to recognize a given target pedestrian across different cameras. However, perspective variations, resulting from differences in shooting angles, often significantly impact the accuracy of person Re-ID. To address this issue, this paper presents an attention-enhanced person Re-ID algorithm based on appearance–gait information interaction. Specifically, appearance features and gait features are first extracted from RGB images and gait energy images (GEIs), respectively, using two ResNet-50 networks. Then, a multimodal information exchange module based on the attention mechanism is designed to build a bridge for information exchange between the two modalities during the feature extraction process. This module aims to enhance the feature extraction ability through mutual guidance and reinforcement between the two modalities, thereby improving the model’s effectiveness in integrating the two types of modal information. Subsequently, to further balance the signal-to-noise ratio, importance weight estimation is employed to map perspective information into the importance weights of the two features. Finally, based on the autoencoder structure, the two features are weighted and fused under the guidance of importance weights to generate fused features that are robust to perspective changes. The experimental results on the CASIA-B dataset indicate that, under conditions of viewpoint variation, the method proposed in this paper achieved an average accuracy of 94.9%, which is 1.1% higher than the next best method, and obtained the smallest variance of 4.199, suggesting that the method proposed in this paper is not only more accurate but also more stable. Full article

Background: Women with gestational diabetes mellitus (GDM) are vulnerable to oxidative stress, yet limited data exist on the antioxidant potential of their breast milk. This study aimed to evaluate the antioxidant capacity and basic composition of colostrum in women with GDM compared to healthy controls, focusing on total antioxidant capacity (TAC) and enzymatic antioxidants: superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx). Methods: The study included 77 lactating mothers: 56 with gestational diabetes (15 managed with diet/exercise—GDM G1; 41 required insulin—GDM G2) and 21 healthy controls. Colostrum samples were collected on days 3–5 postpartum and analyzed for macronutrients and antioxidant enzymes. To enable comparisons across study groups and to explore associations with maternal characteristics, a range of statistical methods was applied. A taxonomic (classification) analysis was then performed using the predictors that best fit the data: study group membership, maternal hypothyroidism history (from the medical interview), and gestational weight gain. Results: TAC was significantly lower in the GDM G2 group compared to GDM G1 and controls (p = 0.001), with no differences in enzymatic antioxidants. The control group had the highest energy (p = 0.048) and dry matter content (p = 0.015), while protein, fat, and carbohydrate levels did not differ significantly. After dividing the study group into four clusters, based on maternal health factors, including GDM status and thyroid function, TAC levels differed significantly between clusters, with the highest values observed in Cluster 3 (healthy controls without thyroid dysfunction) and the lowest in Cluster 2 (GDM and hypothyroidism). Analysis of colostrum composition revealed significant differences in energy content (p = 0.047) and dry matter concentration (p = 0.011), while no significant differences were found in other macronutrients. Conclusions: Our findings suggest that maternal metabolic and endocrine conditions, such as GDM and thyroid dysfunction, may differentially influence the nutritional and functional properties of colostrum—particularly its antioxidant potential. Full article

The role of distributed energy resources in distribution networks is evolving to support system operation, facilitated by their participation in local flexibility markets. Future scenarios envision a significant share of low-power resources providing ancillary services to efficiently manage network congestions, offering a competitive alternative to conventional grid reinforcement. Additionally, the interaction between distribution and transmission systems enables the provision of flexibility services at higher voltage levels for various applications. In such cases, the aggregated flexibility of low-power resources is typically represented as a capability envelope at the interface between the distribution and transmission network, constructed by accounting for distribution grid constraints and subsequently communicated to the transmission system operator. This paper revisits this concept and introduces a novel approach for envelope construction. The proposed method is based on a surrogate model composed of a limited set of standard power flow components—loads, generators, and storage units—enhancing the integration of distribution network flexibility into transmission-level optimization frameworks. Notably, this advantage can potentially be achieved without significant modifications to the optimization tools currently available to grid operators. The effectiveness of the approach is demonstrated through a case study in which the adoption of distribution network surrogate models within a coordinated framework between transmission and distribution operators enables the provision of ancillary services for transmission resilience support. This results in improved resilience indicators and lower control action costs compared to conventional shedding schemes. Full article

This paper proposes a novel methodology based on two intelligent algorithms for regulating the power output of a multi-rotor turbine system. A proportional-integral plus second-order integral regulator is utilized to regulate the energy output of an induction generator. The designed controller is characterized by its ease of configuration, cost-effectiveness, high robustness, and ease of implementation. The controller’s parameters are tuned using a genetic algorithm (GA) and a rooted tree optimization (RTO) algorithm, with the objective of maximizing operational performance and power quality. In accordance with the proposed design methodology, the optimal values for the parameters of the designed strategy are attained through the implementation of integral time-weighted absolute error (ITAE). The present controller has been designed to deviate from conventional controllers, and a comparison will be made between the two using MATLAB under various operating conditions. The operational performance was evaluated in comparison to the conventional algorithm in terms of current quality, torque ripples, threshold overshoot, system parameter changes, and so forth. The experimental results, as measured by the tests conducted, demonstrated that the proposed RTO-based regulator exhibited enhancements of up to 89.88% (traditional control) and 51.92% (GA) in active power ripples, 68.19% (compared to traditional control) in ITAE, 51.91% (traditional control) in reactive power overshoot, and 0.5% (compared to GA) in active power response time. Conversely, the proposed GA-based regulator yielded a steady-state error value that was 96.55% superior to the traditional approach and 86.48% more accurate than the RTO algorithm. Moreover, the efficacy of the RTO-based control system was found to be considerably augmented under variable system parameters. Total harmonic distortion improvements of 69% were observed compared to traditional control methods, and 1% compared to the GA technique. The findings of this study offer significant insights into enhancing the robustness of multi-rotor turbine systems and improving power quality. Full article

The growing integration of distributed energy resources and electrification of end users is driving the need for greater system flexibility in modern power grids. Various electrical assets can contribute to this flexibility, either inherently or through external control mechanisms, although their suitability varies even within the same category of assets. This paper presents a novel methodological approach to assess the flexibility potential of electrical assets based on their inherent technical characteristics and their intended installation context. Inspired by the principles of the Smart Readiness Indicator (SRI) for buildings, the proposed method employs a scoring system to evaluate a set of key functionalities that determine an asset’s readiness to contribute to system flexibility, then through a weighted sum a final index is obtained. These scores are combined through a weighted aggregation to produce a single, easy-to-interpret index that synthesizes multiple characteristics, enabling comparisons across different technologies. Unlike the SRI, this approach is not focused on certification but rather on providing a decision-support tool for end-users. The applicability of the method is demonstrated through a case study evaluating a photovoltaic inverter, followed by a sensitivity analysis to assess the robustness of the weighting scheme. Results indicate that the proposed index provides a transparent and replicable means of quantifying flexibility potential, supporting more informed planning and investment decisions. Full article

The piezoelectric coating structure constitutes the main configuration of contemporary energy harvesting systems, and its development requires accurate modeling of electromechanical coupling behavior under mechanical loads. The present work prepares a framework to analyze orthotropic piezoelectric coating–substrate systems; based on the fundamental solution theory, it derives two-dimensional Green functions from closed-form elementary functions. The formulation can establish the mesh-free solution paradigm through addressing tangential line force loading onto a coated surface. This method helps reconstruct full-field electromechanical responses upon arbitrary mechanical loading by integrating superposition principles and Gaussian quadrature technologies. An important application is in optimizing coating thickness, where parametric research suggests that piezoelectric layer geometry is non-linearly correlated with energy conversion efficiency. Notably, analytical sensitivity coefficients of this framework contribute to gradient-based optimization algorithms, which enhances efficiency compared with traditional empirical frameworks. Full article

The accelerating demand for climate action has underscored the need to link financial innovation with clean energy adoption. This study examines the interplay between green finance, renewable energy consumption, and CO₂ emissions across 15 countries from 2013 to 2022. Green finance is proxied by green bond issuances and environmental protection expenditures, capturing both market-based and fiscal flows. Using panel econometric methods, including fixed effects with Driscoll–Kraay corrections, Prais–Winsten regressions with PCSE, and Feasible Generalized Least Squares (FGLS), the analysis accounts for heteroscedasticity, autocorrelation, and cross-sectional dependence. Results show how green finance significantly reduces emissions, both directly and indirectly, through its positive influence on renewable energy deployment. Renewable energy consumption shows a robust negative association with CO₂ emissions, confirming its pivotal role in energy transition. A mediation analysis further demonstrates that renewable energy partially transmits the effect of green finance on environmental performance. The findings highlight the dual function of green finance in mobilizing investment and accelerating decarbonization, offering timely insights for policymakers seeking effective pathways toward sustainable, low-carbon economies. Full article

Ketosis is one of the most common metabolic disorders in high-yielding dairy cows in early lactation. It has a negative impact on milk yield, reproduction, and general health of the animals. The present review aims to systematize and critically analyze current scientific data on the monitoring, diagnosis, prevention, and treatment of subclinical and clinical ketosis, with the aim of developing an applicable protocol for good veterinary medical practices (GMP). Based on the comparative analysis method of data from the literature and clinical practice, β-hydroxybutyrate (BHBA) in blood is confirmed as the gold standard for diagnosis with specificity and sensitivity above 90%. Indicators such as fat/protein (F/P) > 1.4 and NEFA > 0.4 mmol/L, as well as reduced citrate content in milk, have been evaluated as useful screening tools, although with lower diagnostic value. Despite the advantages of some indirect methods (such as F/P), critical analysis shows that they are strongly influenced by physiological status, lactation stage, and diet and cannot replace direct blood tests. Preventive approaches emphasize the importance of stable nutrition in the pre- and post-calving period, restriction of ketogenic feeds, and use of oral glucose precursors. Literature analysis shows that propylene glycol is effective, but with prolonged use it can reduce appetite. Combined antiketotic products have also been introduced, providing not only energy support but also liver protection. Particular attention has been paid to monensin (applied in the commercial product “Kexxtone”)—a polyether antibiotic with sustained release, which reduces the incidence of ketosis by over 50% and increases milk yield in the first weeks after calving. However, its high cost, antibiotic nature, and need for veterinary supervision limit its universal use. The treatment protocol is differentiated according to the clinical type: glucose I. V. and propylene glycol in type I ketosis and avoidance of glucocorticoids in suspected type II (hepatic steatosis). In the critical analysis It is noted that improper use of glucocorticoids can lead to a worsening of the condition. A structured protocol for DVMP (Dairy Veterinary Medical Practice) is proposed, which includes targeted metabolic monitoring (NEFA, BHBA, F/P); proven preventive regimens (Kexxtone, propylene glycol, balanced rations), and staged prevention and treatment according to the form of ketosis. The adaptation of good practices to the scale of the farm and the level of knowledge and skills of the staff for their correct application remains a challenge. Providing training, a standardized control log, and access to field diagnostic tools is key to the success of any protocol. Full article