Search Results (446)

Climate change introduces severe non-stationarity and high-frequency noise into hydro-meteorological data. This noise degrades the predictive accuracy of traditional data-driven streamflow models. We propose a physics-inspired data enhancement framework coupling the CEEMDAN-based Hilbert-Huang Transform (HHT) with Stochastic Resonance (SR). We applied this framework to the Lanzhou section of the upper Yellow River. HHT isolates the dominant characteristic frequency of the basin’s streamflow system at 0.0026 cycles/day. Using this frequency as a target, we constructed a Bayesian-optimized SR system. The system converts the energy of high-frequency meteorological noise into low-frequency periodic components, facilitating frequency alignment between the meteorological inputs and the hydrological response. We evaluated the SR-enhanced meteorological inputs across three machine learning architectures: Random Forest, XGBoost, and LSTM. All algorithms demonstrated an improved performance. The SR-LSTM model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.91 ± 0.03. This represents a 19% improvement over the baseline LSTM score of 0.79 ± 0.02. The SR-LSTM demonstrated robust accuracy during extreme hydrological events; it achieved a high-flow NSE of 0.89 and effectively mitigated the common peak-underestimation issue by constraining relative peak magnitude errors to approximately −5.08%. Overall, this study presents a practical data enhancement approach for streamflow forecasting under complex climatic conditions. Full article

This comprehensive review investigates hybrid propulsion technologies as a pathway to decarbonization and improved energy efficiency in the maritime sector. Through a review of the recent literature, this study synthesizes current knowledge on energy management strategies and capacity sizing approaches for hybrid ship propulsion systems. Reported results indicate that optimized energy management can reduce fuel consumption and greenhouse gas emissions while minimizing total operational costs. Among real-time strategies, the Equivalent Consumption Minimization Strategy emerges as particularly suitable for maritime use due to its low computational demand and independence from full voyage profile knowledge, yet its maritime application remains far less developed than in the automotive domain. Capacity sizing and energy management are usually treated as separate optimization problems, limiting the achievability of truly optimal solutions. Only a few studies adopt integrated co-optimization frameworks, and these are typically built around simplified or fixed operational profiles. Moreover, the coupling between energy management parameters, such as the ECMS equivalence factor, and hardware sizing remains insufficiently explored. To address this, the review contributes a ship-specific classification of energy management strategies, a consolidated treatment of battery sizing methods with explicit attention to degradation, and a generalized two-loop framework that couples component sizing with ECMS-based energy management. The findings suggest that future research should prioritize adaptive energy management formulations calibrated for stochastic maritime duty cycles, the incorporation of battery degradation models into co-optimization, and validation against stochastic, real-world operating conditions. Full article

Agricultural production systems are frequently affected by uncertainty, processing cycles, quality variability, and irreversible losses, leading to discrepancies between planned and actually available operational resources. This study proposes an integrated framework combining absorbing Markov chains and bankruptcy allocation theory to address endogenous scarcity in stochastic production systems. The production process is modeled using an absorbing Markov chain, where transient states represent operational stages and absorbing states represent successful completion or irreversible loss. The fundamental matrix is used to estimate the effective expected resource availability induced by the stochastic dynamics of the system. When this availability is insufficient to satisfy nominal operational requirements, the problem is reformulated as a bankruptcy allocation problem. Four classical allocation rules are evaluated and compared: proportional, constrained equal awards, constrained equal losses, and the Talmud rule. A stylized cocoa production system illustrates the proposed framework. The results show that the constrained equal awards and Talmud rules better preserve operational continuity under scarcity conditions. The main contribution lies in linking stochastic production modeling with normative resource allocation under endogenous scarcity. Full article

The phoD gene encodes alkaline phosphatase, which hydrolyzes organic phosphorus and releases bioavailable phosphorus for direct utilization by marine organisms. phoD-harboring bacteria are reported to be sensitive to environmental changes. As a common ecological disturbance, annual Ulva prolifera green tides in the southern Yellow Sea pose significant ecological challenges, yet the responses and assembly processes of phoD-harboring bacterial communities remain poorly understood. In this study, high-throughput sequencing was used to characterize these communities across the pre-bloom, bloom and post-bloom stages. The results revealed significant stage-specific shifts in community structure, with the bloom and post-bloom stages exhibiting higher similarity to each other than the pre-bloom stage. Abundant taxa were more sensitive to environmental fluctuations across all stages and were characterized by broader niche breadths but reduced phylogenetic diversity during the bloom. In contrast, rare taxa maintained relatively stable diversity but showed marked niche contraction. Neutral community model and βNTI analyses demonstrated that stochastic processes dominated community assembly overall. Green tide drove rare taxa toward heterogeneous selection and drift, while abundant taxa shifted toward homogeneous selection during the post-bloom stage. Co-occurrence network analysis showed increased microbial correlations during the bloom, implying a trend toward greater network stability of phoD-harboring bacterial communities under green tide disturbance. The lagged responses, functional redundancy and divergent ecological strategies of abundant and rare taxa may explain how green tides drive variations in microbes involved in the phosphorus cycle. These findings provide new insights into the microbial regulatory mechanisms of the nutrient cycle in coastal ecosystems affected by large-scale U. prolifera green tides. Full article

Stock index price series are composed of superimposed multi-frequency components, including long-term trends, cyclical fluctuations, and stochastic noise. Effectively decoupling these heterogeneous components and modeling them separately is key to improving forecasting accuracy. Existing methods under the “decomposition–prediction” paradigm mostly employ fixed-scale decomposition, and the forecasting models are not specifically adapted to the non-stationary and high-noise characteristics of financial data, resulting in limitations in adaptivity and local dynamic capture. This paper proposes a frequency-aware adaptive multi-scale decomposition Transformer hybrid model (FAMS-Transformer). At the decomposition level, the fast Fourier transform is used to dynamically identify dominant cycles, thereby adaptively decoupling trends and fluctuations, overcoming the limitations of fixed-scale decomposition. At the forecasting level, a lightweight depthwise separable convolution is embedded between the self-attention and feedforward network of the Transformer encoder, enhancing the model’s ability to capture local temporal dynamics and achieving collaborative modeling of global dependencies and local information. Comparative experiments with 15 baseline models including LSTM, Transformer, TimesNet, and FreTS on three representative Chinese market indices—Shanghai Composite Index, Shenzhen Component Index, and Small and Medium Enterprises 100 Index—across four prediction horizons from one step to 15 steps demonstrate that FAMS-Transformer achieves the best forecasting accuracy in all scenarios. The coefficient of determination for 15-step prediction remains stably between 0.730 and 0.928. Moreover, the model still performs well on the S & P 500 dataset. Ablation studies and significance tests further validate the effectiveness of each core module and the statistical significance of the performance improvements. Full article

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m³/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me²⁺). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (r_s = 0.87) and pH (r_s = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste. Full article

Bacterial communities play vital roles in coastal biogeochemical cycling and ecological stability. Despite their importance, a significant knowledge gap exists regarding their spatiotemporal dynamics and assembly mechanisms in the tropical coastal waters of the Leizhou Peninsula, China. To investigate the bacterial community structure, co-occurrence networks, and assembly processes, we conducted 16S rRNA gene amplicon sequencing on water samples collected seasonally from August 2022 to June 2023. The bacterial communities were dominated by Proteobacteria and Cyanobacteria, and were characterized by a distinct warm-season peak in the relative of Cyanobium. Alpha diversity indices exhibited significant seasonal fluctuations, reaching a minimum in August (autumn) and a maximum in December (winter). These variations were strongly regulated by water temperature and phosphate availability. Redundancy analysis (RDA) identified salinity as the primary deterministic factor shaping community composition. Seasonal environmental heterogeneity, rather than spatial variation, primarily governed bacterial community dynamics. We also observed a seasonal succession in community assembly mechanisms with deterministic filtering dominated in autumn, whereas stochastic processes prevailed in other seasons. Predicted functional profiles indicated a stable core metabolism, although local anthropogenic inputs stimulated specific metabolic adaptations in industrial and aquaculture zones. Our findings reveal that seasonal environmental filtering (especially temperature and salinity) and a shifting balance between stochastic and deterministic assembly processes govern bacterial dynamics in this tropical coastal ecosystem, with anthropogenic inputs modulating local metabolic functions. This study provides fundamental insights into the mechanisms maintaining microbial diversity and stability in tropical coastal waters facing seasonal and human pressures. Full article

This study set out to investigate the interactions between business cycles, financial cycles and monetary policy in South Africa. Explicitly, the study aims to examine the role of financial factors in business cycle models and the possibility of a unified macroeconomic framework in South Africa. Further, the study assesses the effects of demand shocks, supply shocks, interest rate shocks, and financial shocks on macroeconomic fluctuations. The study applied an analytical approach integrating the Generalised Method of Moments and System Generalised Method of Moments with a Structural New Keynesian Dynamic Stochastic General Equilibrium framework. Accordingly, it was concluded that the financial cycle plays a significant role in business cycle models and is a main driver of macroeconomic fluctuations in South Africa. Further, a unified macroeconomic framework for monetary policy analysis that links the financial system to the real economy in South Africa possibly exists. This study contributes to the South African Reserve Bank’s efforts by deepening understanding of the interactions between the financial system and the real economy and their implications for monetary policy in South Africa. By comparing the standard Taylor rule with a finance-augmented Taylor rule in a DSGE framework, the study helps answer the question of whether financial stability should be adopted as a second objective of monetary policy. Full article

Lead sulfide quantum dots (PbS QDs), as a functional-layer coating, enable non-volatile integration and neuromorphic computing in memristive structures to address the von Neumann bottleneck. Herein, the dual-interface mechanism of PbS QDs in the memristor film structure is reviewed. First, the local electric field enhancement effect generates tip electrode-like structures in the coating film through QD-mediated spatial charge gradients, thereby enabling precise control over the nucleation and growth of conductive filaments (CFs). As a result, the consistency of switching voltages and the thermal stability at elevated temperatures are significantly improved. Conversely, the anion reservoir effect exploits surface dangling bonds on QDs to efficiently capture anions from the dielectric layer, thereby synergistically regulating vacancy migration kinetics. This process enables zero-initialization behavior and ultra-low-power operation. In addition, the spatial distribution design and density modulation of QDs further reinforce both mechanisms. The structural optimization of QD/dielectric interface engineering can simultaneously improve cycling endurance and resistive switching uniformity. Furthermore, modification of QD surface chemistry through ligand decoration and passivation suppresses the stochasticity of ionic diffusion while improving the linearity of synaptic weight updates. This interfacial engineering strategy utilizing QDs as coating films advances the development of high-performance photonic–electronic systems for memory–computing convergence. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

Supply chain control requires balancing operational performance and financial efficiency when decisions are made using delayed and imperfect demand information. Although fixed heuristics, adaptive policies, and reinforcement learning approaches have been proposed, their relative effectiveness and robustness under temporary informational mismatch remain unclear. This study addresses this gap by developing an integrated simulation–reinforcement learning framework that jointly captures operational and financial dynamics in supply chains, which enables adaptive optimisation of working capital policies under uncertainty. A unified simulation framework is developed for a multi-echelon supply chain that jointly models service levels, backlog, customer retention, and working capital exposure through the cash conversion cycle. Five classes of controllers are evaluated: fixed-threshold heuristics, adaptive threshold policies optimised using stochastic and evolutionary search, and a reinforcement learning controller based on proximal policy optimisation. Performance is assessed under stationary demand and under demand disruptions. The results reveal a clear hierarchy of performance. Fixed heuristics provide transparent and stable baselines but suffer from structural rigidity. Adaptive threshold policies substantially improve coordination, with evolutionary search yielding the strongest performance among structured approaches. The reinforcement learning controller achieves the best overall outcomes by learning a nonlinear state–action mapping that sharply reduces backlog and service shortfalls while maintaining comparable working capital exposure. These gains arise from improved coordination across operational and financial decisions rather than single-metric optimisation. Practically, adaptive heuristics offer robust baselines, while learning-based controllers are most valuable in more volatile environments. Full article

Truck-to-excavator assignment is a time-critical control problem in open-pit earthmoving systems (mines, quarries, and large cut-and-fill construction sites) where stochastic travel and service times, changing queues, and equipment outages continually alter the best dispatch decision. A deep reinforcement learning (DRL) dispatch policy is developed and trained using a discrete-event simulation (DES) digital twin of the Sungun copper mine haulage system. The dispatch task is formulated as a Markov decision process using state features that represent fleet locations, excavator and dump queues, and short-term congestion conditions. The resulting deep artificial neural network (DANN) policy is tuned via systematic hyperparameter optimisation and evaluated against a priority-based rule-of-thumb dispatch baseline under long-horizon operating tracks. Results show that the final trained policy improves the average production rate per truck cycle by approximately 17% while reducing avoidable waiting and maintaining stable performance over extended operation, with inference fast enough for real-time dispatch use. Model fidelity is supported by close agreement between simulated and observed daily completed-cycle counts. Robustness is assessed through controlled truck load-capacity perturbations, and scalability is examined through fleet-size sensitivity, which reveals diminishing returns as additional trucks are added under a fixed excavation–haulage configuration. Practical deployment considerations and implications for construction earthmoving logistics are discussed. Full article

In the digital economy era, computing power, as a novel factor of production, serves as a vital engine for driving high-quality economic development. Building upon China’s traditional 42-sector input–output table, this paper incorporates computing power networks as a new sector to construct a 43-sector dynamic input–output (IO) model. Based on this framework, a Dynamic Stochastic General Equilibrium (DSGE) analysis framework is constructed to systematically reveal the dynamic transmission mechanism of computing power within industrial linkages and capital accumulation. From an energy perspective, energy consumption is implicitly captured through carbon emissions and energy structure, which together reflect the scale, efficiency, and composition of energy use in computing power networks. The findings show that the optimal computing power allocation follows a temporal evolution pattern from the service sector to the manufacturing sector, with ICT manufacturing’s computing power quota reaching 31% by 2030. An investment inflection point occurs in 2026, aligning with the digital infrastructure cycle of China’s 14th Five-Year Plan. The “Eastern Data, Western Computing” strategy reduces unit carbon emissions from computing power by 41%. Policy simulations demonstrate that R&D tax credits generate a 2.9-fold multiplier effect through industrial linkages, boosting GDP by 2.3%. The integrated IO-DSGE framework developed in this study provides a quantitative tool for the full-cycle management of “construction–application–regulation” in computing power networks. It holds significant theoretical value and practical implications for enhancing resource allocation efficiency and promoting green, climate-friendly development. Full article

Cycling coaches frequently use training zones and zone distributions based around time in zone (TIZ) to analyse training data and to understand race demands. This study compared the intensity distribution of highly trained cyclists during training and racing using a novel work-in-zone (WIZ) method alongside the traditional TIZ approach. Twelve cyclists recorded their power output during 25 weeks of training and a 7-day stage race. Intensity zones were defined using a three-zone peak-power model, and intensity distribution (TIZ or WIZ) was analysed with compositional data analysis. There were significant main effects for context (training vs. racing) and zone type (TIZ vs. WIZ) on the ILR coordinates. ILR-1, which reflects the balance between Z1 and higher-intensity zones, was higher in training than racing and in TIZ compared with WIZ (p < 0.0001), indicating a relatively greater proportion of Z1 in these conditions. ILR-2, representing the balance within the higher-intensity zones, was significantly lower during racing and higher in TIZ compared with WIZ (p < 0.0001). These findings indicate the cyclists’ training distribution differed substantially from the demands of racing, and that TIZ and WIZ can provide meaningfully different interpretations of intensity distribution. Where TIZ reflects only the time distribution spent within each zone, WIZ incorporates the weighting of intensity. This leads to particularly different results in racing, where intensity is more stochastic and characterised by greater extremes. Combining both methods may enhance understanding of training intensity distribution, race demands, and the difference between these contexts in endurance cyclists. Consequently, WIZ should be used in a complementary manner rather than as a replacement for TIZ. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article