Search Results (541)

With hybrid electric propulsion attracting increasing attention in the maritime sector, quantitative evaluation of fuel consumption benefits associated with energy storage system (ESS) integration remains limited. Through real operational data of 5000 GT class, this study builds a framework to explain the interactions between propulsion configuration, generator load sharing and ESS capacity. This study investigated three generator control strategies and four scenarios for ESS capacity (200–500 kWh) by applying unified FOC procedures. This study reveals that generator control logic can affect more than ESS capacity. Compared with mechanical propulsion (140.72 tons), rule-based control achieved an 11.45% reduction. Equal load sharing scored a 6.27% reduction gap of 5.18%. ESS capacity exhibited a nonlinear effect. FOC exhibited a meaningful threshold at 300 kWh (121.63 tons); beyond this point, additional capacity yielded improvements of less than 0.1 tons. Ironically, a 200 kWh setup resulted in FOC of 126.13 tons—higher than the electric propulsion without battery under rule-based control (124.60 tons). Therefore, ESS capacity and generator control logic should be considered at the same time. These outcomes can provide practical design criteria under realistic operating conditions. Full article

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study. Full article

Climate change poses escalating risks to environmental, economic, and social systems worldwide, making accurate temperature forecasting a critical component of climate impact assessment and mitigation planning. Advances in data-driven modelling have expanded the range of tools available for analysing climate time series, complementing traditional statistical approaches. The continued increase in global surface temperatures, driven primarily by anthropogenic greenhouse gas (GHG) emissions, underscores the need for forecasting models capable of capturing complex and non-linear climate dynamics. This study compares the predictive performance of a Long Short-Term Memory (LSTM) neural network with a Seasonal Autoregressive Integrated Moving Average (SARIMA) model using historical global temperature data. The results show that LSTM outperforms SARIMA at the global scale, achieving an R² of 0.9846, RMSE of 0.1528 °C, and MAE of 0.1198 °C, representing a 50.7% reduction in error relative to the SARIMA baseline (R² = 0.9364; RMSE = 0.3100 °C). However, regional analyses reveal heterogeneous performance, with LSTM overestimating seasonal variability in certain regions, while SARIMA exhibits greater local stability. Sectoral emission analysis identifies agriculture and energy production as the dominant global contributors, with substantial regional variation. These findings suggest that hybrid modelling approaches may offer improved robustness for regional climate assessment and policy applications. Full article

In complex ground environments, conventional RRT* often suffers from low planning efficiency and poor path quality for robot path planning. This paper proposes BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion). First, BGSE-RRT* constructs a nonlinear switching probability via bi-tree cooperative adaptive switching, together with KD-Tree nearest-neighbor acceleration and multi-condition triggering, to adaptively balance global exploration and local convergence. Meanwhile, a goal-guided expansion with dynamic target binding and adaptive step size, under a multi-constraint feasibility check, accelerates the convergence of the two trees. When the goal-guided expansion becomes blocked, BGSE-RRT* generates candidate points in local multi-sector regions using a 2D Halton low-discrepancy sequence and selects the best candidate for expansion; if the multi-sector expansion still fails, a sampling-point-guided expansion is activated to continue advancing and search for a feasible path. Second, B-spline smoothing is applied to improve trajectory continuity. Finally, in five simulation environments and ROS/real-robot joint validation, compared with GB-RRT*, BI-RRT*, BI-APF-RRT*, and BAI-RRT*, BGSE-RRT* reduces planning time by up to 84.71%, shortens path length by 2.94–6.88%, and improves safety distance by 20.68–48.33%. In ROS/real-robot validation, the trajectory-tracking success rate reaches 100%. Full article

Accurately forecasting the risk matrix and constructing a well-controlled portfolio based on these forecasts is the core objective of effective asset allocation. This paper takes the Chinese stock market as the research object, employing multiple machine learning algorithms to systematically compare the predictive performance of the Financial Stress (FS) indicator and the Economic Policy Uncertainty (EPU) index in sectoral risk management. The forecast results are subsequently applied to portfolio construction and optimization. The findings indicate that, in terms of predictive dimensions, EPU demonstrates strong performance in short-term forecasts, but its explanatory power decays rapidly as the forecasting horizon extends. In contrast, the FS factor achieves forecasting accuracy that is significantly superior to both the EPU factor and traditional price series across all time horizons, exhibiting robust long-memory characteristics and cross-period stability. At the portfolio application level, the minimum variance strategy constructed based on FS forecasts effectively reduces out-of-sample portfolio variance, achieving superior risk control performance compared to strategies based on EPU factor forecasts. This result reveals the differentiated mechanisms of the two factor types: EPU acts as a driving force for short-term risk structure reshaping, while financial stress serves as the core variable driving the evolution of long-term risk structures. Machine learning methods provide an effective technical pathway for capturing these complex nonlinear relationships. The research conclusions offer new empirical evidence for investors to optimize asset allocation decisions and for regulatory authorities to improve risk monitoring systems. Full article

For decades, Saudi Arabia has relied heavily on oil revenues to support its economic growth. While this strategy brought substantial benefits, oil prices and global demand remain volatile, and oil itself is a non-renewable resource. These realities raise important concerns about long-term economic sustainability. In response, the country has pursued economic diversification to reduce risk and build a more resilient growth model. This study examines how the roles of the oil and non-oil sectors in driving GDP growth evolved between 1970 and 2024. To capture differences across economic conditions, the study applies both four and ten quantile regression models. These approaches allow us to observe how sectoral contributions change across low, moderate, and high growth periods. The results show that oil sector growth remains positive and significant across the distribution of GDP growth, with a stronger effect during periods of higher growth. At the same time, the non-oil sector is gaining importance, not only in stronger growth conditions, but is also cushioning the economy in periods of low growth. This signals gradual structural progress toward a more balanced and sustainable economy. The two-state Markov-switching model further identifies two persistent growth regimes: one more oil-dependent and another relatively more diversified. However, oil continues to play a meaningful role in both regimes. Overall, the findings suggest a gradual, steady transition rather than a sharp structural break. For long-term sustainability, Saudi Arabia needs to continue strengthening the productivity, resilience, and competitiveness of its non-oil sectors through its oil revenues accrued during periods of high growth. The implications of this study would be beneficial for all resource-rich economies aiming at economic diversification. Full article

The rapid digital transformation of industrial systems in the 21st century has led to an exponential growth in data generated by manufacturing processes and end-user products, particularly in the automotive sector. While this big data creates new opportunities for monitoring and diagnostics, it also introduces significant challenges related to system complexity, scalability, and nonlinearity, as well as the increasing shortage of experienced domain experts. These challenges motivate the adoption of intelligent, automated fault and anomaly detection techniques capable of operating reliably under real-world conditions. The primary objective of this paper is to provide a comprehensive and structured review of the anomaly detection methodologies for automotive applications, with particular emphasis on intelligent fault diagnosis, tolerance, and monitoring architectures. To this end, the paper systematically categorizes existing approaches, including model-based, data-driven, and hybrid techniques, and analyzes their underlying principles, data requirements, computational complexity, and applicability to safety-critical systems. Based on this analysis, the paper highlights current limitations, open research challenges, and emerging trends, including the integration of machine learning and artificial intelligence with domain knowledge and control-oriented frameworks. The main contribution of this work is a unified perspective that supports researchers and practitioners in selecting, designing, and deploying effective anomaly detection solutions for next-generation automotive and cyber-physical systems. Full article

The northern agro-pastoral ecotone of China faces persistent trade-offs among cultivated land (CL) protection, energy development, water constraints, and ecological restoration, posing challenges for sustainable human–land interactions. Focusing on Yulin City from 1980 to 2020, this study develops an integrated diagnostic framework coupling pattern–process–trend–mechanism modules to analyze the spatiotemporal evolution, transition pathways, and driving forces of CL change. Results show that CL dynamics over four decades were shaped by nonlinear interactions among natural conditions, policies, economic development, and technological progress. Spatially, CL changes exhibited a distinct divergence, with ecological-driven contraction in the southern region and sandy land-based compensation in the north. Temporally, the transformation evolved from a gradual, nature-dominated stage to a policy-intensive phase characterized by abrupt shifts, followed by a refined regulation stage with multi-factor synergies. Policy interventions and economic incentives emerged as dominant drivers of CL spatial heterogeneity, with interacting factors exerting bidirectional effects. Building on these findings, a Zoning–Optimization–Synergy (ZOS) framework is proposed to support adaptive land governance, emphasizing differentiated management and cross-sector coordination. This study offers a transferable diagnostic approach for understanding CL dynamics in fragile ecotones and provides insights for managing the water–energy–food nexus under ecological transition and climate change. Full article

The accelerating adoption of electric vehicles (EVs) has positioned them among the fastest-growing sectors in the electricity market. Since reliability, energy efficiency, and robustness are the fundamental criteria in motor drive selection, the permanent magnet synchronous motor (PMSM) has emerged as a preferred choice for EV applications. Nevertheless, achieving high-performance control of PMSM systems remains challenging due to nonlinear dynamics, parameter uncertainties, and external disturbances. To address these issues, this paper proposes a predefined-time output-feedback tracking control strategy for PMSMs subject to full-state error constraints, unknown nonlinear dynamics, external disturbances, and unmeasured states. Multi-dimensional Taylor Networks (MTNs) are employed to approximate unknown nonlinearities, while MTN-based observers are designed to estimate unmeasured states. The proposed controller integrates predefined-time stability theory, a general potential Lyapunov function, dynamic surface control (DSC), and backstepping to guarantee constraint satisfaction and rapid convergence. A hyperbolic tangent function is incorporated to eliminate singularities, and a predefined-time filter is introduced to mitigate the computational complexity of recursive backstepping. Theoretical analysis based on Lyapunov methods proves that all closed-loop signals remain bounded and that the tracking error converges to zero within a prespecified time. Simulation results confirm the effectiveness, robustness, and practical feasibility of the proposed approach for PMSM-driven EV applications. Full article

In recent years, climate change has become increasingly urgent, and governments are intensifying efforts to regulate carbon-intensive industries through policy innovations. The transport sector faces particularly acute decarbonisation challenges due to its reliance on fossil fuels. This study investigates road-rail intermodal transport as a strategic solution that synergises the flexibility of trucking with the superior energy efficiency of rail. A novel arc-path 0-1 nonlinear model is developed, optimising profit maximisation while incorporating hard constraints on transport due dates. The predominant carbon emission policies—command-and-control regulations and carbon pricing mechanisms—are analysed, and the corresponding extended models are constructed. Next, the linearisation techniques are introduced. In the end, a numerical example is built to test the validity of the model and compare the optimisation decisions of the basic model and the extended models. Furthermore, a sensitivity analysis of key parameters is conducted to provide operational recommendations for enterprises to balance carbon emissions and profits. Full article

Schistosomiasis elimination is increasingly constrained less by the technical efficacy of single interventions than by systemic dynamics in coupled human–animal–environment settings, including nonlinear feedback, spatial heterogeneity, and cross-sectoral govern frictions. We conducted a systematic methodological review (search date: 1 January 2026) across PubMed, Web of Science, Scopus, EconLit, and CNKI to identify studies that (i) addressed schistosomiasis control, (ii) used explicit system-based, causal, or network-oriented analytical structures, and (iii) incorporated economic evaluation with multi-domain outcomes. We synthesized modeling architectures, economic methods, and approaches to trade-offs and uncertainty, and applied an evidence-informed systemic causality framework to assess decision-analytic adequacy. The literature grouped into three related strands: transmission and system dynamics models that capture feedback processes and rebound risks; economic evaluations dominated by cost-effectiveness analyses; and cross-sectoral or surveillance-oriented decision models optimizing implementation under resource constraints. Across strands, elimination-stage investments such as surveillance, environmental management, and coordination exhibit strong externalities and quasi-public-good properties that are systematically undervalued in single-sector, single-metric frameworks. We argue that decision-relevant evaluation should be reframed as a multi-objective resource allocation problem that integrates systemic modeling with economic valuation, explicitly addresses uncertainty, and applies multi-criteria decision analysis to support long-horizon, cross-sectoral decision-making. Full article

The pre-prepared meal industry is a vital engine for food sector upgrading in China. This study investigates the key drivers of consumer purchasing decisions and identifies strategic pathways to support high-quality industry development. Grounded in behavioral decision theory and the stimulus–organism–response framework, we propose two central research questions: (1) What are the dominant determinants of consumer purchase intention for pre-prepared meals? and (2) How do these determinants interact in nonlinear and asymmetric ways to shape final decisions? To address these questions, we analyzed 805 valid questionnaires collected in Jilin Province using an integrated machine learning framework. Data quality and validity were ensured through baseline balance tests, and sample imbalance was corrected using the SMOTE–Tomek algorithm. Six models, including Random Forest (RF) and XGBoost, were optimized via Gaussian process-based Bayesian optimization. The RF model achieved optimal performance on the test set, with an F1 score of 0.907, an AUC of 0.928, and a prediction accuracy of 0.876. To enhance model interpretability, Mean Decrease Impurity (MDI) was integrated with the SHAP framework. Our findings reveal that: (1) purchase decisions are predominantly willingness-driven, with behavioral tendency—especially recommendation willingness—accounting for over 72% of predictive importance; (2) rational considerations, such as convenience and channel accessibility, serve as foundational enablers; and (3) recommendation willingness exhibits a significant S-shaped nonlinear threshold, where a shift to “relatively willing” marks a critical marketing intervention window. SHAP force plot analysis further uncovers an asymmetric decision logic: high willingness can compensate for perceived product shortcomings, whereas the absence of core intention functions as a non-compensatory barrier. Theoretically, these findings synthesize machine learning outputs with classical behavioral models (e.g., the Theory of Planned Behavior and Prospect Theory) by empirically quantifying bounded rationality and nonlinear activation mechanisms. These findings suggest that enterprises should transition from traffic-centric to retention-oriented strategies by leveraging word-of-mouth and proximity-based channels. Moreover, establishing a collaborative governance system is essential to mitigate risk perception and ensure long-term industry prosperity. Full article

Public cultural services are traditionally viewed as welfare provisions. However, this perspective overlooks their productive externalities as critical social infrastructure. This study treats China’s National Public Cultural Service System Demonstration Zone program as a quasi-natural experiment to examine its economic performance. The analysis utilizes panel data from 280 prefecture-level cities between 2008 and 2021 and employs a multi-period difference-in-differences model. Results show that the policy successfully increased employment in the cultural sector. This was achieved by enabling flexible labor opportunities through digital platforms and government procurement, rather than through significant growth in formal enterprises. We term this structural divergence De-organized Growth. Mechanism analysis confirms that Fiscal-Digital Synergy drives this phenomenon. Effective collaboration between government funding and digital technology activates cultural consumption on the demand side and facilitates disintermediation on the supply side. Crucially, we identify a nonlinear Digital Exclusion Trap. In this trap, fiscal support is ineffective or even counterproductive in regions falling below a critical digital infrastructure threshold. The findings suggest that the equalized provision of public culture serves as a productive input for achieving UN Sustainable Development Goal 8 regarding decent work. We advocate for a shift in governance paradigms from traditional administration to a strategic purchaser role. This role leverages digital platforms to foster a more inclusive labor market. Full article

Financial markets often display nonlinear and turbulent dynamics during periods of stress, and crude-oil and global equity systems frequently demonstrate closely connected forms of instability. Earlier studies report multifractality, chaotic features and regime-dependent spillovers across commodities and equities, yet existing approaches rarely succeed in capturing both the intrinsic complexity of oil-market behavior and the changing structure of cross-asset dependence. This limitation reduces the ability to distinguish calm from turbulent regimes and weakens short-horizon risk assessment. The present study introduces a unified framework that quantifies and predicts systemic instability within the coupled oil–equity system. The analysis constructs a crude-oil complexity index based on multifractal fluctuation analysis, permutation and approximate entropy, and Lyapunov-based indicators of chaotic dynamics. At the same time, it develops an information-theoretic network of global equity and energy-sector returns and summarizes its instability through measures of edge turnover, spectral radius, degree entropy and strength dispersion. These components are combined to form the Coupled Multiscale Chaos Index (CMCI), a scalar state variable that distinguishes calm, transitional and chaotic market regimes. Empirical results indicate that Brent and WTI exhibit pronounced multifractality, elevated entropy and positive Lyapunov exponents, while the dependence network becomes more centralized, more clustered and more capable of shock amplification during high-CMCI states. The CMCI moves closely with realized volatility and provides significant predictive content for five-day variance across major global equity benchmarks, with performance superior to models that rely only on macro-financial controls. Out-of-sample evaluation shows that forecasts incorporating measures of complexity record substantially lower MSE and QLIKE losses. The findings indicate that systemic instability reflects the interaction between local chaotic dynamics in crude-oil markets and turbulence in the global dependence network. The CMCI offers a practical early-warning indicator that supports risk management, forecasting and macroprudential supervision. Full article

Global value chains (GVCs) have evolved into highly interconnected and geographically fragmented production networks, increasing exposure to systemic disruptions and revealing the limitations of static input–output and conventional network approaches. This study develops a unified analytical framework for modeling the structure, dynamics, and resilience of GVCs by integrating input–output economics with network theory, control theory, optimal transport, information theory, and cooperative game theory. The framework represents GVCs as time-varying, multi-level networks and formalizes shock propagation through stochastic normalization and state-space dynamics. Entropy-regularized optimal transport is employed to model friction-dependent substitution and supply chain reconfiguration, while Koopman operator methods approximate nonlinear adjustment dynamics. Cooperative flow-based indices are introduced to assess systemic importance and bargaining power. The analysis produces a coherent set of structural and dynamic indicators capturing vulnerability, adaptability, and controllability across country–sector nodes. Overall, the framework provides an empirically applicable toolkit for diagnosing structural fragilities, comparing resilience across economies, and supporting scenario-based evaluation of industrial and trade policies in complex global production networks. Full article