Search Results (8,361)

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

Jet machining is widely utilized in innovative technology industries, such as aerospace and semiconductors, due to its minimal thermal damage. However, with the increasingly stringent surface quality requirements of modern manufacturing, conventional jet technologies face limitations in achieving ultra-precision surface finishing and high material removal rates. To address these challenges and adapt to this new situation, multi-energy field-assisted jet machining has emerged as a novel concept, integrating laser, ultrasonic, and magnetic fields. This paper reviews the scientific development and recent advancements of these hybrid technologies within the field of ultra-precision machining. The physical interaction mechanisms between the auxiliary energy fields and the waterjet are elucidated. Specifically, the effects of laser thermal softening, ultrasonic cavitation, and magnetic focusing on new mechanisms of material removal and surface topography are systematically analyzed. The process capabilities and applications of each method are evaluated. Finally, current technical challenges are identified, and the future trends in ultra-precision jet machining are discussed. Full article

Throughout the long history of human development, a large number of activity relics have been left on the earth, among which settlements are important carriers for studying human construction activities. In the era without modern active environmental control technology, humans used their experience to create the miracle of harmonious coexistence between humans and nature. Especially in the construction of settlements under complex environmental stress, it is the crystallization of human wisdom. For China, the settlements of ethnic minorities, due to their unique culture and harsh living environment, are undoubtedly key objects for studying the wisdom of human settlement construction. Therefore, this study takes the Tujia rural settlements in the mountainous environment of northeastern Sichuan as the research object and uses the spatial analysis function of ArcGIS to construct a complete “culture-space” environmental adaptation wisdom research system. The research results show that there is a close relationship between the cultural wisdom and spatial construction wisdom of the Tujia people in northeastern Sichuan. Cultural wisdom plays a key role in guiding settlements to adapt to terrain, water resources, and climate, etc., thus presenting a highly coordinated mechanism between the overall distribution of Tujia rural settlements in northeastern Sichuan and the construction of settlement space and the environment. The “culture-space” environmental adaptation research framework proposed in this study can provide a reference for the study of rural settlement space worldwide, and the clear settlement environmental adaptation strategies in the research can provide guidance for the construction of modern mountainous town spaces. Full article

This study presents a comprehensive 35-year (1990–2025) shoreline change assessment along the southeast coast of Ireland, integrating multi-decadal Landsat satellite archives with GIS-based Digital Shoreline Analysis System (DSAS) metrics to quantify both spatial and temporal coastal dynamics. Unlike previous studies that focus on shorter timeframes or localized sectors, this research provides a regional-scale, orientation-specific comparison between the eastern-facing (SE1; County Wexford) and southern-facing (SE2; County Waterford) shorelines. Shoreline evolution was quantified using four complementary DSAS indicators—Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), allowing robust discrimination between short-term variability and multi-decadal trends. The results reveal noticeable spatial variability in shoreline behavior with 57% accretion and 42% erosion across the eastern-facing coast (SE1) in County Wexford and the southern-facing coast (SE2) in County Waterford. SCE values ranging from 2.26 m to 663.83 m indicate considerable short-term shoreline variability, particularly within dynamic barrier and embayed systems. NSM values between −216.65 m and +663.83 m indicate erosional hotspots, particularly along soft-sediment coasts and exposed southern-facing sectors, whereas accretion is limited to embayments, sandy beaches, and zones of effective sediment trapping. Rate-based analyses show EPR values between −14.82 and +20.38 m/yr and LRR values between −5.27 and +20 m/yr, with LRR providing more reliable estimates of multi-decadal trends in highly dynamic environments. The findings highlight the strong influence of coastal orientation, sediment availability, geological controls, and human activities on shoreline change in southeastern Ireland. These findings provide valuable evidence to support coastal management, hazard mitigation, and climate adaptation planning, with the assistance of policymakers, to develop effective strategies that enhance the resilience and quality of life of coastal communities. Full article

In recent years, optimization algorithms have emerged as powerful computational tools for addressing complex and dynamic challenges across diverse domains. These domains include engineering, technology, management, and decision-making. Their growing importance is motivated by (a) the increasing complexity of modern systems, (b) the need for efficient resource utilization, and (c) the demand for scalable algorithmic solutions. These algorithms enable the systematic and computational exploration of large solution spaces, supporting decision-making and design under uncertainty, large-scale data, and evolving requirements. This study provides a structured review and comparative scientometric analysis of optimization algorithms, covering: (a) exact methods, (b) approximation techniques, (c) metaheuristics, and (d) emerging physics-informed frameworks. The analysis highlights algorithmic trends, performance-oriented research directions, and the increasing integration of mathematical programming, machine learning, and numerical methods. The results show a renewed focus on classical algorithmic paradigms. Moreover, rapid growth in hybrid and physics-informed optimization approaches is observed. These findings confirm the central role of optimization algorithms in modern algorithm engineering and interdisciplinary computational research. Full article

While return migrant workers (RMWs) are increasingly viewed as key to rural development, their specific impact on grain production remains ambiguous. Clarifying this role is critical to manage the dual nature of their reintegration—leveraging valuable resources and knowledge while addressing complex reintegration challenges—to ensure national food security and advance agricultural modernization. Drawing on data from the 2018 China Labor-force Dynamics Survey (CLDS), this study explicitly tests the hypothesis that migration experience significantly reduces the likelihood that RMW households engage in grain production. The empirical results from probit models support this hypothesis, and this finding is robust across multiple specifications. Further analysis shows that migration experience significantly reduces land cultivation scales—especially among larger producers—and increases land abandonment. Additionally, it inhibits technology adoption or invest in agricultural technology. These results suggest that migration experience may weaken, rather than enhance, RMWs’ commitment to grain production, challenging the policy expectation that they can lead agricultural transformation. The study calls for more nuanced policy interventions that account for the structural constraints facing RMW households and their limited contribution to large-scale, efficient grain farming. Full article

Ethnopharmacological relevance. Withania somnifera (L.) Dunal, widely used in traditional medical systems such as Ayurveda, Unani, and Middle Eastern folk medicine, is valued for its adaptogenic, anti-inflammatory, neuroprotective, antimicrobial, and anticancer properties. These activities are primarily attributed to withanolides, with Withaferin A recognized as one of the most bioactive constituents. Although traditional preparations often rely on the root, leaf use provides a more sustainable alternative and may yield significant quantities of active metabolites. Identifying efficient, modern extraction technologies that can enhance the recovery of bioactive compounds from leaves is essential for developing effective, standardized ethnopharmacological formulations. Materials and methods. Plants of W. somnifera grown from seeds were subjected to different environmental conditions (control, drought, cold, yeast extract treatment). Leaves were extracted using Pressurized Cyclic Solid–Liquid Extraction (PCSLE) with hydroalcoholic solvents and compared with conventional infusion of dried leaves. Extracts were fractionated with solvents of varying polarity and analyzed by TLC, HPLC, and NMR for quantification of Withaferin A. Expression levels of key withanolide-biosynthetic genes (CAS, SMT1, DWARF1, CYP71, CYP76) were assessed using qRT-PCR. Antimicrobial activity of pure Withaferin A, aqueous extract, and hydroalcoholic PCSLE extract was evaluated through MIC and MBC assays against Gram-positive and Gram-negative strains. Cytotoxic activity was measured via MTT assays in six human cancer cell lines after 3, 6, and 24 h of treatment. Results. PCSLE yielded substantially higher levels of Withaferin A than traditional infusion, especially in medium-polarity fractions (chloroform and ethyl acetate), with concentrations reaching 0.70% in fresh leaf mass (4.8% dry weight), compared to 0.11% obtained by infusion. Gene expression analysis revealed that 24-week-old plants exhibited the highest transcription of withanolide-biosynthetic genes, and drought stress significantly upregulated CAS, SMT1, DWARF1, CYP71, and CYP716, indicating enhanced metabolic flux toward withanolide production. Hydroalcoholic PCSLE extracts showed broad-spectrum antimicrobial activity, with MIC and MBC values comparable to pure Withaferin A and demonstrating bactericidal effects against Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, and Listeria monocytogenes. The aqueous extract showed activity only against Gram-positive strains. Cytotoxicity assays demonstrated an optimistic, dose-dependent reduction in cell viability across all tumour cell lines treated with the hydroalcoholic PCSLE extract, closely mirroring the activity of pure Withaferin A and consistently exceeding the effect of the aqueous extract. IC50 values confirmed the high bioactive content of PCSLE extracts and suggested mechanisms like those known for Withaferin A. Conclusions. PCSLE proved to be a highly efficient extraction technology for obtaining leaf extracts rich in Withaferin A, outperforming conventional extraction methods while exploiting sustainable plant tissue. Developmental stage and drought stress significantly modulated the expression of genes involved in withanolide biosynthesis, highlighting agronomic strategies capable of enhancing metabolite production. Hydroalcoholic PCSLE extracts exhibited antimicrobial and cytotoxic activities comparable to pure Withaferin A, supporting their relevance as promising therapeutic candidates. These findings advocate for the use of W. somnifera leaves as a sustainable source of bioactive compounds and demonstrate that advanced extraction technologies can contribute to the development of innovative ethnopharmacological preparations for antimicrobial and anticancer applications. Full article

Light-based diagnostic and therapeutic approaches are increasingly important in modern biomedicine, with organic dyes emerging as versatile optical agents due to their tunable photophysical properties. Precise control over absorption and emission characteristics has enabled their application in fluorescence, photoacoustic, and Raman imaging, as well as in photodynamic and photothermal therapies. However, challenges related to biocompatibility, aqueous stability, and in vivo performance remain critical for clinical translation. Organic dyes that absorb in the near-infrared region are particularly attractive because of their deeper tissue penetration and reduced background interference. This review highlights key structure property relationships of organic dyes and summarizes current design strategies, including chromophore modification, peripheral functionalization for water solubility, and self-assembled nanotheranostic systems. Recent biomedical applications in cancer diagnosis and therapy, bacterial detection, and imaging-guided treatment are discussed, along with future directions for advancing dye-based technologies in healthcare. Full article

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology. Full article

Energy storage plays a key role in the energy transition by enabling the effective integration of variable renewable energy sources such as solar and wind power and by supporting the stability and flexibility of modern energy systems. The rapid development of energy storage technologies has become one of the pillars of sustainable energy management; however, it simultaneously raises environmental, material, and systemic challenges. This review analyses the environmental implications of energy storage development using an integrative perspective that combines technological, environmental, and system-level analysis. The paper examines major classes of energy storage technologies, including electrochemical, mechanical and physical, thermal energy storage, and chemical pathways within Power-to-X, with particular emphasis on their technical characteristics, maturity, and life cycle environmental performance. Lithium-ion battery systems typically achieve round-trip efficiencies of 85–92% and cycle lifetimes exceeding 5000 cycles, while flow batteries may exceed 10,000 cycles under stationary operating conditions. Mechanical storage technologies such as pumped hydro provide efficiencies of approximately 70–85% with operational lifetimes exceeding several decades. Key challenges related to critical raw material availability, recycling, end-of-life management, and ecosystem impacts are discussed, highlighting the importance of sustainable production and recovery strategies in supporting the circular economy. In addition, the review addresses the consequences of insufficient reuse of secondary materials and the growing relevance of digitisation and cyber resilience of energy storage systems as indirect contributors to environmental risk. The review also considers geopolitical aspects related to critical material supply chains and the cyber security of energy storage infrastructure, emphasising their growing importance for the resilience and environmental sustainability of future energy systems. The analysis indicates that further development of energy storage technologies will significantly influence not only power systems but also transport, industry, and heat sectors. The results emphasise that sustainable deployment of energy storage requires hybrid system architectures and policy frameworks that account for environmental performance, system flexibility, and long-term resilience in line with the principles of sustainable development. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Power system blackouts remain a major concern for modern electricity networks, as they often result from cascading failures that lead to substantial load shedding and widespread service disruptions. This paper presents a dynamic resilience assessment of hybrid AC/DC power systems and investigates the effectiveness of voltage-source-converter-based high-voltage direct current (VSC-HVDC) technology in enhancing system resilience under outage contingencies. The study contributes by integrating protection devices and their settings into the analysis and by providing a quantitative evaluation of the system response to N-2 and N-3 contingencies using PSS^®E simulations. The demand not served index is used as a measure of resilience, and its cumulative distribution functions are computed to compare the performance of AC and DC interconnections. The results underscore the importance of VSC-HVDC links in mitigating cascading failures, highlighting their potential as a resilience-enhancing component in modern power grids. Full article

Enhancing urban ecological resilience (UER) is crucial for mitigating soil erosion, improving land use efficiency, and preventing ecological degradation. The digital–real economy integration (DRI) plays a pivotal role in strengthening UER, offering a vital pathway for modernizing ecological governance systems and capabilities in the Yellow River Basin (YRB). Based on ecological resilience theory, this study establishes a three-dimensional evaluation framework centered on “resistance–recovery–adaptation”. Using panel data from 78 cities in the YRB from 2011 to 2023, we empirically examine the impact of DRI on UER. The results indicate that DRI significantly improves UER in the YRB, with notably strong positive effects on recovery and adaptation capacities, although there is no significant effect on resistance capacity. Mechanism analysis reveals that DRI promotes UER primarily through three channels: upgrading the industrial structure, strengthening government governance, and spurring green technological innovation. Heterogeneity analysis further shows that the positive impact of DRI on UER is more pronounced in downstream cities, urban agglomerations, non-resource-based cities, key environmental protection cities, green data center pilot cities, and informatization–industrialization integration pilot cities. Spatial analysis confirms DRI generating positive spatial spillover effects on the UER of neighboring cities. This study provides a theoretical basis for understanding the ecological governance potential of DRI and offers policy insights to support coordinated digital and green transformation in the YRB. Full article

The establishment of AI ethics bylaws in academia is needed for teaching, learning, and assessment. The adaptive parameters of these bylaws define the ethical, pedagogical, and operational standards for the use of artificial intelligence tools within academia. The main aim is to ensure that AI tools are used to enhance educational practices while preserving human judgment, safeguarding academic integrity, and promoting critical thinking. Specifically, these are intended to mentor all domains of academia to uphold the core values of fairness and transparency while adapting to the advent of modern technologies. While many are enthused by the support provided by large language models, it is also important to prevent over-reliance or misuse of AI technologies. This establishes clear responsibility for faculty, students, and administration. These significant bylaws pay more attention to these issues to provide a foundation for good governance, evaluation, and amendment of AI-related practices. To provide normative insight into the anticipated reception of these bylaws, we conducted a small exploratory pilot study with STEM faculty. The resulting observations offer preliminary indications of the feasibility of the proposed method for future research and policy development. Full article