Search Results (39)

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article

Time-varying digital filters are widely used in dynamic signal processing applications, but their transient response can significantly impact performance, particularly in real-time systems. This study focuses on reducing transient time in time-varying filters through second-order section (SOS) optimization. By employing a numerical optimization approach, we selectively adjust the coefficients of a single SOS within a higher-order filter to minimize the transient period while maintaining overall stability. Using a sequential quadratic programming (SQP) algorithm, we determine a time-varying coefficient trajectory over a finite horizon, ensuring a rapid convergence to steady-state behavior. Experimental results demonstrate that this targeted coefficient adaptation reduces transient time by up to 80% compared to conventional static designs, with minimal computational overhead. Additionally, a comparative analysis with traditional linear time-invariant (LTI) filters highlights the advantage of this method in suppressing transient oscillations while preserving long-term filter characteristics. The proposed approach provides a practical and efficient strategy for enhancing filter responsiveness in applications requiring both stability and real-time adaptability. These findings suggest that selective time variation in SOS decomposition can be a valuable tool in digital filter design, improving efficiency without excessive memory or processing demands. Full article

Accurate and adaptive bus dispatching is vital for medium-sized urban centers, where static schedules often fail to accommodate fluctuating passenger demand. In this work, we propose a dynamic heuristic that integrates machine learning-based demand forecasts into a discrete-time planning horizon, thereby enabling real-time adjustments to dispatch decisions. Additionally, we introduce a tailored mathematical model—grounded in mixed-integer linear programming and space-time flows—that serves as a benchmark to evaluate our heuristic’s performance under the operational constraints typical of traditional public transportation systems in Colombian mid-sized cities. A key contribution of this research lies in combining predictive modeling (using Prophet for passenger demand) with operational optimization, ensuring that dispatch frequencies adapt promptly to varying ridership levels. We validated our approach using a real-world case study in Montería (Colombia), covering eight representative routes over a full day (5:00–21:00). Numerical experiments show that: 1. Our heuristic matches or surpasses 95% of the optimal solution’s operational utility on most routes, with an average gap of 4.7%, relative to the benchmark mathematical model. 2. It maintains high service levels—above 90% demand coverage on demanding corridors—and robust bus utilization, without incurring excessive operating costs. 3. It reduces computation times by up to 98% compared to the optimization model, making it practically viable for daily scheduling where solving large-scale models exactly can be prohibitively time-consuming. Overall, these results underscore the heuristic’s practical effectiveness in boosting profitability, optimizing resource use, and rapidly adapting to demand fluctuations. The proposed framework thus serves as a scalable and implementable tool for transportation operators seeking data-driven dispatch solutions that balance operational efficiency and service quality. Full article

This paper introduces a novel weighted fusion methodology for grid-level short-term load forecasting that addresses the critical limitations of direct aggregation methods currently used by regional dispatch centers. Traditional approaches accumulate provincial forecasts without considering regional heterogeneity in load characteristics, data quality, and forecasting capabilities. Our methodology implements a comprehensive evaluation index system that quantifies forecast trustworthiness through three key dimensions: forecast reliability, provincial impact, and forecasting complexity. The core innovation lies in our principal component analysis (PCA)-based weighted aggregation mechanism that dynamically adjusts provincial weights according to their evaluated reliability, further enhancing through time-varying weights that adapt to changing load patterns throughout the day. Experimental validation across three representative seasonal periods (moderate temperature, high temperature, and winter conditions) substantiates that our weighted fusion approach consistently outperforms direct aggregation, achieving a 24.67% improvement in overall MAPE (from 3.09% to 2.33%). Performance gains are particularly significant during critical peak periods, with up to 62.6% error reduction under high-temperature conditions. The methodology verifies remarkable adaptability across different temporal scales, seasonal variations, and regional characteristics, consistently maintaining superior performance from ultra-short-term (1 h) to medium-term (168 h) forecasting horizons. Analysis of provincial weight dynamics reveals intelligent redistribution of weights across seasons, with summer months characterized by Jiangsu dominance (0.30–0.35) shifting to increased Anhui contribution (0.30–0.35) during winter. Our approach provides grid dispatch centers with a computationally efficient solution for enhancing the integration of heterogeneous forecasts from diverse regions, leveraging the complementary strengths of individual provincial systems while supporting safer and more economical power system operations without requiring modifications to existing forecasting infrastructure. Full article

Flexible custom manufacturing is becoming increasingly important, and, in the near future, it will serve as a key method to counter growing competition and meet market demands across most industrial sectors. This situation necessitates the substantial reorganization of companies’ material and information flows, as traditional planning approaches focused on serial production and longer time horizons are gradually losing their effectiveness. An integrated digital twin system that unifies production and logistics planning is emerging as a promising solution. The proposed approach entails implementing a digital twin directly within custom manufacturing, enabling the continuous monitoring and real-time adjustment of production plans based on instant data from sensors and information systems. The system architecture is designed around multiple modules responsible for data collection and processing, scheduling, simulation, statistical analysis, and effective communication between the system and its users. By leveraging these components, the solution can flexibly adapt to any deviations or changes as they occur. Within the scope of this research, attention is devoted not only to the handling of dynamic and random data but also to the prioritization of individual orders. Equally emphasized is the role of intelligent communication tools, which promptly inform us about shifts in the production process and allow for rapid plan modifications to ensure the highest possible levels of efficiency and reliability. Full article

This study proposes a novel approach to enhance the stability and performance of reinforcement learning (RL) in long-horizon tasks. Overestimation bias in value function estimation and high uncertainty within environments make it difficult to determine the optimal action. To address this, we improve the truncated quantile critics algorithm by managing uncertainty in robotic applications. Our dynamic method adjusts the discount factor based on policy entropy, allowing for fine-tuning that reflects the agent’s learning status. This enables the existing algorithm to learn stably even in scenarios with limited training data, ensuring more robust adaptation. By leveraging policy entropy loss, this approach effectively boosts confidence in predicting future rewards. Our experiments demonstrated an 11% increase in average evaluation return compared to traditional fixed-discount-factor approaches in the DeepMind Control Suite and Gymnasium robotics environments. This approach significantly enhances sample efficiency and adaptability in complex long-horizon tasks, highlighting the effectiveness of entropy-guided RL in navigating challenging and uncertain environments. Full article

Multi-Agent Systems (MAS) are widely deployed in smart factory environments, where efficient task assignment and path planning for agents can greatly enhance production efficiency. Existing algorithms usually ignore resource constraints, overly simplify the geometric shape of agents, and perform poorly in large-scale scenarios. In this paper, we propose a Multi-Factor Task Assignment and Adaptive Window Enhanced Conflict-Based Search (MTA-AWECBS) algorithm to solve these problems, which considers the resource constraints and volume of agents, improving the algorithm’s scalability and adaptability. In task assignment, a novel scheme is designed by considering distance cost, maximum travel distances, and maximum number of executable tasks. In path planning, we first propose a new mathematical description of global traffic congestion level. Based on this, an adaptive window is proposed to dynamically adjust the time horizon in the WECBS algorithm, improving search efficiency and solving the deadlock issue. Additionally, based on experimental observations, two optimization strategies are proposed to further improve operation efficiency. The experimental results show that MTA-AWECBS outperforms Token Passing (TP), Token Passing with Task Swaps (TPTSs), and Conflict-Based Steiner Search (CBSS) in handling a large number of tasks and agents, achieving an average $39\%$ reduction in timestep cost and an average $22\%$ reduction in total path cost. Full article

This study presents an adaptive sliding mode predictive control (ASMPC) algorithm intended to improve the control precision and robustness of path tracking for wheeled agricultural vehicles. Firstly, the kinematics state equations of the vehicle were established based on path tracking errors. Secondly, in order to design the path tracking controller by combining the precision advantage of model predictive control (MPC) algorithm with the robustness advantage of sliding mode control (SMC) algorithm, the sliding mode functions were designed and used as the output equations to establish the kinematics state space model of the vehicle. Thirdly, on the basis of linearization and discretization for the kinematics state space model, the control law of path tracking was obtained using the MPC algorithm. Finally, according to the fuzzy rules designed by the working speed of the vehicle and the curvature of the reference path, the prediction horizon and control horizon of the MPC algorithm were adaptively adjusted to further improve the control precision and robustness of the path tracking system. The results of CarSim and MATLAB/Simulink co-simulation show that the proposed ASMPC algorithm is superior to the traditional SMC algorithm and conventional MPC algorithm in terms of control precision, dynamic performance, and robustness. The results of our field test show that the root mean square (RMS) values of the lateral errors for straight path tracking and curve path tracking do not exceed 2.1 and 8.7 cm, respectively, in the speed range of 1.0 to 3.5 m/s, suitable for field working. The control precision and robustness of the proposed ASMPC algorithm can meet the working requirements of wheeled agricultural vehicles. Full article

To reduce hydrogen consumption by hydrogen fuel cell vehicles (HFCVs), an adaptive power-following control strategy based on gated recurrent unit (GRU) neural network operating condition recognition was proposed. The future vehicle speed was predicted based on a GRU neural network and a driving cycle condition recognition model was established based on k-means cluster analysis. By predicting the speed over a specific time horizon, feature parameters were extracted and compared with those of typical operating conditions to determine the categories of the parameters, thus the adjustment of the power-following control strategy was realized. The simulation results indicate that the proposed control strategy reduces hydrogen consumption by hydrogen fuel cell vehicles (HFCVs) by 16.6% with the CLTC-P driving cycle and by 4.7% with the NEDC driving cycle, compared to the conventional power-following control strategy. Additionally, the proposed strategy effectively stabilizes the battery’s state of charge (SOC). Full article

To achieve accurate predictions of tail strike events during the landing phase of flight training, we propose a stacking ensemble learning prediction model that uses Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Adaptive Boosting (AdaBoost) as base models, with Logistic Regression (LR) serving as the meta-model. This model is built on non-exceedance flight data recorded on airborne SD cards. By evaluating the importance scores of the feature parameters influencing tail strike events, we identified the optimal set of features for model input while using the landing pitch angle as the model output. We then compared the R² and RMSE of each model. The results indicate that under a prediction horizon of 5 s prior to landing, the ensemble learning model demonstrates high predictive accuracy. This capability provides flight trainees with sufficient reaction time to adjust their flight attitudes, thereby helping to avoid the occurrence of tail strike events during landing. Full article

Accurate prediction of the Sharpe ratio, a key metric for risk-adjusted returns in financial markets, remains a significant challenge due to the complex and stochastic nature of stock price movements. This paper introduces a novel deep learning model, the Temporal Fusion Transformer with Adaptive Sharpe Ratio Optimization (TFT-ASRO), designed to address this challenge. The model incorporates real-time market sensor data and financial indicators as input signals, leveraging multiple data streams including price sensors, volume sensors, and market sentiment sensors to capture the complete market state. Using a comprehensive dataset of US historical stock prices and earnings data, we demonstrate that TFT-ASRO outperforms traditional methods and existing deep learning models in predicting Sharpe ratios across various time horizons. The model’s multi-task learning framework, which simultaneously predicts returns and volatility, provides a more nuanced understanding of risk-adjusted performance. Furthermore, our adaptive optimization approach effectively balances the trade-off between return maximization and risk minimization, leading to more robust predictions. Empirical results show that TFT-ASRO achieves a $18\%$ improvement in Sharpe ratio prediction accuracy compared to state-of-the-art baselines, with particularly strong performance in volatile market conditions. The model also demonstrates superior uncertainty quantification, providing reliable confidence intervals for its predictions. These findings have significant implications for portfolio management and investment strategy optimization, offering a powerful tool for financial decision-makers in the era of data-driven investing. Full article

There is a growing focus on sustainability, characterized by making changes that anticipate future needs and adapting them to present requirements. Sustainability is reflected in various areas of materials science as well. Thus, more research is focused on the fabrication of advanced materials based on earth-abundant metals. The role of iron and its alloys is particularly significant as iron is the second most abundant metal on our planet. Additionally, the electrochemical method offers an environmentally friendly approach for synthesizing multifunctional alloys. Thus, iron can be successfully codeposited with a targeted metal from complexing electrolytes, opening a large horizon for a smart tuning of properties and enabling various applications. In this review, we discuss the practical aspects of the electrodeposition of iron-based alloys from complexing electrolytes, with a focus on refractory metals as multifunctional materials having magnetic, catalytic, mechanical, and antimicrobial/antibacterial properties with advanced thermal, wear, and corrosion resistance. Peculiarities of electrodeposition from complexing electrolytes are practically significant as they can greatly influence the final structure, composition, and designed properties by adjusting the electroactive complexes in the solution. Moreover, these alloys can be further upgraded into composites, multi-layered, hybrid/recovered materials, or high-entropy alloys. Full article

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics. Full article

Background/Objectives: To evaluate the health benefits, costs, and cost-effectiveness of vaccination with bivalent respiratory syncytial virus stabilized prefusion F vaccine (RSVpreF) for the prevention of lower respiratory tract disease caused by respiratory syncytial virus (RSV) in Greek adults 60 years of age and older. Methods: A Markov model was adapted to simulate lifetime risk of health and economic outcomes from the public payer’s perspective over a lifetime horizon. Epidemiology, vaccine effectiveness, utilities, and direct medical costs (EUR, 2024) were obtained from published studies, official sources, and local experts. Model outcomes included the number of medically attended RSV cases, stratified by care setting (i.e., hospital, emergency department [ED], outpatient visits [OV]), and attributable RSV-related deaths, costs, life years (LY), quality-adjusted life-years (QALY), and incremental cost-effectiveness ratios (ICERs) of RSVpreF vaccination compared with no vaccination. Results: The model projected 258,170 hospitalizations, 112,248 ED encounters, 1,201,604 OV, and 25,463 deaths related to RSV in Greek older adults resulting in direct medical costs of EUR 1.6 billion over the lifetime horizon. Assuming RSV vaccination would reach the same coverage rates as pneumococcal and influenza programmes, 18,118 hospitalizations, 7874 ED encounters, 48,079 OV, and 1706 deaths could be prevented over the modelled time horizon. The health benefits associated with RSVpreF contributed to an incremental gain of 10,976 LYs and 7230 QALYs compared with no vaccination. The incremental analysis reported that vaccination with RSVpreF was estimated to be a cost-effective strategy resulting in ICERs of EUR 12,991 per LY gained, EUR 19,723 per QALY gained, and EUR 7870 per hospitalized RSV case avoided compared with no vaccination. Conclusions: Vaccination with RSVpreF was a cost-effective strategy for the prevention of RSV disease in Greek adults over 60 years of age. The introduction of RSV vaccination can improve public health by averting RSV cases and deaths and has the potential to fulfil an unmet medical need. Full article

Lotus pedunculatus (lotus) and Lupinus polyphyllus (Russell lupin) persist in the upland grasslands of New Zealand, where soil acidity and associated aluminium (Al) toxicity impede conventional pasture legumes. This experiment investigated the response of lotus and Russell lupin to soil acidity and Al. The species were sown in 20 cm tall 1.2 L pots of acidic upland soil. A mass of 4.5 or 6.7 g lime (CaCO₃)/L was added to either the top or bottom or both soil horizons (0–9 cm and 9–18 cm), resulting in six treatments across six randomised blocks in a glasshouse. The soil pH was 4.4, 4.9, and 5.4; the exchangeable Al concentrations were 24, 2.5, and 1.5 mg/kg for 0, 4.5, and 6.7 g lime/L. At 16 weeks post-sowing, the plants were divided into shoots and roots at 0–9 cm and 9–18 cm. Root morphology, shoot and root dry matter (DM), shoot nitrogen (N), and nodulation were measured. The total plant DM and shoot-to-root DM ratio were higher, and the shoot %N was lower for the lotus plants than the Russell lupin plants for the various lime rates (13.2 vs. 2.9 g plant⁻¹, 5.6 vs. 1.6, and 2.4 vs. 3.3%, p < 0.05). No response to lime in terms of total DM or total root morphology parameters was exhibited in either species (p > 0.05). Root morphology adjustments in response to acidity between soil horizons were not observed. The results indicated that lotus and Russell lupin are tolerant to high soil acidity (pH 4.4–5.4) and exchangeable Al (1.5–24 mg kg⁻¹), highlighting their considerable adaptation to grasslands with acidic soils. Full article