Search Results (16,583)

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

The Gulf of Mexico and the Caribbean Sea are key regions of the western Atlantic, where sea-state conditions are critical for coastal safety and offshore operations. This study analyzes wave climate trends (1981–2022) using WAVEWATCH III simulations validated against buoy observations. The Mann–Kendall test and Theil–Sen estimator were employed to quantify trends in significant wave height (Hs), energy period (Te), and wave power (P), while correlation analysis was performed to explore teleconnections with the Oceanic Niño Index (ONI), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The results reveal basin-wide increases in mean Hs and P, characterized by pronounced spatial and seasonal heterogeneity. The most robust positive trends occur during winter and spring; in summer and fall, the weaker or negative tendencies, particularly in Te, suggest an intensification of seasonal contrasts rather than uniform change. Teleconnection analysis demonstrates that, among the climate indices considered in this study, ENSO is the primary driver of interannual wave variability in the Caribbean, particularly modulating wave power through remotely generated swell. While the NAO exerts regionally dependent control associated with storm-track modulation, the AMO plays a secondary role, affecting swell-dominated sectors. In contrast, the Gulf of Mexico shows limited sensitivity to large-scale climate modes, with wave variability largely governed by local wind–sea processes. These findings highlight the contrasting wave dynamics between these two basins, providing critical insights for coastal hazard assessments, maritime traffic along major shipping routes, oil spill management, and regional wave energy planning. Full article

With a modest estimate of 11 million workers and high greenhouse gas emissions, the Indian brick sector is a relevant study for understanding how low-carbon energy transition impacts justice for the society, environment, and livelihoods. This empirical article provides an analysis of the ongoing policy-driven energy efficiency transition and justice trade-offs and benefits in the brick production sector in the state of Bihar. The transition is explored in a larger framework of power relations and vulnerability to determine whether the policies enable or challenge transformative justice for the labour force, nature and future generations. Present policies focus on regulations and financial incentives relevant for entrepreneurs with pre-existing skills, network and financial resources. Further, present policy narratives lack attention to mechanisms that reproduce the socio-economic inequality of the brick labour force, and implications for balancing different livelihood and environmental objectives. We conclude that the findings emphasise the need for integrating a wider variety of social dimensions and relevant support schemes to overcome inequality barriers and safeguard the environment for future generations. Full article

Reanalysis datasets provide gridded, high-frequency estimates of atmospheric variables that are essential for studying weather and climate, particularly in regions with sparse observational networks. Despite their widespread use, the quality of reanalysis products remains insufficiently validated in tropical regions, particularly in areas with complex terrain. In this study, we evaluate the performance of surface-level temperature and atmospheric pressure fields from ERA5 and ERA5-Land in the state of Alagoas, northeastern Brazil. The analysis is based on a 12-year comparison (2008–2019) with observational data from the National Institute of Meteorology (INMET). Prior to validation, altitude corrections were applied to minimize elevation-induced biases in the reanalysis fields. Performance was assessed using statistical metrics. Both reanalyses showed strong agreement with observations, with average correlations exceeding 0.91 for temperature and pressure. ERA5 temperature biases ranged from −0.2 °C to 0.3 °C, and those for ERA5-Land from −0.6 °C to −0.3 °C, with RMSE around 1.6 °C. Pressure biases were initially larger (−20 hPa to +6 hPa in ERA5), but were reduced to below 0.5 hPa at key reference stations after correction. Diurnal and seasonal cycle analyses confirmed the datasets’ ability to reproduce temporal variability, though both reanalyses tended to overestimate minimum temperatures and underestimate maximum temperatures. Further investigation is needed to identify the origin of anomalous temperature jumps in ERA5’s diurnal cycle, which seem unrelated to the assimilation cycles. Overall, the results highlight the robust performance of ERA5 and ERA5-Land in representing surface atmospheric conditions in tropical coastal regions, while also emphasizing the continued need for regional validation and preprocessing before application in high-resolution or short-term studies. Full article

Accurate turbojet engine performance prediction is crucial for condition monitoring, health management, and safe operation. Conventional component-level models require iterative solutions of strongly nonlinear matching equations and are sensitive to un-modeled effects, limiting accuracy and computational efficiency. Purely data-driven models are efficient but lack explicit physical constraints, resulting in poor interpretability and generalization outside the training domain. To address these issues, this paper proposes a component-decoupled, physically constrained hybrid modeling framework for turbojet engine steady-state performance prediction using on-board measurements. The engine is decomposed into component-level neural sub-models, with physics-guided feature engineering and mutual-information-based feature selection applied to optimize inputs. Component predictions are coupled via aerothermodynamic constraints to reconstruct unmeasured parameters and thrust. Validation on steady-state test data from a 120 kgf class micro turbojet engine shows the model achieves 1.157% maximum relative deviation (MRD) and 0.226% average relative deviation (ARD) for thrust, with MRDs of key gas path parameters within 0.3%. Compared with purely data-driven models, it offers higher accuracy, better generalization, and physically consistent unmeasured parameter estimates, providing a practical approach for engine performance prediction and health management. Full article

With the rapid development of robot technology, the multi-robot cooperation system has been widely used in rescue, monitoring, logistics, and other fields. Aiming at the key problems in multi-robot cooperative localization and target search, considering the search time, search mileage, and search risk, a cooperative localization and search algorithm based on ant colony optimization (ACO-CLS) is proposed based on the analysis of the target weight factor, the sensitivity of the number of robots, the adaptability of robot formation, and the sensitivity of robot speed. Firstly, a multi-sensor fusion localization algorithm based on IMU and UWB sensors is designed, and the error-state Kalman filter (ESKF) is used to achieve high-precision position estimation. Secondly, a dynamic grouping strategy based on weight is proposed to realize intelligent grouping based on target priority and robot position. Then, the ant colony algorithm is introduced to make path decisions, and the robot search is guided by pheromone updates and heuristic information. Finally, an intelligent reallocation mechanism after target discovery is designed to realize the dynamic optimization of resource allocation. The simulation results show that the proposed algorithm is superior to the traditional methods in terms of location accuracy, search efficiency, and system robustness, and has important theoretical value and application prospects. Full article

This paper presents a LiDAR-informed adaptive-cost nonlinear model predictive control (NMPC) strategy for wind turbine pitch regulation. The proposed method uses a reinforcement learning (RL) agent as a supervisory cost-shaping module that adjusts the weights in the NMPC cost function. The pitch command is obtained from the constrained NMPC optimizer, which preserves the physical prediction model, actuator limits, and receding-horizon solution structure. LiDAR-derived preview wind-speed information is used as an estimate of the incoming disturbance and is introduced into both the prediction model and the agent state. This design helps the controller account for wind-field variation over the prediction horizon and adapt the relative emphasis on power regulation, load mitigation, and pitch-action smoothness. Compared with feedforward PID (FF-PID) and fixed-weight feedforward NMPC (FF-NMPC) controllers, the proposed controller shows stronger adaptability under abrupt and stochastic wind variations in OpenFAST-MATLAB/Simulink co-simulations. Full article

Background/Objectives: Sleep disturbance is common among youth in the child welfare system, yet the role of placement instability and placement setting in shaping sleep outcomes remains understudied. This study examined the association between placement instability, time spent in different care settings, and sleep disturbance among children in foster care. Methods: We conducted a retrospective cohort study using longitudinal administrative child welfare data from a Midwestern U.S. state, including 20,888 youth aged 5–18 years who entered foster care between 2010 and 2020. Sleep disturbance was assessed using the Child and Adolescent Needs and Strengths (CANS) sleep item. Baseline was defined as the first CANS assessment within one month of entry into care, and follow-up as the assessment closest to discharge or the end of a three-year observation window, whichever occurred first. We estimated association using a time-lagged linear mixed-effects model predicting sleep disturbance after each placement episode, including placement instability: 1 (reference), 2, 3, or ≥4 placement(s), time since placement, time spent in care settings (kinship, foster home, treatment foster home, congregate care, institutional care), and baseline trait factor scores derived from non-sleep CANS items, while controlling for sleep at the time of placement and demographics. Results: At baseline, 2016 children had actionable sleep disturbance (CANS sleep = 2 or 3; 1701 moderate and 315 severe). By the end of follow-up, this increased to 2884 children (2372 moderate and 512 severe). In linear mixed-effects models, placement instability demonstrated a dose–response association with higher subsequent sleep disturbance relative to one placement (2 placements: β = 0.025; 3 placements: β = 0.045; ≥4 placements: β = 0.067; all p ≤ 0.02). Time spent in kinship care was associated with lower sleep disturbance (β = −0.049; p < 0.001), whereas time spent in treatment foster homes was associated with higher sleep disturbance (β = 0.035; p < 0.001). Trauma in the family, medical/developmental needs, and internalizing/sexual issues were positively associated with sleep disturbance. Time and instability interactions showed modest attenuations of instability-associated sleep disturbance over time for higher placement counts. Conclusions: Placement instability is associated with progressively worse sleep disturbance over time among youth in foster care, even after controlling for sleep status at placement and baseline functioning. Sleep disturbance may represent an actionable indicator for the child welfare system, highlighting opportunities for targeted screening and support during placement transitions. Full article

Severe and unpredictable wind conditions significantly disrupt flight safety, mission planning, and scheduling. Traditional wind forecasting methods rely on low-resolution mesoscale models or resource-intensive instrumentation. This study evaluates the accuracy of 40 m Large-Eddy Simulations (LESs), nested within a mesoscale framework, to better resolve hazardous wind phenomena over GrandSKY, North Dakota, the first large-scale commercial Uncrewed Aircraft System (UAS) test park in the United States, serving as a hub for UAS innovation and Beyond Visual Line of Sight operations. Using low-altitude airborne observations from Meteodrone flights, satellite data, and ground-based measurements, we assess the model’s accuracy in predicting wind speed and direction during both summer and winter. Results demonstrate that the 40 m LES provides improved predictions of wind gust variability compared to the 1 km forecast, and the impact on flight safety is quantified. The LES also reveals notable discrepancies in UAS flyability predictions, which result in up to a 17% reduction in operational windows during the summer. This study’s novelty lies in using a 40 m resolution LES nested within a 1 km WRF simulation, combined with multi-source observations, to resolve low-altitude turbulence and quantify its impact on UAS operations. A 10–18% correction factor can be applied to TKE (or derived wind variability) in coarser WRF runs to better estimate maximum wind speeds without LES. The findings highlight the potential of high-resolution LES modeling to support reliable UAS operations in weather-sensitive environments, laying the groundwork for broader integration of advanced simulation techniques in national airspace management systems. Full article

Four algal adsorbents were prepared from two types of green sea algae (Ulva lactuca and Ulva pertusa), either by treatment with concentrated sulfuric acid or without treatment. A comparative study of Au(III) adsorption in an HCl medium was performed. While both untreated adsorbents showed good performance at low HCl concentrations, the treated adsorbents achieved quantitative adsorption and high selectivity for Au(III) across a broad range of HCl concentrations. The adsorption of Au(III) onto the algal biomass adsorbents followed the typical Langmuir monolayer adsorption model. At an HCl concentration of 0.010 M, the maximum adsorption capacities were 1.14, 0.86, 6.57, and 6.28 mol kg^–1 for DUL, DUP, TUL, and TUP, respectively. A kinetic study conducted at different temperatures was consistent with the pseudo-first-order kinetic model and enabled estimation of the activation energy of the adsorption reaction. Structural changes before and after treatment were analyzed using FT-IR spectroscopy. Confirmation of Au(III) adsorption and its subsequent reduction to the elemental state was achieved through XRD and SEM/EDX analyses as well as digital imaging of the Au-loaded adsorbents. Finally, the adsorbed and reduced Au was successfully desorbed using an acidic thiourea solution. Full article

This paper develops a dynamic game-theoretic model to evaluate market competitiveness in industries characterized by price competition and adjustment stickiness. We extend the dynamic oligopoly framework for estimating market competitiveness in the literature from a quantity-setting to a price-setting context with differentiated goods. By deriving the subgame perfect equilibrium in a linear-quadratic structure, we utilize an index analogous to the price conjectural variation to measure market competitiveness with differentiated goods. The model is applied to the Chinese retail oil market, and we find that the Chinese retail oil market, particularly dominated by two state firms, exhibits characteristics close to a collusive benchmark within the maintained model. The dynamic game model provides a tractable analytical tool for antitrust authorities to monitor strategic coordination in dynamic environments where price transparency or regulation may facilitate tacit coordination of pricing behavior to a high degree. Full article

Reliable anomaly early warning for hydropower station sump wells remains challenging due to the strong nonlinearity of water level dynamics and the limited adaptability of conventional fixed-threshold alarms. Here, we present a hybrid deep learning framework—termed CNN–Attention–LSTM–BO—that fuses multi-scale local feature extraction, adaptive temporal weighting, and sequential dependency modeling within a unified architecture, with all critical hyperparameters tuned via Bayesian optimization. A four-dimensional input representation is first constructed from the raw water level signal and its first- and second-order differences together with the drainage pump operating state, capturing both trend and transient information. One-dimensional convolutions at multiple kernel scales encode short-range fluctuation patterns, a Bahdanau-style temporal attention layer selectively amplifies informative time steps, and a stacked LSTM propagates long-horizon risk dependencies. At the decision stage, a dual dynamic thresholding scheme couples an improved $3\sigma$ criterion with kernel density estimation (KDE) to partition the smoothed risk score into three graded alert levels (normal/warning/critical), replacing the binary alarm paradigm. Experiments on the SWaT benchmark yield an average area under the ROC curve (AUC) of 0.9246, an average Accuracy of 0.8812, and a best single-well false alarm rate (FAR) of 3.21% (Well-4), with an average FAR of 8.97% across three wells, outperforming both traditional limit-value alarms and ablated variants lacking CNN or attention modules. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

Electromechanical actuators play a pivotal role in aerospace servo systems; however, their high-precision tracking performance is frequently compromised by external disturbances and system nonlinearities. To address these challenges, this paper proposes a disturbance observer-based fixed-time backstepping sliding-mode control strategy. Firstly, the high-order dynamics are decomposed into load and electrical subsystems employing a backstepping control framework. To effectively handle mismatched external disturbances in the load subsystem, a prescribed-time integral sliding-mode observer is designed, which guarantees accurate disturbance estimation within a prescribed time for feedforward compensation. Subsequently, a fixed-time sliding-mode controller incorporating a segmented reaching law is developed. This controller ensures that tracking errors converge to zero within a fixed time, independent of initial system states, while mitigating chattering. Hardware-in-the-loop experimental results demonstrate the superior performance of the proposed strategy. Compared to conventional methods, the proposed controller significantly enhances transient response under step disturbances by reducing the peak deviation by up to 94% and shortening the recovery time by at least 60%. Furthermore, under sustained sinusoidal disturbances and dynamic tracking scenarios, the output fluctuations and tracking errors are attenuated to negligible levels, thereby exhibiting notable improvements over traditional methods. Full article

Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements. Full article