Search Results (65,847)

Identifying material parameters in crystal plasticity constitutive models with high precision and high efficiency can be especially complicated due to the ever-increasing complexity of the models. These material parameters are typically calibrated through the fitting of macroscale experimental data, such as true stress–strain curves, while microscale experimental data, including phase evolution, twinning volume fraction, and so on, are rarely considered and used for verification. In the present study, a novel and computationally efficient optimization procedure for material parameters identification in a crystal plasticity constitutive model has been proposed, which couples a response surface model (RSM) and a genetic algorithm (GA). Specifically, 34 macroscopic true stress–strain data (21 for rolling direction, RD, and 13 for transverse direction, TD) and 4 microscale {10-12} extension twin (ET) volume fraction data have been utilized for multi-objective training. Furthermore, the objective function has been optimized in the present study by tailoring the weights of macroscale stress–strain data and microscale volume fractions for {10-12} ET. The proposed optimization methodology has been verified via visco-plastic self-consistent (VPSC) simulation of tensile deformation for AZ31 magnesium (Mg) alloy sheet with bimodal non-basal texture at room temperature. Results show that the fitness value of the optimization procedure would rapidly converge to a stable value of ~80 within 200 iterations. The obtained material parameters for VPSC simulation on the basis of RD-tensile and TD-tensile experimental data show good validity and applicability in aspects of mechanical response, activities of involved deformation mechanisms, evolution of volume fraction for {10-12} ET, and characteristics of texture evolution. Full article

In Articulation Morphology, inspired by the theory of anaphytes that was first proposed in 1843, ramification is the key principle in plant morphology in the open growth of plants. It engenders articulation: the formation of articles, called anaphytes. While the theory of anaphytes included tenets that are now considered outdated, Articulation Morphology—proposed here as a modern version of this theory—retains and further develops only those aspects that remain valid and fundamentally important, namely ramification and articulation. In this view, plants are articulated wholes: systems of articles formed through ramification and articulation: the formation of articles. These articles are understood dynamically as process combinations according to process morphology. For practical purposes, they may be described in traditional structural terms such as root, stem, leaf, or leaflet, but without implying a controversial and limited morphological theory such as the classical root–stem–leaf theory of mainstream morphology. Hence, articulation morphology is strictly empirical, solely relying on the observable processes of open growth, ramification and articulation. In contrast to classical mainstream morphology, which often fails to accommodate atypical or deviant structures, articulation morphology is all-inclusive: even the most deviant structures can be understood as deviant patterns of ramification and articulation. Furthermore, articulation morphology is unifying because articles constitute a fundamental morphological unit that applies to all plants from algae to bryophytes and vascular plants, whereas organ-centred classical mainstream morphology lacks such a fundamental unifying unit for all plants. Within this framework, the central concept of articulation morphology is no longer homology but transformation—the transformation of ramification and articulation. Owing to this fundamental shift and to its empirical, dynamic, all-inclusive, and unifying foundation, articulation morphology may be regarded as a new paradigm for plant morphology—an open morphology. From this perspective, plant evo-devo, especially plant morpho evo-devo, becomes the investigation of the development and evolution of ramification and articulation. Full article

The pyrometallurgical reprocessing of spent fuel developed by the United States is currently one of the most promising nuclear fuel reprocessing methods. The electroreduction, electrolytic refining, and electrodeposition processes involve electrochemical research in high-temperature molten chloride systems. In recent years, much progress has been made in simulating and studying molten-salt systems from a microscopic perspective using the first-principles molecular dynamics (FPMD) simulation technique. Using this method for simulation calculations is more conducive to analyzing the microscopic action mechanism and microscopic mechanism in the system from the atomic level and explaining the internal reasons for various electrochemical behaviors and phenomena. This opens up a new path for the study of molten-salt electrochemical systems. However, there are still a few systematic reviews of simulating work in first-principles computation. Therefore, this work summarizes the theoretical calculation work on molten-salt electrochemical systems of recent years, focusing on the research progress in computational aspects such as coordination properties, physical properties, and electrode behavior, which has good guiding value for the application of FPMD in molten-salt electrochemistry. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Five-membered monoheterocycles, either isolated or embedded in more complex systems, are ubiquitous structural motifs in nature and hence privileged targets of synthetic chemistry. Among a plethora of methodologies used for their assembly, [3+2] annulation strategies keep attracting particular interest among chemists, partly because of some significant characteristics from both the operative and the environmental viewpoints. Herein, the extensive use of conjugated nitroolefins as twofold electrophilic, two-carbon components of [3+2] MIRC (Michael-Initiated Ring Closure) annulations is reviewed as a practical and mechanistic update covering the last decade (2015–2025). Full article

Introduction: The biological effects of dietary polyphenols have gained increasing attention due to their roles in regulating oxidative stress, inflammatory processes, and mitochondrial function. Human studies suggest that polyphenol intake may support aspects of post-exercise recovery, neuromuscular function, and selected aspects of physical performance. However, most investigations have been conducted in young or metabolically healthy populations, limiting direct clinical translation to older adults. Objective: This narrative review aims to synthesize current mechanistic and human evidence on the physiological and recovery-related effects of dietary polyphenols in the context of exercise adaptation and skeletal muscle function, and to examine their potential relevance to muscle aging and sarcopenia. Methods: A structured, non-systematic literature search was conducted to integrate findings from human intervention trials, preclinical studies, and mechanistic research addressing polyphenols, exercise adaptation, muscle recovery, and muscle aging. Evidence was synthesized narratively with emphasis on shared physiological pathways and functional outcomes. Results: Human intervention studies suggest that polyphenol intake may attenuate biomarkers of exercise-induced muscle damage, modulate inflammatory responses, and accelerate recovery of muscle strength and functional performance. Mechanistic evidence supports the involvement of redox homeostasis, mitochondrial regulation, and inflammatory signaling as central mediators of these effects. While clinical data in older populations remain limited, converging evidence suggests biological overlap between recovery-related pathways and mechanisms implicated in age-related muscle decline. Conclusions: Current evidence is consistent with a biologically plausible role for polyphenols in modulating exercise-related physiological and recovery processes. By aligning recovery-focused evidence with pathways central to muscle aging, this review proposes a translational framework that may inform the design of future targeted clinical trials in older and clinical populations. Full article

Purpose: The study explores the health-related consequences of gender-based violence against women in South Africa. Accordingly, gender-based violence (GBV) has become a serious and pervasive issue in South Africa, affecting practically every aspect of life. Gender-based violence (GBV) persists as a widespread public health emergency in South Africa, disproportionately impacting women across various socio-economic and cultural contexts. This study examines the many health-related effects of gender-based violence, utilising both quantitative data from healthcare facilities and qualitative insights from survivor accounts. The results indicate a significant association between gender-based violence and a heightened prevalence of chronic medical ailments, including hypertension, reproductive health issues, and gastrointestinal diseases. The study also emphasizes a notable increase in mental health illnesses, such as sadness, anxiety, and post-traumatic stress disorder (PTSD), among survivors. The research reveals increasing patterns of intergenerational health effects, indicating that offspring of GBV survivors have increased risks of emotional and behavioural issues. These observations highlight the pressing necessity for cohesive health and social support systems, legislative change, and community-based interventions to mitigate the enduring health impact of gender-based violence on women in South Africa. Full article

Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature. Full article

This study examines the environmental risks associated with talc mining in Slovakia, focusing on various aspects. It applies a structured risk assessment methodology to evaluate the probability and severity of environmental impacts stemming from talc extraction, flotation, and tailings pond operations. Key stressors include chemical pollutants such as oils, diesel, and flotation reagents, as well as physical disruptions like georelief alteration and vegetation loss. The findings highlight high environmental risks from technical infrastructure leaks and tailings pond operations, particularly regarding groundwater contamination and landscape modification. Moderate risks were identified in diesel and oil substance leakage, while flotation processes posed minimal risk. The research underscores the need for improved risk mitigation strategies, such as enhanced monitoring and containment systems, to protect local ecosystems and water resources. The study contributes to a better understanding of the long-term environmental impacts of mineral resource exploitation and provides a foundation for more sustainable mining practices. Full article

This paper presents a comprehensive methodology for developing a numerical model of a masonry wall using the Non-Smooth Contact Dynamics (NSCD) method implemented in the open-source software LMGC90 version 2025. The modeling procedure relies on Python scripting and includes defining material properties, importing geometry from CAD tools, configuring the model, and specifying contact interactions between discrete elements. Each brick is modeled as an individual rigid element, allowing realistic simulation of frictional and cohesive behavior at joints. It outlines key theoretical aspects of the NSCD framework, including the formulation of global and local variables, interaction laws, and numerical integration. Numerical examples demonstrate the discrete element approach’s ability to capture complex in-plane and out-of-plane structural phenomena induced by seismic loading and differential foundation settlement. The results highlight the advantages of discrete modeling in representing discontinuities and failure processes that are difficult to simulate with a conventional continuum-based approach. Full article

Background/Objectives: Coping with death is an essential yet challenging aspect of nursing. In Ecuador, limited training and cultural factors may influence how nurses face the process of death and dying. This study aimed to explore nurses’ perspectives and highlight the degree of congruence between the numerical and discursive data provided by participants. Methods: A sequential explanatory mixed-methods design (QUAN → qual) using questionnaires and qualitative interviews was employed. The quantitative phase included 497 nurses who completed the Bugen Coping with Death Scale and the qualitative phase involved semi-structured interviews with 18 nurses. Quantitative data were analysed descriptively, while qualitative data underwent thematic analysis. Integration occurred at the methodological level—through the building of the qualitative data collection instrument—and at the levels of analysis and interpretation. Results: Nurses demonstrated moderate coping levels on the Bugen Coping with Death Scale. Although many reported being comfortable discussing death, qualitative data revealed substantial emotional distress and limited preparedness—particularly when facing their own mortality or the death of loved ones. Nurses expressed fear of suffering, sadness, and helplessness, especially when caring for dying children or young mothers. Communication with patients and families at the end of life emerged as a major challenge. Spirituality was identified as a key coping resource. Conclusions: Coping with death remains a complex and emotionally demanding process for nurses in Ecuador. Continuous education, emotional support, and training in spiritual and psychological dimensions of care are essential to strengthen nurses’ resilience and enhance the quality of care. Full article

This article addresses issues related to power supply reliability for electronic security systems (ESSs) during their operational lifetime. ESS are deployed both in enclosed building structures, where environmental conditions are stabilised, and across large open areas exposed to natural environmental conditions, such as transport depots, airports, railway stations, ports, and other similar facilities. Laboratory tests on selected power supply units used in ESSs have been conducted by the authors, as well as a theoretical analysis of the reliability of the power supply process. The reliability analysis of the power supply took into account the reliability of delivering electrical energy with specified parameters to all components forming a system aimed at ensuring the safety of electronic security systems (ESSs). Power supply is essential for the correct operation of all modules, components, devices, and alarm control panels (ACPs) within ESSs. In addition to meeting the basic requirements for the provision of electrical power, the system designer must also give particular consideration to power supply reliability, especially in facilities classified as part of the state critical infrastructure (CI). This issue is particularly significant in the case of Fire Detection and Alarm Systems (FASs), which constitute the most critical safety systems responsible for protecting human life and health. Accordingly, this article discusses selected aspects of power supply for representative electronic security systems (ESSs). The subsequent part of this paper presents operational tests of selected ESS power supply units. A further topic addressed in the article is the definition of models of the operational process of power supply systems and the execution of computer simulations. The analysis of the operational process of ESS power supply units, expressed as models and graphs and supported by computer simulations, enabled the formulation of conclusions regarding reliability. The conclusions drawn from this article may be applied in the design, routine maintenance, and operation of ESSs. Full article

This paper builds on research from a project aimed at promoting the circular economy through processes based on low-impact materials derived from natural fibers. A methodology is developed to assess the economic, environmental, and social sustainability of alternative production scenarios, thereby supporting the ranking of options. Assuming the principles of Life Cycle Thinking and circular economy and the operational aspects of Life Cycle Costing (LCC), Life Cycle Assessment (LCA), Social Life Cycle Assessment (S-LCA) approaches normed by international standards, an integrated approach is proposed based on the construction of a joint Global Cost indicator. Attention is paid to harmonizing impacts assessed in their own units of measurement to arrive at a monetary indicator for summarizing and simplifying the prioritization of alternatives. As a result, the integrated Global Cost calculation methodology is presented to internalize social and environmental impacts, as well as economic ones, and to evaluate the sustainability of materials derived from primary and waste natural fibers. Full article

Introduction: In recent decades, demographic aging has led to an inversion of the population pyramid, with a marked increase in the proportion of older adults. This shift has been accompanied by a higher prevalence of chronic and life-limiting diseases, while there have also been significant technical and scientific advances. However, these developments have not been matched by a proportional expansion of healthcare human resources, including in palliative care (PC). Consequently, healthcare systems face increasing pressure, particularly in the provision of end-of-life care. Artificial intelligence (AI) has emerged as a promising tool to support and improve healthcare delivery. Objective: This study aims to review the literature on the impact of AI on palliative care, with particular emphasis on its clinical applications and ethical implications in end-of-life care. Methods: A narrative review was conducted using a structured search of PUBMED, CINAHL and Web of Science databases, covering publications from the last ten years (2015–2025). Search terms included combinations of “artificial intelligence”, “machine learning”, “palliative care”, “end-of-life care”, and “ethics”. Articles were included if they addressed clinical applications, implementation challenges or ethical aspects of AI in PC. Reference lists of selected articles were screened to identify additional relevant studies. The findings were analyzed and synthesized thematically into key domains of application and ethical concern. Results: The literature suggests that AI is currently a promising tool in PC, particularly in prognostication, symptom assessment, clinical decision support, and communication. These applications may represent a paradigm shift compared to conventional approaches. However, it is important not to forget that patients in PC need much more than algorithmic decision trees. Thus, current evidence is largely exploratory, with limited real-world validation. Empathetic emotional support, physical comfort and compassion are things that artificial intelligence cannot provide. AI does not replace humans in interpersonal relationships and dignity; it only complements them. Conclusions: AI-based technologies hold significant potential to address contemporary challenges in PC, including inequitable access, workforce strain, and the need for more efficient service delivery. Nevertheless, their implementation raises substantial ethical concerns related to autonomy, transparency, data governance, and the preservation of human dignity. AI should therefore be understood as a complementary tool that supports—but does not replace—the human dimension of PC. Full article