Search Results (4,173)

Tin is an indispensable metal for contemporary society owing to its extensive application. China is a major tin manufacturer and consumer worldwide. Nonetheless, the crucial characteristics of its tin metabolism remain limited. Therefore, a dynamic material flow analysis (MFA) from 2001 to 2022 was performed in this study to trace China’s tin flows and stocks. Findings show that China became a net tin exporter from a life cycle perspective, and annual tin consumption embodied in various final products varied between 49.3 kilo tons (Kt) in 2001 and 161.5 Kt in 2022, with home appliances and electronics being the dominant consumption sectors. A total of 913.3 Kt of tin became in-use stocks. In addition, the imported tin embodied in various final products varied between 13.9 Kt in 2001 and 21.6 Kt in 2022, with machinery being the dominant consumption sector. The exported tin embodied in various final products varied between 12.0 Kt in 2001 and 76.3 Kt in 2022, with machinery being the dominant consumption sector. Finally, this study proposes some suggestions, in view of the Chinese reality, like enhancing tin recycling, promoting tin geological prospecting, optimizing the structure of the tin trade, and promoting regional cooperation, to improve the supply security of tin resources. Full article

With the widespread application of cyber–physical systems (CPS) in the field of automation, security concerns have become increasingly prominent. One critical and urgent challenge is the accurate identification of sensor nodes compromised by false data injection (FDI) attacks in multiple-input multiple-output (MIMO) control systems. Building on the implementation of multi-step sampling and residual-based anomaly detection using a support vector machine (SVM), this paper further introduces the Shapley value evaluation method from cooperative game theory and a voting mechanism, and proposes a method for node attack localization. First, multi-step sampling is conducted within each control period to provide a large amount of effective data for the localization of attacked sensor nodes. Next, the residual between the estimated value of the MIMO system’s full response and the actual value received by the controller is calculated, and an SVM model is used to detect anomalies in the residual. Finally, the Shapley value contribution of each residual to the SVM anomaly detection result is evaluated based on cooperative game theory and combined with a voting mechanism to achieve accurate localization of the attacked sensor nodes. Simulation results demonstrate that the proposed method achieves an anomaly detection accuracy of 96.472% and can accurately localize attacked nodes in both single-node and multi-node attack scenarios, indicating strong robustness and practical applicability. Full article

This paper investigates the error performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) systems employing one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. In cooperative DF MIMO systems, detection errors at the relay may propagate to the destination, thereby degrading overall detection performance. Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. To address this issue, an approximate maximum likelihood (AML) detection scheme is introduced, which significantly reduces complexity while maintaining reliable performance. However, its analysis under one-bit ADCs is challenging because of its nonlinearity. The main contributions of this paper are summarized as follows: (1) a tractable upper bound on the pairwise error probability (PEP) of the AML detector is derived using Jensen’s inequality and the Chernoff bound, (2) the asymptotic behavior of the PEP is analyzed to reveal the achievable diversity gain, (3) the analysis shows that full diversity is attained only when symbol pairs in the PEP satisfy a sign-inverted condition and the relay correctly decodes the source symbol, and (4) the simulation results verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed analysis. Full article

Background/Objectives: Biliary tract cancers (BTCs) are aggressive malignancies with a poor prognosis. Surgery remains the only curative option, yet recurrence rates are high, and the role of adjuvant chemotherapy remains debated. This study aims to evaluate the impact of adjuvant chemotherapy and prognostic factors on survival outcomes in resected BTCs. Methods: We conducted a retrospective multicenter study analyzing patients diagnosed with intrahepatic (iCCA) and extrahepatic cholangiocarcinoma (eCCA) or gallbladder cancer (GBC) who underwent curative-intent surgical resection between 1999 and 2023. Demographic, clinicopathological, and treatment data were collected from institutional databases. Survival outcomes were assessed using Kaplan–Meier analysis, and prognostic factors were identified through Cox proportional hazards regression. Results: A total of 155 patients were included, with a median follow-up of 84.6 months. The cohort comprised 38.7% iCCA, 31.6% eCCA, and 29.7% GBC. R0 resection was achieved in 77.4% of cases, while lymph node involvement was present in 39.4%. Median overall survival (OS) significantly varied by stage (p < 0.001), ranging from >60 months for stage I to ~12 months for stage IVA. Eastern Cooperative Oncology Group (ECOG) performance status (PS) emerged as the strongest independent prognostic factor for OS (p < 0.001). Adjuvant chemotherapy, administered to 49.0% of patients, did not significantly improve OS in the overall cohort (p = 0.899). However, subgroup analyses suggested potential benefits in iCCA and eCCA but not in GBC. High CA19-9 levels and vascular invasion were associated with poorer survival outcomes. Conclusions: This study highlights the prognostic significance of ECOG PS, resection margin status, lymph node involvement, and CA19-9 levels in resected BTCs. The lack of a clear survival benefit from adjuvant chemotherapy underscores the need for improved therapeutic strategies. Future research should focus on refining risk stratification models and identifying more effective adjuvant treatments to enhance long-term survival outcomes in patients with BTC. Full article

The Gunung Sewu UNESCO Global Geopark (GSUGGp) is one of Indonesia’s 12 UNESCO-designated geoparks. Its presence is expected to enhance rural development by boosting the local economy through tourism. However, there is a lack of statistical evidence quantifying the economic benefits of geopark development, mainly due to the complex, non-linear nature of these impacts and limited village-level economic data available in Indonesia. To address this gap, this study aims to measure how socio-economic and environmental factors contribute to the Village Development Index (VDI) within the GSUGGp area, which includes the districts of Gunung Kidul, Wonogiri, and Pacitan. A machine learning–deep learning approach was employed, utilizing four algorithms grouped into eight models, with hyperparameter tuning and cross-validation, tested on a sample of 92 villages. The analysis revealed insights into how 17 independent variables influence the VDI. The Artificial Neural Network (ANN) algorithm outperformed others, achieving an R-squared of 0.76 and an RMSE of 0.040, surpassing random forest, CART, SVM, and linear models. Economically related factors—considered the foundation of rural development—had the strongest impact on village progress within GSUGGp. Additionally, features related to tourism, especially beach tourism linked to geological landscapes, contributed significantly. These findings are valuable for guiding geopark management and policy decisions, emphasizing the importance of integrated strategies and strong cooperation among local governments at the regency and provincial levels. Full article

(1) Background: This study used latent profile analysis (LPA) to investigate family patterns of paternal and maternal responses to adolescents’ discrete emotions (happiness, sadness, and anger) and examined the relationship between these profiles and demographic factors, as well as adolescents’ emotion adjustment (emotion regulation and depressive symptoms). (2) Methods: A sample of 666 adolescents reported parental responses and their emotional adjustment; their mothers provided family information. (3) Results: (a) The LPA identified four profiles for adolescent happiness, including high enhancing but low dampening and neglect from both parents (Consistent Supportive); low enhancing but high dampening and neglect from both parents (Consistent Unsupportive); low to moderate scores on each response from both parents (Consistent Disengaging); and high maternal dampening and neglect but relatively low scores on the paternal response (Inconsistent). There were two profiles for sadness (Consistent Supportive, Consistent Unsupportive) and three for anger (Consistent Supportive, Consistent Unsupportive, Consistent Disengaging). (b) Parents with boys, higher incomes, better education, and greater marital satisfaction were likely to be classified into the Consistent Supportive profile across emotions. (c) When adolescents perceived their parents with the Consistent Supportive profile, they would show the best emotional adjustment; while for parents with the Inconsistent profile (for happiness) and the Consistent Unsupportive profile, the adolescents had the poorest outcome. Interestingly, adolescents who perceived their parents as fitting the Consistent Disengaging profile (especially for anger) exhibited comparatively less adverse adjustment. (4) Implications: A person-centered approach highlights different patterns of emotion socialization, underscores the importance of fostering parental cooperation and supportive responses to adolescents’ happiness, and suggests that joint disengagement from anger may promote healthier emotional development. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

This study investigates economic decision-making behaviors among welfare recipients in Miami, Florida, by leveraging well-established experimental protocols: the Guessing Game, the Prudence Measurement Task, the Risk Aversion Task, and the Stag Hunt Game. For this purpose, our study defines financial decisions as the underlying individual preferences that serve as validated proxies for savings behavior, debt management, job-search intensity, and participation in cooperative finance. A central objective is to compare the behavior of welfare recipients to that of undergraduate students, a cohort typically used in experimental economics research. The analysis reveals significant differences between the two groups in strategic thinking and coordination, particularly across ethnic and gender lines. Non-Hispanic/Latino participants in Miami displayed significantly higher average guesses in the Guessing Game compared to their counterparts in Tucson, indicating potential discrepancies in the depth of strategic reasoning. Additionally, female participants in Tucson exhibited higher levels of coordination in the Stag Hunt Game compared to females in Miami, suggesting variance in cooperative behavior between these groups. Despite these findings, regression models demonstrate that location, gender, and ethnicity collectively account for only a small fraction of the observed variance, as evidenced by low R² values and substantial mean squared errors across all games. These results suggest that individual heterogeneity, rather than broad demographic variables, may be more influential in shaping economic decisions. This study underscores the complexity of generalizing findings from traditional student samples to more diverse populations, highlighting the need for further investigation into the socioeconomic factors that drive financial decision-making. Full article

Primary-level social risk prevention and control is a complex, systemic endeavor that requires close cooperation among various local government departments. Within this context, addressing bureaucratic segmentation and strengthening interdepartmental collaboration are critical issues in primary-level social risk governance. This study uses social network analysis and the exponential random graph model to examine the collaborative network structure and driving mechanisms among government departments engaged in risk prevention, with J City as a network study. The findings reveal that (1) while a collaborative governance framework exists, the network has low overall density, strong localized clustering, and a clear core-periphery structure, indicating the need for improved coordination and more refined collaborative mechanisms; (2) the formation of the risk prevention network is influenced by both endogenous structural factors and exogenous actor attributes. Endogenously, reciprocity and transitivity play significant roles in tie formation; exogenously, departments with similar resource mobilization capacities are more likely to collaborate, while those with strong communication, digital technology, and resource mobilization capabilities are more likely to initiate collaborations, and those with high communication capacity are more likely to accept collaborative offers. This study offers insights into the dynamics and formation mechanisms of departmental collaborative networks in primary-level social risk governance. Full article

Packaging waste is a major environmental issue, making the transition to sustainable solutions imperative. This article proposes the concept of Shared Producer Responsibility (SPR) as a key approach to advancing sustainable packaging in the fast-moving consumer goods (FMCG) sector. The study explores how the United Nations Sustainable Development Goals (SDGs), environmental, social, and governance (ESG) reporting, and stakeholder engagement converge to support this transition. The research identifies current trends, challenges, and gaps in sustainable packaging practices through a systematic literature review (SLR) and analysis of sustainability and ESG reports from leading FMCG and packaging companies. The findings highlight the need for standardised reporting frameworks and improved stakeholder cooperation to enhance transparency and accountability in sustainability efforts. This study proposes a conceptual framework for accelerating sustainable packaging adoption through combining strategies like consumer education, regulatory incentives, and clear product labelling. The proposal to implement the concept of Shared Producer Responsibility emphasises the shared accountability of FMCG companies and packaging manufacturers in managing the full environmental lifecycle of packaging materials. This approach is crucial for achieving SDG 12 (responsible consumption and production) and SDG 13 (climate action) and driving more effective and sustainable packaging practices across the FMCG industry. Full article

The space flexible netted pocket capture system provides a flexible and stable solution for capturing non-cooperative space objects. This paper investigates the control problem for the capture of non-cooperative targets undergoing motion. A dynamic model of the capturing net is established based on the absolute nodal coordinate formulation (ANCF) and equivalent plate–shell theory. A contact collision force model is developed using a spring–damper model. Subsequently, a feedforward controller is designed based on the estimated collision force from the dynamic model, aiming to compensate for the collision effects between the target and the net. By incorporating the collision estimation data, an extended state observer is designed, taking into account the collision estimation errors and the flexible uncertainties. A sliding mode feedback controller is then designed using the fast terminal sliding mode control method. Finally, simulation analysis of target capture under different motion states is conducted. The results demonstrate that the spacecraft system’s position and attitude average flutter amplitudes are less than ${10}^{-2}$ m and ${10}^{-2}$ deg. In comparison to standard sliding mode control, the designed controller reduces the attitude jitter amplitude by an order of magnitude, thus demonstrating its effectiveness and superiority. Full article

Nucleolin (NCL), an RNA-binding protein which regulates critical cellular processes, is frequently dysregulated in human cancers, including breast cancer, making it an attractive therapeutic target. However, molecular details of the RNA-NCL interaction have not been investigated yet. A tRNA fragment named tRF3E, displaying tumor suppressor roles in breast cancer, was found to bind NCL with high affinity displacing NCL-controlled transcripts. Here, we investigated the determinants and cooperativity of tRF3E-NCL interaction by Electrophoretic Mobility Shift Assays and in silico docking analysis, using wild-type or mutated tRF3E. We found that NCL, through its RNA-binding domains (RBD1–2 and RBD3–4), binds simultaneously two tRF3E molecules, giving rise to an energetically favored complex. Instead, a mutant form of tRF3E (M19–24), in which the NCL recognition element in position 19–24 has been disrupted, contacts NCL exclusively at RBD3–4, causing the loss of cooperativity among RBDs. Importantly, when expressed in MCF7 breast cancer cells, tRF3E significantly reduced cell proliferation and colony formation, confirming its role as tumor suppressor, but tRF3E functional properties were lost when the 19–24 motif was mutated, suggesting that cooperativity among multiple domains is required for the NCL-mediated tRF3E antitumor function. This study sheds light on the dynamic of RNA-NCL interaction and lays the foundations for using tRF3E as a promising NCL-targeted biodrug candidate. Full article

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science. Full article

This article explores the role of the European Union (EU) as a normative gender actor promoting women’s participation in STEM (Science, Technology, Engineering, and Mathematics) within the Eastern Partnership (EaP) region. In a context marked by global inequality and overlapping international efforts, this paper assesses the extent to which EU-driven Europeanisation influences national gender policies in non-EU states. Using a postfunctionalist lens, this research draws on a qualitative analysis of EU-funded programmes, strategic documents, and a detailed case study encompassing Armenia, Georgia, Moldova, Ukraine, Belarus, and Azerbaijan. This study highlights both the opportunities created by EU initiatives such as Horizon Europe, Erasmus+, and regional programmes like EU4Digital and the challenges presented by political resistance, institutional inertia, and socio-cultural norms. The findings reveal that although EU interventions have fostered significant progress, structural barriers and limited national commitment hinder the long-term sustainability of gender equality in STEM. Moreover, the withdrawal of other global actors increases pressure on the EU to maintain leadership in this area. This paper concludes that without stronger national alignment and global cooperation, EU gender policies risk becoming symbolic rather than transformative. Full article

This research focuses on enhancing cooperative localization for heterogeneous mobile robots composed of a quadcopter and an unmanned ground vehicle. The study employs sensor fusion techniques, particularly the Extended Kalman Filter, to fuse data from various sensors, including GPSs, IMUs, and cameras. By integrating these sensors and optimizing fusion strategies, the research aims to improve the precision and reliability of cooperative localization in complex and dynamic environments. The primary objective is to develop a practical framework for cooperative localization that addresses the challenges posed by the differences in mobility and sensing capabilities among heterogeneous robots. Sensor fusion is used to compensate for the limitations of individual sensors, providing more accurate and robust localization results. Moreover, a comparative analysis of different sensor combinations and fusion strategies helps to identify the optimal configuration for each robot. This research focuses on the improvement of cooperative localization, path planning, and collaborative tasks for heterogeneous robots. The findings have broad applications in fields such as autonomous transportation, agricultural operation, and disaster response, where the cooperation of diverse robotic platforms is crucial for mission success. Full article