Search Results (34,157)

The transition toward a Circular Economy (CE) has received significant attention from academia, industry, and policymakers; however, manufacturing practices remain predominantly linear, generating waste and inefficiencies. This study addresses the lack of accessible sustainability assessment methods by introducing the Circularity Calculator (CC), a novel tool for evaluating circular strategies during the early phases of process development. Unlike existing assessment frameworks, which often require extensive data and customization, the CC can be integrated directly to existing processes to combine environmental and economic impact into a streamlined evaluation process for early decision-making. The research involves collaboration with a leading German automotive manufacturer. Site visits and interviews enabled the identification of material flows and primary waste streams, which informed the definition of relevant indicators. The CC generates a dimensionless index, enabling comparison and prioritization of proposed scenarios without relying on supply-chain-wide data, which is often unavailable at early stages. Implications demonstrate the adaptability of the CC across industrial contexts, supporting conceptual planning and operational phases. Its intuitive design facilitates adoption by practitioners without extensive expertise in sustainability. The tool represents an advance in CE assessment, contributing to Sustainable Development Goals (SDGs) 9, 12, and 17 by promoting sustainable industrial practices, resource circularity, and collaborative evaluation frameworks. Full article

Background: Artificial intelligence (AI) and machine learning (ML) are increasingly used in the diagnosis and management of bone metastases, spanning lesion detection, segmentation, prognostic modeling, fracture risk assessment, and surgical decision support. However, the literature is heterogeneous and rapidly evolving, making it difficult for clinicians to contextualize these developments. Methods: We performed a narrative review of the literature on AI/ML applications in bone metastasis management, focusing on studies that address clinically relevant problems such as detection and segmentation of metastatic lesions, prediction of skeletal-related events and survival, and support for reconstructive decision-making. We prioritized recent, peer-reviewed work that reports model performance and highlights opportunities for clinical translation. Results: Most published studies center on imaging-based diagnosis and lesion segmentation using radiomics and deep learning, with generally high internal performance but limited external validation. Emerging work explores prognostic models and biomechanically informed fracture risk estimation, yet these remain at an early proof-of-concept stage. Very few frameworks are integrated into routine workflows, and explainability, bias mitigation, and health-economic impacts are rarely evaluated. Conclusions: AI and ML tools have substantial potential to standardize imaging assessment, refine risk stratification, and ultimately support personalized management of bone metastases. Future research should focus on externally validated, multimodal models; development of AI-augmented alternatives to the Mirels score; federated multicenter collaboration; and routine incorporation of explainability and cost-effectiveness analyses. Full article

This study investigates the flow and heat transfer characteristics within a printed circuit heat exchanger (PCHE) equipped with airfoil fins. A numerical model of a counter-flow airfoil-fin PCHE was developed, using transcritical methane as the cold medium and a 50 wt% ethylene glycol aqueous solution (50% EGWS) as the hot medium. The effects of the airfoil fin array longitudinal staggering ratio (Ks), transverse pitch ratio (Kb), and longitudinal pitch ratio (Ka) on the thermal-hydraulic performance of the PCHE were systematically analyzed using the thermal performance factor (TPF) for comprehensive evaluation. The optimal configuration was determined to be Ks = 0.2, Kb = 0.5, and Ka = 1.0, achieving a TPF up to 1.18 times higher than that of the baseline structure (Ks = 1.0). The analysis highlights that aggressive heat transfer enhancement incurs a substantial pressure drop penalty; for instance, reducing Ka from 2.0 to 1.0 increases the Nusselt number (Nu) by approximately 13%, while simultaneously increasing the Fanning friction factor (f_Fanning) by 22%, indicating a significant pressure drop cost. The developed correlations exhibit deviations within ±10% of the simulated values over the Reynolds number (Re) range of 8000–25,000, providing a reliable tool for the optimized design of PCHEs. Full article

Aiming at the challenges of complex spatial-temporal correlation and strong nonlinearity in the power prediction of large-scale wind farm clusters, this study proposes a short-term wind power prediction method that combines a dynamic graph structure and a Kolmogorov–Arnold Network (KAN) enhanced neural network. Firstly, a spectral embedding fuzzy C-means (FCM) cluster partition method combining geographic location and numerical weather prediction (NWP) is proposed to solve the problem of insufficient spatio-temporal representation ability of traditional methods. Secondly, a dynamic directed graph construction mechanism based on a stacked wind direction matrix and wind speed mutual information is designed to describe the directional correlation between stations with the evolution of meteorological conditions. Finally, a prediction model of dynamic graph convolution and Transformer based on KAN enhancement (DGTK-Net) is constructed to improve the fitting ability of complex nonlinear relationships. Based on the cluster data of 31 wind farms in Gansu Province of China and the cluster data of 70 wind farms in Inner Mongolia, a case study is carried out. The results show that the proposed model is significantly better than the comparison methods in terms of key evaluation indicators, and the root mean square error is reduced by about 1.16% on average. This method provides a prediction tool that can adapt to time and space changes for engineering practice, which is helpful to improve the wind power consumption capacity and operation economy of the power grid. Full article

Background/Objectives:Pyrus ussuriensis Maxim. has been cultivated in many regions worldwide. This plant is also regarded as a profitable fruit crop for the development of many food and functional products. There is limited research on the application of the LC-MS associated reaction method for screening active compounds. In this study, we developed an analytical technique employing an ultra-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS) system. Methods: The metabolite annotation procedure was used to interpret and validate data analysis via spectral matching against public databases. Results: As a result, metabolites from P. ussuriensis water and EtOH extracts were identified, and their quantities were further evaluated. The established method was employed to determine antioxidant capacity using a pre-incubation UHPLC-2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, thereby identifying antioxidant ingredients. The antioxidative interference of active constituents was predicted by calculating the decrease in the peak areas of the chemical composition detected in chromatograms between treated and non-treated samples. Furthermore, drug-likeness was also assessed via pharmacokinetics (absorption, distribution, metabolism, and excretion: ADME) evaluation. Conclusions: The online UHPLC-MS-DPPH method would be a powerful tool for the rapid characterization of antioxidant ingredients in plant extracts. The current study highlights the value of P. ussuriensis for improved health benefits. Full article

Background: The COVID-19 pandemic exposed major deficiencies in global health governance, including fragmented authority, inequitable resource distribution, and weak compliance mechanisms. Although the WHO Pandemic Agreement (2025) addresses several of these gaps, significant operational and institutional challenges remain. This study aims to develop and empirically validate a Global Governance for Health (GGFH) Framework that strengthens leadership, financing, equity, and legal accountability across global, regional, and national levels. Methods: A three-round Delphi study was conducted. Thirty-one experts from diverse sectors, including public health, international law, economics, environment, and diplomacy, evaluated 32 structured governance statements across seven domains. Experts rated all statements using a 7-point Likert scale. Consensus was determined using a strict threshold median ≥ 6; SD ≤ 1.35; ≥75% agreement. Open-text comments were systematically reviewed through thematic analysis. All statements were systematically mapped to the WHO Pandemic Agreement articles to identify areas lacking operational clarity or enforceability. Results: All seven governance domains achieved consensus by Round 3. High agreement emerged on strengthening WHO leadership, implementing sustainable and equitable financing mechanisms, embedding LMIC representation, establishing legal preparedness and capacity-building, and integrating independent accountability tools. Correlation and interdependence analyses demonstrated that governance goals form an integrated, mutually reinforcing system, with financing, equity, and legal frameworks identified as core enablers of effective treaty implementation. Conclusions: The Delphi process validated a comprehensive and operational Global Governance for Health Framework. The GGFH complements the WHO Pandemic Agreement by addressing its unresolved governance, financing, and equity limitations and offers a structured roadmap to guide global pandemic preparedness and treaty implementation. Full article

Background: Nutrition during the perinatal period, including pregnancy, childbirth, postpartum, and lactation, is a critical determinant of maternal and neonatal health. While the importance of balanced nutrition is well established, the integration of nutritional counseling into midwifery care remains inconsistent across settings. Evidence suggests that midwives are uniquely positioned to deliver nutrition-related support, yet gaps persist in their formal training and in the availability of structured guidance. These gaps are particularly evident in certain regions, such as Greece, where dedicated national guidelines for perinatal nutrition are lacking. Methods: This systematized narrative review synthesises evidence from studies published between 2010 and 2025, retrieved through PubMed, CINAHL, Scopus, and relevant national guidelines. Although the synthesis draws on diverse study designs to provide contextual depth, randomized controlled trials (RCTs) were prioritized and synthesized separately to evaluate the effectiveness of midwife-led interventions. In total, ten randomized controlled trials were included in the evidence synthesis, alongside additional observational and qualitative studies that informed the narrative analysis. Both international and Greek literature were examined to capture current practices, challenges, and knowledge gaps in the nutritional dimension of midwifery care. Results: Findings indicate that adequate intake of macronutrients and micronutrients, including iron, folic acid, vitamin D, iodine, calcium, and omega-3 fatty acids, is essential for optimal maternal and neonatal outcomes. Despite this, studies consistently report insufficient nutritional knowledge among midwives, limited confidence in providing counseling, and variability in clinical practice. Socio-cultural factors, such as dietary traditions and migration-related challenges, further influence nutritional behaviors and access to guidance. Emerging approaches, including e-health tools, group counseling models, and continuity-of-care frameworks, show promise in enhancing midwives’ capacity to integrate nutrition into perinatal care. Conclusion: Nutrition is a cornerstone of perinatal health, and midwives are strategically placed to address it. However, gaps in training, inconsistent guidelines, and cultural barriers limit the effectiveness of current practices. Strengthening midwifery education in nutrition, developing context-specific tools, and fostering interdisciplinary collaboration are essential steps toward more comprehensive and culturally sensitive perinatal care. Future research should focus on longitudinal and intervention studies that assess the impact of midwife-led nutritional counseling on maternal and neonatal outcomes. Full article

Accurate, subject-specific estimation of cervical muscle forces is a critical prerequisite for advancing spinal biomechanics and clinical diagnostics. However, this task remains challenging due to substantial inter-individual anatomical variability and the invasiveness of direct measurement techniques. In this study, we propose a novel data-driven biomechanical framework that addresses these limitations by integrating massive-scale personalized musculoskeletal simulations with an efficient Feedforward Neural Network (FNN) model. We generated an unprecedented dataset comprising one million personalized OpenSim cervical models, systematically varying key anthropometric parameters (neck length, shoulder width, head mass) to robustly capture human morphological diversity. A random subset was selected for inverse dynamics simulations to establish a comprehensive, physics-based training dataset. Subsequently, an FNN was trained to learn a robust, nonlinear mapping from non-invasive kinematic and anthropometric inputs to the forces of 72 cervical muscles. The model’s accuracy was validated on a test set, achieving a coefficient of determination (R²) exceeding 0.95 for all 72 muscle forces. This approach effectively transforms a computationally intensive biomechanical problem into a rapid tool. Additionally, the framework incorporates a functional assessment module that evaluates motion deficits by comparing observed head trajectories against a simulated idealized motion envelope. Validation using data from a healthy subject and a patient with restricted mobility demonstrated the framework’s ability to accurately track muscle force trends and precisely identify regions of functional limitations. This methodology offers a scalable and clinically translatable solution for personalized cervical muscle evaluation, supporting targeted rehabilitation and injury risk assessment based on readily obtainable sensor data. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article

Extended Reality (XR) technologies have matured into powerful tools for training in high-stakes domains, from emergency response to search and rescue. Yet current systems often struggle to balance real-time AI-driven personalisation with the need for human oversight and calibrated trust. This article synthesizes the programmatic contributions of a multi-study doctoral project to advance a design-and-evaluation framework for trustworthy adaptive XR training. Across six studies, we explored (i) recommender-driven scenario adaptation based on multimodal performance and physiological signals, (ii) persuasive dashboards for trainers, (iii) architectures for AI-supported XR training in medical mass-casualty contexts, (iv) theoretical and practical integration of Human-in-the-Loop (HITL) supervision, (v) user trust and over-reliance in the face of misleading AI suggestions, and (vi) the role of interaction modality in shaping workload, explainability, and trust in human–robot collaboration. Together, these investigations show how adaptive policies, transparent explanation, and adjustable autonomy can be orchestrated into a single adaptation loop that maintains trainee engagement, improves learning outcomes, and preserves trainer agency. We conclude with design guidelines and a research agenda for extending trustworthy XR training into safety-critical environments. Full article

Cocoa production in West Africa supplies most of the global demand but is increasingly constrained by yield stagnation, soil degradation, disease pressure, and climate variability. This review examines how integrating regenerative agriculture (RA) with unmanned aerial systems (UAS) and artificial intelligence (AI) can support more precise and resilient cocoa management across heterogeneous smallholder landscapes. A PRISMA-guided systematic review of peer-reviewed literature published between 2000 and 2024 was conducted, yielding 49 core studies analyzed alongside supporting evidence. The synthesis evaluates regenerative agronomic outcomes, UAV-derived multispectral, thermal, and structural diagnostics, and AI-based analytical approaches for stress detection, yield estimation, and management zoning. Results indicate that regenerative practices consistently improve soil health and yield stability, while UAS data enhance spatial targeting of rehabilitation, shade management, and stress interventions. AI models further improve predictive capacity and decision relevance when aligned with data availability and institutional context, although performance varies across systems. Reported yield stabilization or improvement typically ranges from 12–30% under integrated approaches, with concurrent reductions in fertilizer and water inputs where spatial targeting is applied. The review concludes that effective scaling of RA–UAS–AI systems depends less on technical sophistication than on governance arrangements, extension integration, and cooperative service models, positioning these tools as enabling components rather than standalone solutions for sustainable cocoa intensification. Full article

This study aims to develop and apply a novel methodology to assess the scope, benefits and challenges of distributed photovoltaic generation (DG-PV). The research provides a replicable framework applicable to any country, as long as official energy consumption data are available and the nation is seeking to modify its energy matrix as part of a sustainable transition through the design of renewable-energy-based policies. To support the viability of the proposal, data from the Ecuadorian electrical system for the period between 2014 and 2024 were analyzed using technical, operational and socio-economic indicators defined in the methodology. These include renewable participation, energy diversification, DG-PV, technical efficiency, regulatory index, operational resilience and electrical coverage. The investigation concludes with the definition of a Distributed Photovoltaic Integration Index (DPII), which can be used to measure a country’s progress toward the proper implementation of renewable energy. The DPII supports informed decision-making by allowing utilities and policymakers to prioritize distributed photovoltaic integration and compare alternative energy transition scenarios. In the case of Ecuador, a DPII of 0.170 is obtained for 2024 compared to a value of 0 for 2014. This result is mainly due to an increase in renewable energy participation (P1), which rose from 0.49 to 0.76 during this period, largely supported by hydropower expansion. This value was obtained because over the last ten years, Ecuador has committed to implementing active policies that incorporate renewable energies, as well as other aspects such as technical efficiency and the expansion of electrical coverage. This approach offers a replicable quantitative tool for evaluating the integration of DG-PV, providing key information for energy planning and for the formulation of policies that promote the decarbonization, decentralization and digitalization of the national electrical system. Full article

Accurate prediction of drilling fluid rheological parameters under high-temperature and high-pressure (HTHP) conditions is critical for reliable drilling hydraulics and wellbore pressure control in deep and ultra-deep wells. However, most existing empirical and semi-empirical rheological models are developed for limited temperature–pressure ranges and specific fluid formulations, which restrict their applicability and accuracy under HTHP conditions. In this study, systematic rheological experiments were conducted on multiple drilling fluid systems over wide temperature–pressure ranges (20–200 °C and 0.1–200 MPa). Based on the experimental data, a unified predictive model for key rheological parameters was developed using a symbolic regression (SR) algorithm. The model performance was evaluated using standard statistical metrics and compared with commonly used conventional models. Compared with conventional models, the proposed model shows stronger applicability for predicting the rheological parameters of the investigated oil-based and water-based drilling fluids over a wider temperature–pressure range. It effectively overcomes the limitations of existing models under HTHP conditions (150–200 °C and 80–200 MPa) and demonstrates improved prediction accuracy and robustness for both high- and low-density drilling fluids. The overall prediction errors are generally within approximately 10%. The results indicate that the proposed unified model provides a reliable and computationally efficient tool for predicting drilling fluid rheological parameters under HTHP conditions, facilitating its integration into wellbore hydraulics, wellbore pressure, and equivalent circulating density calculations in deep and ultra-deep well applications. Full article

An increasing number of stroke survivors are burdened by persistent disabilities, requiring long-term rehabilitation. However, the extent of functional gain is highly variable, severely impairing patients’ quality of life. This variability highlights a critical gap in current prognostic tools, which rely primarily on clinical and neuroimaging data. The aim of this review is to synthesize the current literature on serum biomarkers in stroke survivors and to evaluate their prognostic value for rehabilitation outcomes. Our synthesis indicates that biomarkers reflecting distinct pathophysiological processes are emerging as key prognostic indicators. Markers of inflammation such as Tumor Necrosis Factor-alpha (TNF-α), Interleukin-6 (IL-6), and Interleukin-1 beta (IL-1β), and neuro-glial injury, including S100 Calcium-Binding Protein B (S100B), Neuron-Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), and Neurofilament Light Chain (NfL), are consistently associated with poorer functional outcomes. Conversely, markers of neuroplasticity, such as Brain-Derived Neurotrophic Factor (BDNF) and Insulin-like Growth Factor-1 (IGF-1), serve as potential indicators of recovery potential, although their predictive accuracy remains inconsistent across studies. Furthermore, emerging biomarkers of synaptic activity, such as Syntaxin-1a (STX1A) and Synaptosomal-Associated Protein, 25kDa (SNAP-25), and neuromuscular junction integrity, such as C-terminal Agrin Fragment (CAF), offer novel insights into brain–periphery communication, though their clinical utility is still under investigation. While promising, the translation of these biomarkers into clinical practice is hindered by methodological limitations, including assay heterogeneity and lack of large-scale validation. Future standardization of these molecular signatures is a critical step toward implementing precision medicine in stroke rehabilitation. Full article

This study evaluates the agri-environmental performance of the EU-27 Member States using the PROMETHEE multiple-criteria decision analysis method, based on three Eurostat indicators linked to the sustainability pillars: Harmonized Risk Indicator 1 (HRI1, social pillar), pesticide sales intensity (kg/ha UAA, environmental pillar), and environmental protection investments (% GDP, economic pillar). The analysis uses the most recent available Eurostat data (primarily from 2023) and examines three weighting scenarios: (i) equal weights, (ii) higher emphasis on the economic pillar, and (iii) higher emphasis on the environmental and social pillars. Across all scenarios, Slovenia ranked first (net flow, φ = 0.4173 to 0.4734), followed by Czechia (φ = 0.2796 to 0.3260) and France (φ = 0.1886 to 0.2240), while Malta (φ = −0.3356 to −0.3691), Cyprus (φ = −0.2916 to −0.3027), and Estonia (φ = −0.2641 to −0.2903) consistently occupied the lowest positions. The stability of rankings across alternative weighting schemes indicates robust performance patterns, with minimal shifts for most Member States. Overall, the results highlight persistent cross-country differences in agri-environmental performance despite common EU regulatory frameworks, underlining the relevance of national implementation capacity and investment strategies. The proposed PROMETHEE-based ranking provides a transparent and policy-aligned benchmarking tool that can support monitoring and prioritization of interventions related to pesticide risk reduction and environmental investment across EU Member States. Full article