Search Results (1,822)

As global copper demand continues to grow, China, being the largest copper consumer, faces increasingly complex challenges in ensuring the security of its supply chain. However, a substantive gap remains: prevailing assessments rely on static index systems and discrete scenario analyses that seldom model uncertainty-driven, continuous-time strategic interactions, leaving the conditions for self-enforcing cooperation and the attendant policy trade-offs insufficiently identified. This study models the interaction between Chinese copper importers and foreign suppliers as a continuous-time stochastic differential game, with feedback Nash equilibria derived from a Hamilton–Jacobi–Bellman system. The supply security utility is specified as a diffusion process perturbed by Brownian shocks, while regulatory intensity and profit-sharing are treated as structural parameters shaping its drift and volatility—thereby delineating the parameter region for self-enforcing cooperation and clarifying how sudden disturbances reconfigure equilibrium security. The research findings reveal the following: (i) the mean and variance of supply security utility progressively strengthen over time under the influence of both parties’ maintenance efforts, while stochastic disturbances causing actual fluctuations remain controllable within the contract period; (ii) spontaneous cooperation can be achieved under scenarios featuring strong regulation of domestic importers, weak regulation of foreign suppliers, and a profit distribution ratio slightly favoring foreign suppliers, thereby reducing regulatory costs; this asymmetry is beneficial because stricter oversight of domestic importers curbs the primary deviation risk, lighter oversight of foreign suppliers avoids cross-border enforcement frictions, and a modest supplier-favored profit-sharing ratio sustains participation—together expanding the self-enforcing cooperation set; (iii) sudden events exert only short-term impacts on supply security with controllable long-term effects; however, an excessively stringent regulatory environment can paradoxically reduce long-term supply security. Security effort levels demonstrate positive correlation with supply security, while regulatory intensity must be maintained within a moderate range to balance incentives and constraints. Full article

Introduction: Oncological diseases are one of the leading causes of mortality worldwide, with the high cost of therapies representing a critical barrier for health systems. Generic drugs have emerged as an alternative to reduce costs and improve access; however, their quality, safety, and efficacy remain a subject of regulatory and clinical debate. This issue is particularly sensitive in oncology, where generics often involve cytotoxic agents, narrow therapeutic indices, and complex formulations, all of which amplify the risks of therapeutic interchangeability. Materials and Methods: A multidisciplinary team composed of 19 experts in oncology, hepatology, gastroenterology, toxicology, endocrinology, and pharmacology was convened based on established academic contributions, clinical expertise, and participation in regulatory or guideline development. Evidence was synthesized through a non-systematic narrative review of PubMed, Embase, and regional databases. Consensus recommendations were developed using a two-round Delphi process, with agreement defined as ≥75%. Results: The Delphi panel produced six key recommendations: (1) stricter requirements for bioequivalence and bioavailability, tailored to oncology; (2) strengthened pharmacovigilance and real-world monitoring; (3) standardized protocols for therapeutic interchangeability, particularly for narrow therapeutic index agents; (4) active physician involvement in formulary decision-making; (5) harmonized regional regulatory frameworks, informed by FDA and EMA standards; and (6) expanded research on oncology-specific pharmacokinetic markers. While safety concerns dominated discussions, experts also acknowledged the potential of generics to reduce costs, improve equity, and enhance the sustainability of oncology care. Conclusions: The findings underscore the need for oncology-specific regulatory frameworks that extend beyond conventional bioequivalence standards. A balance is required: cost savings and equity gains offered by generics must be matched with robust safety mechanisms, regulatory harmonization, and physician-led oversight. Future research should expand expert representation, integrate real-world data, and address biosimilars in dedicated analyses to ensure safe and equitable integration of non-innovator therapies in cancer care. Full article

In the era of digital intelligence, information literacy (IL) competency has become a critical indicator for measuring the comprehensive quality and sustainable development potential of university’s education. Using Yancheng Institute of Technology as a case study, this study systematically elucidates the connotation and current development status of college students’ IL within the framework of sustainable development. An evaluation index system is constructed, comprising four dimensions: information awareness and attitude, information ethics, law and security, information knowledge and skills, and information integration and innovation. The study employs the Analytic Hierarchy Process (AHP) to determine the weights of indicators at various levels and integrates the Fuzzy Comprehensive Evaluation Method (FCEM) to establish a quantitative assessment model for IL competency. Empirical research demonstrates that the proposed model effectively enables a multidimensional and quantitative evaluation of students’ IL, with results that exhibit sound scientific validity and applicability. Based on the analysis, specific strategies are proposed to enhance students’ IL from the perspectives of curriculum design, teaching models, and library services, thereby providing theoretical references and practical pathways for advancing informatization and sustainable development in higher education. Full article

This study investigated groundwater suitability for irrigation in the Hennaya Irrigated region of Northwest Algeria. The research pursued two primary objectives: first, to establish the hydrochemical origin of the groundwater through comprehensive analyses including hydrochemical parameters, diagrams, and hierarchical clustering; and second, to assess its suitability for irrigation based on key criteria such as the Water Quality Index (WQI), Wilcox, and US Salinity diagrams. The analysis revealed a high level of groundwater suitability for irrigation, as indicated by various indices: Sodium Adsorption Ratio (SAR) values ranged from 1.69 to 2.55 (Excellent), Sodium Percentage (Na%) ranged from 24.22% to 36.98% (Good), and the Residual Sodium Carbonate (RSC) was negative, falling between −8.91 to −1.70 meq/L (Safe). Kelly’s Ratio (KR) ranged from 0.32 to 0.59 (Good), and the Permeability Index (PI) was between 62% and 99% (Moderate). Supported by the Analytic Hierarchy Process (AHP) and spatial analysis, the Water Quality Index (WQI) values ranged from 69.25 to 88.71, categorizing the groundwater in the study area as ‘Good’ quality. While suitable for irrigation, the groundwater showed slight salinity (EC 1247–2010 μS/cm) and alkalinity (pH 7.09–8.02), with elevated total dissolved solids (TDSs) ranging from 990 to 1930 mg/L, approaching the permissible limits for optimal agricultural use. The dominant ion concentrations (Ca²⁺ > Na⁺ > Mg²⁺ > K⁺; HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻) indicate a mixed hydrochemical facies influenced by both water–rock interactions and evaporative processes. Although these findings are promising, they highlight the necessity for preventive measures. Ongoing proactive management and continuous monitoring are essential to ensure the long-term sustainability and protection of groundwater resources in the region. Full article

Advances in Earth observation technology using multispectral imagery from satellite Earth observation systems and sensors mounted on unmanned aerial vehicles (UAVs) are enabling more accurate crop monitoring. These images, once processed, facilitate the analysis of crop health by enabling the study of crop vigour, the calculation of biomass indices, and the continuous temporal monitoring using vegetation indices (VIs). These indicators allow for the identification of diseases, pests, or water stress, among others. This study compares images acquired with the Altum PT sensor (UAV) and Super Dove (satellite) to evaluate their ability to detect specific problems in super-intensive olive groves at two critical times: January, during pruning, and April, at the beginning of fruit development. Four different VIs were used, and multispectral maps were generated for each: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), the Normalized Difference Red Edge Index (NDRE) and the Leaf Chlorophyll Index (LCI). Data for each plant (n = 11,104) were obtained for analysis across all dates and sensors. A combined methodology (Spearman’s correlation coefficient, Student’s t-test and decision trees) was used to validate the behaviour of the variables and propose predictive models. The results showed significant differences between the sensors, with a common trend in spatial patterns and a correlation range between 0.45 and 0.68. Integrating both technologies enables multiscale assessment, optimizing agronomic management and supporting more sustainable precision agriculture. Full article

Wheat is an important crop in Rwanda; however, rapid population growth, urbanization, and shifting dietary preferences have driven demand far beyond domestic production capacity, resulting in a steady increase in imports. Closing this gap requires a variety of management strategies that jointly optimise yield, processing quality, and sustainability. This study evaluated ten widely cultivated wheat (Triticum aestivum L.) varieties in Rwanda through an integrated assessment of grain yield, quality traits, and rheological properties. Yields ranged from 4.3 to 6.3 t ha⁻¹, with Nyaruka and Gihundo achieving the highest productivity. Quality attributes, including protein content (PC), wet gluten (WG), gluten index (GI), falling number (FN), and Zeleny sedimentation value (ZSV), varied significantly, with Cyumba and Reberaho showing superior protein levels. Mixolab-based rheological analyses revealed marked diversity in dough development time, torque, and water absorption, with Keza and Nyangufi exhibiting favorable baking profiles. Statistical analyses highlighted trade-offs between yield and quality, as high-yielding varieties such as Nyaruka showed weaker baking characteristics. These findings demonstrate that linking agronomic performance with grain and dough quality traits provides a pathway towards targeted breeding, sustainable intensification, and enhanced food security. Integrating genetic selection with tailored management and processing strategies can improve both productivity and product value, strengthening the resilience and economic viability of Rwanda’s wheat sector. Full article

In the current geopolitical context, sustainable energy development has become one of the pillars of global economic growth. This issue is well recognized in the European Union, which has undertaken a number of measures to achieve sustainable development goals. For these measures to be effective, it is essential to conduct a reliable, multi-variant diagnosis of the state of energy development in the EU-27 countries. This paper addresses this highly topical and important issue. It presents a new proprietary method—the Entropy–Evolutionary Evaluation of Sustainability (E³)—based on a multidimensional approach to researching and evaluating the state of sustainable energy development in the EU-27 countries between 2014 and 2023. Through the integration of 19 indicators representing the adopted dimensions of the study (energy, economic, environmental, and social), the method enabled both a static assessment and a dynamic analysis of energy transition processes across space and time. To determine the weights of the indicators for each dimension of sustainable energy development, the CRITIC, Entropy, and equal weight methods, along with the Laplace criterion, were applied. The Analytic Hierarchy Process method was used to establish the weights of the dimensions themselves. An important component of the approach was the inclusion of scenario studies, which made it possible to assess sustainable energy development under five variants: baseline, level, equilibrium, transformational, and neutral. These scenarios were based on different weight values assigned to three factors: the level of energy development (L), its stability (S), and the trajectory of change ($\tilde{T}$). The results, expressed in the form of a total index value and dimensional indices, reveal significant diversity among the EU-27 countries in terms of sustainable energy development. Sweden, Finland, Denmark, Latvia, and Austria achieved the best results, while Cyprus, Malta, Ireland, and Luxembourg—countries heavily dependent on energy imports, with limited diversification of their energy mix and high energy costs—performed the worst. The developed method and the results obtained should serve as a valuable source of knowledge to support decision-making and the formulation of strategies concerning the pace and direction of actions related to the energy transition. Full article

The ongoing digitalization of energy infrastructure is a crucial enabler for improving efficiency, reliability, and sustainability in gas distribution networks, especially in the context of decarbonization and the integration of alternative energy carriers (e.g., renewable gases including biogas, green hydrogen). This study presents the development and application of a Digital Twin framework for a real-world gas distribution network developed using open-source tools. The proposed methodology covers the entire digital lifecycle: from data acquisition through smart meters and GIS mapping, to 3D modelling and simulation using tools such as QGIS, FreeCAD, and GasNetSim. Consumption data are collected, processed, and harmonized via Python-based workflows, hourly simulations of network operation, including pressure, flow rate, and gas quality indicators like the Wobbe Index. Results demonstrate the effectiveness of the Digital Twin in accurately replicating real network behavior and supporting scenario analyses for the introduction of greener energy vectors such as hydrogen or biomethane. The case study highlights the flexibility and transparency of the workflow, as well as the critical importance of data quality and availability. The framework provides a robust basis for advanced network management, optimization, and planning, offering practical tools to support the energy transition in the gas sector. Full article

Energy consumption and environmental pollution pose significant challenges to sustainable development. This study develops a comprehensive coupled framework model that advances the quantitative integration of carbon (C), nitrogen (N), and phosphorus (P) cycles driven by multiple anthropogenic pollution sources. This paper used Panjin city as a case study to analyze the dynamic changes and interconnections among C, N, and P. Results indicated that net anthropogenic carbon inputs (NAI_C) increased by 33% from 2016–2020, while net anthropogenic nitrogen inputs (NAI_N) and net anthropogenic phosphorus inputs (NAI_P) decreased by 14% and 28%, respectively. The primary driver of NAI_C was energy consumption, while wetlands were the dominant carbon sequestration sink. Agricultural production was identified as the primary source of NAI_N and NAI_P, and approximately 4.5% of NAI_N and 2.9% of NAI_P were discharged into receiving water bodies. We demonstrate that human activities and natural processes exhibit dual attributes, producing positive and negative environmental effects. The increase in carbon emissions drives economic growth and industrial restructuring; however, the enhanced economic capacity also strengthens the ability to mitigate pollution through environmental protection measures. Similarly, natural ecosystems, including forests and grasslands, contribute to carbon sequestration and the release of non-point source pollution. The comprehensive environmental impact assessment of C, N, and P revealed that the comprehensive environmental index for Panjin city exhibited an improved trend. The factors of energy structure, energy efficiency, and economic scale promoted NAI_C growth, with the economic scale factor alone accounting for 93% of the total increment. Environmental efficiency factor and population size factor were the primary drivers in reducing NAI_N and NAI_P discharges into the receiving water bodies. We propose a novel management model, ecological restoration, clean energy utilization, resource recycling, and pollution source reduction to achieve systemic governance of C, N, and P inputs. Full article

The construction industry is struggling to resolve the issue of the enormous quantity of waste produced during construction processes, which impacts the performance and sustainability of projects. Causes of waste generation have been studied by researchers to formulate waste minimization strategies for these projects. The research on waste in highway infrastructure projects and waste causes specific to the roles and competencies of project team members is inadequate. This quantitative study addresses this gap by evaluating the influence of project managers (PMs) in minimizing CW through a structured questionnaire survey administered to 300 professionals, yielding 129 valid responses (43% response rate). The results indicate that 8.5% of construction materials are wasted in highway projects. Among four key project stakeholders (PM, quantity surveyor, designer, and client), PMs were rated as having the most significant impact on waste minimization (mean Likert score: 4.5/5). Using the Relative Importance Index (RII), the study identified the top five waste causes linked to PM competencies: faulty work requiring the work to be carried out again (RII = 0.742), wrong construction methods (0.734), lack of awareness (0.731), poor supervision (0.721), and poor material planning (0.706). A waste minimization framework is proposed, linking each of these causes to specific PM competencies and actionable strategies. These findings provide empirical support for targeting PM training and resource planning to reduce material waste in highway construction projects. Full article

This study aims to evaluate the effects of repeated mechanical recycling on the properties of a novel aliphatic polyketone composite reinforced with 15 wt% and 30 wt% glass fibers (PK15GF and PK30GF), providing insights into its potential for sustainable engineering applications. The investigation focuses on three main aspects: changes in melt flow index (MFI) and viscosity, the influence of glass fiber content on thermal and mechanical stability, and the retention of structural integrity and crystallinity under multiple processing cycles. Composites, commercially available since 2019, were subjected to single- and five-cycle recycling with 100% reprocessed content. Comprehensive characterization—including tensile testing, Differential Scanning Calorimetry (DSC), Dynamic Mechanical Analysis (DMA), Fourier Transform Infrared Spectroscopy (FT-IR), Melt-Flow Index (MFI), Differential Thermal Analysis (DTA), and mechanical tensile testing—revealed filler-dependent alterations in morphology, thermal stability, and crystallinity. MFI decreased from 100.56 to 42.63 g/10 min for PK15GF, indicating pronounced chain scission, recombination, and crosslinking, whereas PK30GF decreased only from 89.00 to 59.76 g/10 min. FT-IR spectra confirmed greater crosslinking in PK15GF, while DSC and DMA demonstrated smaller T_g and ΔHm variations in PK30GF (T_g +0.45 °C, ΔHm −13.93 J·g⁻¹) versus PK15GF (T_g +1.13 °C, ΔHm −69.24 J·g⁻¹). These findings reveal that higher glass fiber content mitigates degradation, preserves structural integrity, and maintains thermal and viscoelastic stability, establishing clear correlations between filler content, mechanical performance, and recyclability. Overall, this work provides mechanistic insights into degradation pathways and demonstrates the potential of glass fiber-reinforced aliphatic polyketones for sustainable, high-performance engineering and automotive applications. Full article

In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess Plateau, over the past 25 years, due to many factors, such as coal mining, using the area as a case study. In this study, Landsat satellite images from 2000 to 2024 were used to derive the remote sensing ecological index (RSEI), while the RSEI results were comprehensively analyzed using the Sen+Mann-Kendall method with Geodetector, respectively. Simultaneously, this study utilized land use datasets to calculate the ecological grade (EG) index. The EG index was then analyzed in conjunction with the RSEI. The results show that in the time dimension, the ecological quality of the Ningdong mining area shows a non-monotonic trend of decreasing and then increasing during the 25-year period; The RSEI average reached its lowest value of 0.279 in 2011 and its highest value of 0.511 in 2022. In 2024, the RSEI was 0.428; The coupling matrix between the EG and RSEI indicates that the ecological environment within the mining area has improved. Through ecological factor-driven analysis, we found that the ecological environment quality in the study area is stably controlled by natural topography (slope) and climate (precipitation) factors, while also being disturbed by human activities. This experimental section demonstrates that ecological and environmental evolution is a complex process driven by the nonlinear synergistic interaction of natural and anthropogenic factors. The results of the study are of practical significance and provide scientific guidance for the development of coal mining and ecological environmental protection policies in other mining regions around the world. Full article

The main objective of this study is to determine the knowledge and awareness levels of climate change among preparatory class students at Zonguldak Bülent Ecevit University in the Western Black Sea Region of Türkiye using an unsupervised clustering approach. Within this scope, a survey was administered to university students (n = 280). Participant scores for the survey sections containing five-point Likert-type questions on climate change awareness were calculated using min–max normalization. The normalized data was then processed using the k-means algorithm, a well-known technique in unsupervised machine learning. This resulted in a classification (clustering) related to climate change awareness. The number of clusters was determined using the Silhouette index. Three clusters identified using k-means and Silhouette index ($S\approx 0.55)$ revealed the knowledge and application levels of student groups regarding climate change awareness. As a result of clustering, it was determined that Cluster-3 students (n = 134, 47.9%), defined as having a high level of knowledge and application, had a higher impact value in their overall assessments of green space-focused issues related to climate change awareness compared to the overall assessments of students in other clusters. Some notable findings concerning the attitudes of Cluster-3 students highlight climate change awareness-related practices. These include minimizing water consumption to levels necessary for ecosystem water management (mean = 95.7, std. deviation = 10.9) and exercising controlled, sustainable daily energy use to alleviate pressure on green spaces (mean = 94.4, std. deviation = 12.5). This study offers practical insights for policymakers, educators, and institutions, emphasizing the need to enhance climate education and to promote the active involvement of younger generations in shaping sustainable environments. Full article

Accurate identification of groundwater pollution sources is crucial for the socio-economic development of a region. In highly urbanized areas, where human activities have a pronounced impact on groundwater, however, the hydrochemical evolution patterns and sources of pollutants remain unclear. Taking Shenzhen, a highly urbanized city in China, as a case study, this research employed a combination of multivariate statistical techniques and the Positive Matrix Factorization (PMF) model to elucidate the hydrochemical evolution and quantitatively parse the pollution sources of groundwater in such regions. The results revealed that the pH of groundwater in the study area ranged from 4.24 to 7.31, indicating weak acidity to neutrality. The exceedance rates for pH, NH₄⁺, COD, Mn, and Fe were as high as 67.1%, 44.3%, 44.3%, 34.3%, and 31.4%, respectively. The Water Quality Index assessment revealed that 32.9% of the groundwater samples were classified as poor, highlighting the significant impact of human activities. Land-use types significantly affected groundwater quality, with urban areas exhibiting higher concentrations of the COD, NO₃⁻, Mn, and Fe compared to agricultural and forested areas. The predominant hydrochemical type of groundwater in the study area was HCO₃·Cl—Ca·Na, with rock weathering (primarily silicate weathering) being the dominant process controlling groundwater chemistry. The PMF model identified three major pollution sources in the highly urbanized region: domestic and industrial wastewater, enhanced water-rock interactions leading to the release of hydrochemical components, and agricultural fertilizers, contributing 43.9%, 37.0%, and 19.1% to groundwater pollution, respectively. Geostatistical spatial interpolation techniques demonstrated that in urban areas, groundwater quality was primarily controlled by the discharge of domestic and industrial wastewater, while in agricultural areas, excessive fertilizer application was the main driver of groundwater degradation. These findings provide a scientific basis for groundwater pollution prevention and sustainable utilization in highly urbanized regions. Full article

Against the backdrop of global agricultural transformation, rural China faces the critical challenge of reconciling economic development with environmental conservation and social well-being. This study, grounded in the rural revitalization strategy, investigates the internal mechanisms, level measurement, and sustainable development paths of rural industrial integration based on the “Triple Integration of Production, Livelihood and Ecology” (PLE) philosophy. Firstly, we discussed the suitability and the mechanisms of this philosophy on China’s rural industrial integration. Secondly, based on a textual corpus extracted from academic journals and policy documents, we employed an LDA topic model to cluster the themes and construct an evaluation indicator system comprising 29 indicators. Then, utilizing data from the China Statistical Yearbook and the China Rural Statistical Yearbook (2013–2022), we measured the level of China’s rural industrial integration using the entropy method. The composite integration index displays a continuous upward trend over 2013–2022, accelerating markedly after the 2015 stimulus policy, yet a temporary erosion of “production–livelihood–ecology” synergy occurred in 2020 owing to an exogenous shock. Lastly, combining the system dynamics model, we simulated over the period 2023–2030 the three sustainable development scenarios: green ecological development priority, livelihood standard development priority and production level development priority. Research has shown that (1) the “Triple Synergy of Production, Livelihood and Ecology” philosophy and China’s rural industrial integration are endogenously unified, and they form a two-way mutual mechanism with the common goal of sustainable development. (2) China’s rural industrial integration under this philosophy is characterized by production-dominated development and driven mainly by processing innovation and service investment, but can be constrained by ecological fragility and external shocks. (3) System dynamics simulations reveal that the production-development priority scenario (Scenario 3) is the most effective pathway, suggesting that the production system is a vital engine driving the sustainable development of China’s rural industrial integration, with digitalization and technological innovation significantly improving integration efficiency. In the future, efforts should focus on transitioning towards a people-centered model by restructuring cooperative equity for farmer ownership, building community-based digital commons to bridge capability gaps, and creating market mechanisms to monetize and reward conservation practices. Full article