Search Results (323)

This study examines the growth dynamics of Nassella tenuis (Phil.) Barkworth, a palatable perennial tussock grass, abundant in the natural grasslands of Central Argentina. It focuses on the effects of water regimes and grazing history. Plants were collected from sub-humid and semiarid grasslands with contrasting grazing histories (grazed and ungrazed) and cultivated under controlled conditions. Key growth traits, such as leaf elongation, senescence, and net growth rates, as well as tiller production, were assessed across the growth cycle. The results reveal that sub-humid grasslands favor faster growth rates and higher tiller production, while semiarid grasslands exhibit lower growth rates, potentially reflecting adaptive strategies for water-limited environments. Seasonal analysis revealed distinct life cycle patterns: plants from sub-humid grasslands exhibited higher elongation rates during autumn and spring, whereas growth in semiarid plants remained consistently low across seasons. Grazing history significantly influenced growth patterns, with grazed plants showing reduced tiller numbers and growth rates but lower senescence rates, particularly in semiarid grasslands. These findings underscore the importance of aligning grazing management practices with the growth dynamics of N. tenuis and the water regime of the site to optimize forage production while maintaining grassland resilience. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

The rubber industry is facing increasing pressure to adopt sustainable practices due to environmental concerns associated with the use of non-renewable resources and the growing accumulation of waste tyres and agricultural byproducts. This study explores the potential of partially replacing conventional carbon black (CB) with sustainable alternatives derived from agricultural waste (wine by-products) and pyrolysed waste tyres in natural rubber/styrene-butadiene rubber (NR/SBR) composites for tyre applications. A series of NR/SBR composites were formulated with varying ratios of CB to agricultural waste and pyrolysed tyre waste, while maintaining consistent levels of other additives. The resulting composites were then subjected to a comprehensive suite of analyses, including scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) surface area measurements, Fourier transform infrared spectroscopy (FTIR), bound rubber content determination, Payne effect analysis, thermogravimetric analysis (TGA), dynamic mechanical thermal analysis (DMTA), and mechanical property testing. Furthermore, a Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) analysis were conducted to evaluate the environmental and economic viability of the proposed CB replacements. The results reveal that the incorporation of agricultural waste and pyrolysed tyre waste can significantly impact the curing behaviour, mechanical properties, and thermal stability of rubber composites. Importantly, some of the formulations demonstrate comparable tensile strength, elongation at break, and hardness compared to traditional CB-filled composites. The LCA and LCC analyses further highlight the potential for substantial reductions in greenhouse gas emissions, fossil resource depletion, and overall production costs, thereby supporting the transition toward more sustainable tyre manufacturing practices. Full article

Hybrid offshore wind-wave systems play an important role in renewable energy transition. To maximize energy utilization efficiency, a comprehensive assessment to select optimal locations is urgently needed. The hydraulic power characteristics of these systems cause power fluctuations that reduce grid frequency stability. Thus, a site suitability assessment and a grid-forming battery energy storage system (BESS) configuration method are proposed. Considering energy efficiency, dynamic complementary characteristics, and output stability, a framework integrating three indices of Composite Energy Output Index (CEOI), Time-Shifted Cross-Covariance Index (TS-CCI), and Energy Penetration Balance Index (EPBI) is constructed to assess site suitability. To ensure secure and stable operation of microgrid, the frequency response characteristics of the hybrid system are analyzed, and the corresponding frequency constraint is given. A BESS configuration method considering frequency constraint is developed to minimize life cycle costs and maintain grid stability. Applied to a case study along China’s southeast coast, the assessment method successfully identified the optimal offshore station, confirming its practical applicability. The BESS configuration method is validated on a modified IEEE 30-bus system, with a 6.35% decrease in life cycle cost and complete renewable utilization. This research provides a technical and cost-effective solution for integrating hybrid wind-wave energy into island microgrids. Full article

Background/Objectives: This study conducted a secondary and integrative analysis of qualitative data on adolescents and young adults (AYAs) with biliary atresia who survive with their native livers. These individuals struggle with independence and self-care due to prolonged parental involvement. Prior studies have insufficiently clarified how AYAs and parents jointly navigate daily responsibility transitions during this period. Therefore, we aimed to elucidate this process and develop a practical model to support nursing care. Methods: Semi-structured interview data from eight adolescent–parent dyads (one male and seven females, aged 17–25; one father and seven mothers, aged 40–60) were reanalyzed using the modified grounded theory approach. By reframing the analytical focus on dyadic interactions, four transition phases were identified, which were then integrated with the findings of two prior studies to construct an integrative process model. Results: The transition comprised four phases: (1) parent-led recuperation, (2) a vicious cycle of control and rebellion, (3) passing the axis of responsibility, and (4) aligning the parent–child rhythm to create a patient-centered life. The transition processes were shaped by changes in cognition and behavior. The model illustrates mutual adaptation through communication, negotiation, and reflection, identifying opportunities for nursing intervention. Conclusions: This process model offers a practical framework for nurses to assess readiness for care transitions, support transitional role shifts, and co-develop care strategies. The model provides insights into relationship-based communication and shared decision-making in transitional care by capturing the relational dynamics between AYAs and their parents. Full article

The Chinese sturgeon (Acipenser sinensis), a critically endangered anadromous fish species, spends over 90% of its life cycle in marine habitats, yet research on its marine ecology and habitat requirements is limited due to sparse data. To address this, we integrated satellite remote sensing with ecological modeling to assess spatiotemporal dynamics in marine habitat suitability across China’s continental shelf (2003–2020). Nine key habitat factors were derived from multi-source remote sensing data and inverted transparency algorithms. Species occurrence data were coupled with the Maximum Entropy (MaxEnt) model to evaluate habitat preferences and seasonal shifts. Results revealed distinct environmental preferences: shallow depths (≤20 m), sea surface and bottom temperature (10–30 °C and 10–25 °C), salinity (10–35‰), transparency (0.40–3.00 m), eastward and northward seawater velocity (−0.20–0.15 m/s and −0.20–0.20 m/s), moderate productivity (1000–3000 mg/m²), and zooplankton carbon (0.20–6.00 g/m²). Habitat factor importance varied seasonally—salinity, depth, and net primary productivity dominated in spring; bottom temperature and productivity in summer/autumn; salinity and transparency in winter. Spatially, high-suitability areas peaked in autumn (70% total suitable habitat), concentrating near the Yangtze Estuary, northern Jiangsu coast, and Zhoushan Archipelago. This study emphasizes the need to prioritize these areas for protection and inform proliferation and release schemes for Chinese sturgeon. It also demonstrates the efficacy of remote sensing for mapping essential habitats of migratory megafauna in complex coastal ecosystems and provides actionable insights for targeted conservation strategies. Full article

This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities and the dynamic evaluation of portfolio performance. The model explicitly accounts for right censoring and demonstrates strong predictive accuracy. Furthermore, by incorporating additional information about the portfolio’s loss process, we show how to empirically estimate key risk measures—such as Value at Risk (VaR) and Expected Shortfall (ES)—that are sensitive to the age of the loans. Through simulations, we illustrate how loss distributions and the corresponding risk measures evolve over the loans’ life cycles. Our approach emphasizes the significant dependence of risk metrics on loan age, illustrating that risk profiles are inherently dynamic rather than static. Using a real-world dataset of 10,479 loans issued by Angolan commercial banks, combined with assumptions regarding loss processes, we demonstrate the practical applicability of the proposed methodology. This approach is particularly relevant for emerging markets with limited access to advanced credit risk modeling infrastructure. Full article

Background/Objectives: Inteins are mobile genetic elements invading highly conserved genes across all domains of life and viruses. Five active intein insertion sites (MCM-a through e) had previously been identified and studied in the archaeal replicative helicase minichromosome maintenance (MCM) subunit gene mcm, making MCM an ideal system for dissecting the dynamics of multi-intein genes. However, work in this system thus far has been limited to particular archaeal groups. To better understand the dynamics and diversity of these inteins, MCM homologs spanning all archaeal groups were extracted from NCBI’s non-redundant protein sequence database, and the distribution and structural architectures of their inteins were characterized. Methods: The amino acid sequences of 4243 archaeal MCM homologs were retrieved from NCBI’s non-redundant protein sequence database. These sequences were systematically assessed for their intein content through within-group multiple sequence alignments. To characterize the inteins present at each site, extensive intein structure predictions and comparisons were performed. Phylogenetic analyses were used to investigate intein relatedness between and within sites, as well as the distribution of different MCM inteins in geographically overlapping populations of archaea. Results: In total, 11 active MCM intein insertion sites were identified, expanding on the previously known five. The insertion sites have varied invasion activity levels across archaeal groups, with Nanobdellati (DPANN) being the only group with all 11 sites active. In all but two (Methanonatronarchaeia and Hadarchaeota) of the archaeal groups studied where inteins were present, at least one MCM homolog was invaded by more than one intein. With respect to intein structure, within-intein insertions bearing semblance to DNA-binding domains were identified, with varied presence between inteins. Additionally, a study of archaeal MCM sequences of samples collected from the Atacama Desert in June 2013 revealed high MCM intein diversity levels. Conclusions: We identified six new active intein insertion sites in archaeal MCM, more than doubling the five previously known sites. All eleven intein insertion sites were either close to the ATP binding site, or the lined the channel through which the single-stranded DNA is pulled during the catalytic cycle of the helicase. Many of the analyzed inteins contained insertions bearing similarity to DNA-binding helix-turn-helix domains suggesting potential involvement in the intein homing process. Additionally, the high levels of MCM intein diversity observed in archaea from the Atacama Desert provide novel and strong support for a co-existence model of intein persistence. Full article

The sustainability of bridge infrastructure is becoming increasingly important due to rising environmental, economic, and social demands. However, most current assessment models remain fragmented, often overlooking the social pillar, underutilizing risk integration across the lifecycle, and failing to fully leverage digital tools such as Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA), resulting in incomplete sustainability evaluations. This study addresses these limitations by introducing a practical and adaptable model that integrates BIM, LCSA, and expert-driven risk prioritization. Five Hungarian bridge projects were modeled using Tekla Structures and analyzed in OpenLCA to quantify environmental, economic, and social performance. A custom Sustainability Level Change (SLC) algorithm was developed to compare baseline scenarios (equal weighting) with risk-informed alternatives, simulating the impact of targeted improvements. The results demonstrated that prioritizing high-risk sustainability indicators leads to measurable lifecycle gains, typically achieving SLC improvements between +2% and +6%. In critical cases, targeted enhancement scenarios, applying 5% and 10% improvements to top-ranked, high-risk indicators, pushed gains up to +12%. Even underperforming bridges exhibited performance enhancements when targeted actions were applied. The proposed framework is robust, standards-aligned, and methodologically adaptable to various bridge types and lifecycle phases through its data-driven architecture. It empowers infrastructure stakeholders to make more informed, risk-aware, and data-driven sustainability decisions, advancing best practices in bridge planning and evaluation. Compared to earlier tools that overlook risk dynamics and offer limited lifecycle coverage, this framework provides a more comprehensive, actionable, and multi-dimensional approach. Full article

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life. Full article

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element simulation, and experimental validation, we establish a bidirectional coupling framework analyzing dynamic contact mechanics and wear evolution. By developing phased contact state identification criteria and geometric constraints, a transient load calculation model is established, revealing dynamic load characteristics with peak contact forces reaching 206.34 N. A dynamic contact stress integration algorithm is proposed by combining Archard’s theory with ABAQUS finite element simulation and ALE adaptive meshing technology, enabling real-time iterative updates of wear morphology and contact stress. This approach constructs an exponential model correlating cumulative wear depth with docking cycles (R² = 0.997). Prototype experiments demonstrate a mean absolute percentage error (MAPE) of 14.6% between simulated and measured wear depths, confirming model validity. With a critical wear threshold of 0.8 mm, the predicted service life reaches 45,270 cycles, meeting 50-year operational requirements (safety margin: 50.9%). This research provides theoretical frameworks and engineering guidelines for wear-resistant design, material selection, and life evaluation in high-reliability automatic docking systems. Full article

Modern manufacturing strives for peak efficiency while facing pressing demands for environmental sustainability. Balancing these often-conflicting objectives represents a fundamental trade-off in modern manufacturing, as traditional methods typically address them in isolation, leading to suboptimal outcomes. Process mining offers operational insights but often lacks dynamic environmental indicators, while standard Life Cycle Assessment (LCA) provides environmental evaluation but uses static data unsuitable for real-time optimization. Frameworks integrating real-time data for dynamic multi-objective optimization are scarce. This study proposes a comprehensive, data-driven, cloud-based framework that overcomes these limitations. It uniquely combines three key components: (1) real-time Process Mining for actual workflows and operational KPIs; (2) dynamic LCA using live sensor data for instance-level environmental impacts (energy, emissions, waste) and (3) Multi-Objective Optimization (NSGA-II) to identify Pareto-optimal solutions balancing efficiency and sustainability. TOPSIS assists decision-making by ranking these solutions. Validated using extensive real-world data from a tube manufacturing facility processing over 390,000 events, the framework demonstrated significant, quantifiable improvements. The optimization yielded a Pareto front of solutions that surpassed baseline performance (87% efficiency; 2007.5 kg CO₂/day). The optimal balanced solution identified by TOPSIS simultaneously increased operational efficiency by 5.1% and reduced carbon emissions by 12.4%. Further analysis quantified the efficiency-sustainability trade-offs and confirmed the framework’s adaptability to varying strategic priorities through sensitivity analysis. This research offers a validated framework for industrial applications that enables manufacturers to improve both operational efficiency and environmental sustainability in a unified manner, moving beyond the limitations of disconnected tools. The validated integrated framework provides a powerful, data-driven tool, recommended as a valuable approach for industrial applications seeking continuous improvement in both economic and environmental performance dimensions. Full article