Search Results (1,063)

This paper presents an energy-focused analysis of structural materials used in passenger cars, with a particular emphasis on the impact of construction materials on total energy consumption throughout the vehicle’s life cycle. Three production periods (2000, 2010, and 2020) were analysed for B- and C-segment vehicles using inventory data from Life Cycle Assessment databases, the scientific literature, and certified dismantling stations. The embodied energy of key material groups—steel, aluminium, plastics, and other materials—was calculated based on representative mass shares and material-specific energy intensity indicators. The computational model was supplemented with statistical analyses (Kolmogorov–Smirnov test, Levene’s test, ANOVA, and Tukey’s post hoc tests) to verify whether observed temporal trends were statistically significant. The results indicate that total material-related energy inputs increased from approximately 57 GJ to 64 GJ per vehicle, while the specific energy intensity per kilogram decreased from 47.6 MJ/kg to 42.6 MJ/kg. Aluminium exhibited a pronounced reduction in unit energy intensity due to the rising share of secondary materials, whereas plastics and other materials showed substantial increases. Steel remained the largest contributor in absolute terms because of its dominant mass share. This study highlights the growing importance of the production phase in the environmental balance of modern vehicles, particularly in the context of the rising share of lightweight materials and recycling-based components. The results emphasise the importance of energy-efficient material use and underscore the significance of material selection and recycling strategies in reducing energy demand within the automotive sector. Full article

The article discusses the control of nonlinear processes with first-order dominant dynamics, focusing on implementation using modern hardware available in various programmable devices and embedded systems. The first two approaches rely on linearization with an ultra-local process model, considering small changes of the process input and output around a fixed operating point, which can be adjusted through gain scheduling with the setpoint variable. This model is used to configure either the historically established automatic reset controller (ARC) or a stabilizing proportional (P) controller enhanced by an inversion-based disturbance observer (DOB). This solution can be interpreted as an application of modern control theory (MCT), as DOB-based control (DOBC) or as advanced disturbance rejection control (ADRC). Alternatively, they can be viewed as a special case of automatic offset control (AOC) based on two types of linear process models. In the third design method, setpoint tracking by exact linearization (EL) is extended with a nonlinear DOB designed using the inverse of the nonlinear process dynamics (EEL). The fourth approach augments EL-based tracking with a DOB derived from the transfer functions of nonlinear processes (NTF). An illustrative example involving the control of a liquid reservoir with a variable cross-section clarifies motivation for the definition of (linear) local and ultra-local process models as well as their advantages in designing robust control that accounts for process uncertainties. Thus, the speed, homogeneity, and shape of transient responses, the ability to reconstruct disturbances, control signal saturation, and measurement noise attenuation are evaluated according to the assumptions specified in the controller design. The novelty of the paper lies in presenting a unifying perspective on several seemingly different control options under the impact of measurement noise. By explaining their essence, advantages, and disadvantages, it provides a foundation for controlling more complex time-delayed systems. The paper emphasizes that certain aspects of controller design, often overlooked in traditional linearization procedures, can significantly improve closed-loop properties. Full article

To avoid predators, benthic fish will stir up the sediment on the seabed by flapping their pectoral fins, thus burying themselves. This self-burial concealment strategy can offer bionic enlightenment for the benthic residence method of Unmanned Underwater Vehicles (UUVs). In this paper, based on the observation results of the self-burial behavior of benthic fish, a two-dimensional fluid-particle numerical model was developed to simulate the processes of sediment transport induced by pectoral fin flapping. In addition, an orthogonal experimental design was employed to analyze the effects of body length, flapping amplitude, flapping number, flapping frequency, and particle size on burial ratio, input power, and burial efficiency. The results reveal that rapid pectoral fin flapping enables benthic fish to fluidize sediments and achieve self-burial. Among the influencing factors, body size has the most significant impact on coverage ratio and input power, as larger fish generate stronger tip vortices and fluid disturbances, making local flow velocities more likely to exceed the critical starting velocity. In contrast, particle size has the weakest effect on burial performance, while kinematic parameters exert a far greater impact on self-burial than environmental parameters. The research results can offer references for the biomimetic design of self-burying UUVs. Full article

Over the last decade, bark beetle outbreaks have significantly impacted forests in Central Europe, causing extensive loss of forest cover. We evaluated the impact of partial deforestation in three mountain forest catchments in the Jeseníky Mountains, comparing them with the unaffected Červík catchment (Beskydy Mountains) and the severely affected Pekelský stream catchment (Czech-Moravian Highlands). Atmospheric deposition in the catchments was similar, with total element input driven primarily by precipitation volumes rather than ion concentrations. We did not observe the hypothesized increase in DOC and nitrogen export, although nitrate outflow was slightly higher than atmospheric input in two cases. Significant export of calcium, magnesium, and bicarbonates was driven mainly by the geology of the individual catchments. The limited impact of bark beetle outbreaks on DOC dynamics can be attributed to the relatively low proportion of clear-cut areas and the rapid development of ground vegetation on impacted sites. Full article

This study investigates groundwater levels (GWLs) in the alluvial aquifer of the Sava River valley, located in the north-western part of Croatia. It provides the first quantitative assessment of groundwater levels using machine learning in this part of Europe. Groundwater levels from 1998 to 2017 were predicted using support vector regression (SVR). The input variables were initially monthly data on two basic elements that influence groundwater dynamics (precipitation and the Sava River levels). Later, GWLs from the previous month (GWL-1) were added as an additional predictor. Results demonstrated that the SVR model effectively predicts groundwater levels. Introducing GWL-1 reduced RMSE and MAE values by more than 47% and 46%, respectively, while increasing the R² value by over 36%. The improvement was more pronounced farther from the Sava River, since GWLs near the river are more directly influenced by river stage fluctuations, diminishing the impact of GWL-1. Compared to the existing regional numerical model (NM), the SVR model outperformed the NM with improvements of approximately 12% to 76% across performance indicators. Our findings suggest that the SVR model provides a reliable method for predicting groundwater levels at specific observation wells, making it a valuable tool for applications such as forecasting groundwater availability for farmers during dry periods and flood risk assessment during periods of heavy rainfall. Full article

Heavy metal (HM) pollution in the southern Tibetan Plateau has attracted global attention. Prior studies have noted HM enrichment and water issues in Tibetan rivers, but seasonal variation, sources, and controlling factors remain unclear. This study measured HM levels in high-frequency river water and suspended particulate matter (SPM) at the Lhaze on the Yarlung Tsangpo River (YTR), assessing pollution and ecological risks. The results showed that the overall surface water quality was excellent. The SPM overall showed a low potential ecological risk. Nevertheless, pollution risks were observed for As and B in river water samples during the dry season. Additionally, As and B were found to be in moderate-to-heavy pollution levels for SPM samples, and there was a moderate potential ecological risk for As during the dry season. The source identification results revealed geothermal spring input as the primary factor contributing to the ecological risks of As and B in the YTR water. While rock weathering dominates the origins of Al, Mn, and Fe in river water, with contributions ranging from 64% to 90% of their total amounts, water availability during weathering reactions in the dry and wet seasons serves as the primary control factor for their release, mobility in the YTR basin, and concentration in the river water. As an erosion product, SPM exhibited no significant seasonal changes in metal element concentrations and showed a moderate correlation with water discharge, indicating a stable HM ecological impact from the erosion process in the YTR basin. Full article

Background: Patients after anterior cruciate ligament reconstruction (ACLR) often exhibit persistent biomechanical deficits, particularly during high-demand tasks like the single-leg drop jump (SLDJ). At approximately six months post-ACLR, patients frequently rely on visual input to compensate for persistent sensorimotor deficits during dynamic tasks, which may lead to altered movement patterns. While visual perturbations have been studied in bilateral jump tasks, their impact on SLDJ biomechanics in ACLR patients remains unexplored. Methods: Patients who were still engaged in rehabilitation and not yet cleared for unrestricted return to sport performed SLDJ under three visual conditions: normal vision, low visual perturbation, and high visual perturbation using stroboscopic glasses. Kinematic and kinetic variables were measured using a 3-dimensional motion analysis system and force platform. Comparisons were made between the ACLR and non-operated limbs, as well as across visual conditions. Results: 24 patients (17 males, 7 females; mean age 25.6 ± 6.3 years, mean height 174 ± 9.0 cm, mean weight 74.7 ± 17.2 kg) were included in the analysis. Knee adduction excursion during landing was significantly affected by visual perturbation (F(2, 46) = 6.55, p = 0.004, η² = 0.019). Post hoc analysis showed that high visual perturbation significantly decreased knee adduction excursion compared to normal vision on the ACLR limb (mean difference 1.499°, SE = 0.388, pBonf = 0.003, Cohen’s d = 0.542). A significant difference was also found between low and high visual perturbation on the ACLR limb (mean difference 1.543°, SE = 0.388, pBonf = 0.002, Cohen’s d = 0.558). No significant changes were observed in the non-operated limb across visual conditions. Conclusions: High visual perturbation significantly altered knee adduction excursion on the ACLR limb, resulting in a shift toward greater knee abduction during landing. No changes were observed in the non-operated limb. These findings support the use of visual perturbation in functional assessment protocols after ACLR to better identify persistent biomechanical deficits that may contribute to reinjury risk. Full article

Tropical forests are critical regulators of global water and energy cycles, with evapotranspiration ($ET$) being a key ecohydrological process. However, monitoring $ET$ over tropical forests is a challenge due to their complex structure, and the logistical difficulties in obtaining observations that are both spatially representative and have wide coverage. Remote sensing data offer an alternative to these limitations, although the effectiveness of $ET$ remote sensing-based models over these areas is not well-known. Thus, this study evaluates the performance of four remote sensing-based $ET$ models (SSEBop, geeSEBAL, PT-JPL and T-SEB) in tropical forests. We compared models’ estimations against flux tower observations and assessed the uncertainty in models’ outputs driven by different meteorological input forcings. Additionally, we conducted a spatial–temporal analysis of models’ response to the impact of deforestation on $ET$ patterns. Our results showed a good agreement between modeled and observed $ET$ using the most accurate meteorological input dataset (RMSEs ranging from 1.1 to 1.3 mm.day⁻¹ for ERA5-Land). The deforestation analysis for sites in Africa, America and Asia revealed an agreement of the models in demonstrating the impact of deforestation on $ET$, though performance varied due to different deforestation patterns. For the long-term results, models showed different responses to forest removal, highlighting the uncertainties of the individual models and underscoring the necessity of multi-model approaches in providing more accurate information. These findings demonstrate that current high-resolution remote sensing models can effectively monitor $ET$ in tropical forests on a global scale, especially for assessing the impacts of deforestation in data-scarce regions. Full article

Climate-induced flooding is a major issue throughout the globe, resulting in damage to infrastructure, loss of life, and the economy. Therefore, there is an urgent need for sustainable flood risk management. This paper assesses the effectiveness of the hybrid defense system using advanced artificial intelligence (AI) techniques. A data series of energy dissipation (ΔE), flow conditions, roughness, and vegetation density was collected from literature and laboratory experiments. Out of the selected 136 data points, 80 points were collected from literature and 56 from a laboratory experiment. Advanced AI models like Random Forest (RF), Extreme Boosting Gradient (XGBoost) with Particle Swarm Optimization (PSO), Support Vector Regression (SVR) with PSO, and artificial neural network (ANN) with PSO were trained on the collected data series for predicting floodwater energy dissipation. The predictive capability of each model was evaluated through performance indicators, including the coefficient of determination (R²) and root mean square error (RMSE). Further, the relationship between input and output parameters was evaluated using a correlation heatmap, scatter pair plot, and HEC-contour maps. The results demonstrated the superior performance of the Random Forest (RF) model, with a high coefficient of determination (R² = 0.96) and a low RMSE of 3.03 during training. This superiority was further supported by statistical analyses, where ANOVA and t-tests confirmed the significant performance differences among the models, and Taylor’s diagram showed closer agreement between RF predictions and observed energy dissipation. Further, scatter pair plot and HEC-contour maps also supported the result of SHAP analysis, demonstrating greater impact of the roughness condition followed by vegetation density in reducing floodwater energy dissipation under diverse flow conditions. The findings of this study concluded that RF has the capability of modeling flood risk management, indicating the role of AI models in combination with a hybrid defense system for enhanced flood risk management. Full article

River-training structures such as spur dikes are frequently used in the field of river engineering, which play a critical role in flow regulation and stabilization of the riverbank. However, previous studies lack a precise prediction of factors inducing scour and turbulence phenomena, such as tip velocity, for optimal design of the spur dikes. This study addresses a key gap in previous research by predicting tip velocity around spur dikes using advanced and interpretable machine learning models while simultaneously evaluating the influence of key geometric and hydraulic parameters. For this purpose, the current study utilized advanced artificial intelligence (AI) techniques like Gaussian Process Regression (GPR), Categorical Boosting (CatBoost), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), optimized with Particle Swarm Optimization (PSO), to predict tip velocity in the vicinity of the spur dike. In this paper, a small dataset of 69 laboratory-scale experimental trials was collected; therefore, the chosen AI models were selected for their ability to handle such limited data points. In this study, the input parameters included Froude number (Fr), separation length to spur dike length ratio (L/l), and incidence angle (β), while the output parameter was tip velocity. The selected four AI models were trained on 70%, 15%, and 15% of the data for the training, testing, and validation phases, respectively. SHapley Additive exPlanations (SHAP) analysis was used to observe the influence of the critical parameters on the tip velocity. The results demonstrated the superior performance of GPR, followed by the CatBoost model, compared to other models. GPR and CatBoost show greater values of coefficient of determination (R²) (GPR R² = 0.972 and CatBoost R² = 0.970) and lower values of root mean square error (RMSE) (GPR RMSE = 0.0107 and CatBoost RMSE = 0.0236). The result of the heatmap and SHAP analysis indicated a greater influence of Fr and L/l and a lower impact of β on the tip velocity. The results of this study recommend the utilization of GPR and CatBoost for precise and robust performance of the hydrodynamic phenomenon around the spur dikes, supporting scour mitigation strategies in river engineering. Full article

The accuracy and timeliness of precipitation inputs have significant impact on flood forecasting. Upstream Minjiang River Basin is characterized by complex terrain and highly variable climatic conditions, posing a significant challenge for runoff forecasting. This study proposes a combined forecasting approach integrating numerical weather prediction (NWP) models with hydrodynamic models to enhance flood process simulation. The most appropriate initial field data for the Weather Research and Forecasting Model (WRF) exist in time and space resolution. Compared with the measured series, the characteristics of precipitation forecasting are summarized from practical and scientific perspectives. InfoWorks ICM is then used to implement runoff generation calculations and flooding processes. The results indicate that the WRF model effectively simulates the spatial distribution and peak timing of precipitation in the upper Minjiang River. The model systematically underestimates both peak rainfall intensity and cumulative precipitation compared to observations. Initial field data with 0.25° spatial resolution and 3 h temporal intervals demonstrate good performance and the 10–14 h forecast period exhibits superior predictive capability in numerical simulations. Updates to elevation and land use conditions yield increased cumulative rainfall estimates, though simulated peaks remain lower than measured values. The runoff results could indicate peak flow but rely on the precipitation inputs. Full article

One of the main factors influencing wheat productivity is nitrogen (N) management. This study examined the impact of varying N-fertilizer rates on spring wheat yield and N use efficiency by adjusting the “source-sink” relationship between assimilates and N accumulation and transport. The objective was to identify the optimal N rate for the region. The field experiment included five N-fertilizer rates: 0 kg ha⁻¹ (N1), 52.5 kg ha⁻¹ (N2), 105.0 kg ha⁻¹ (N3), 157.5 kg ha⁻¹ (N4), and 210.0 kg ha⁻¹ (N5). Results indicated that the yield response was not proportional to N-fertilizer rates, with maximum biomass (6029 kg ha⁻¹) and grain yield (2625 kg ha⁻¹) achieved under N3. N fertilization primarily increased yield by regulating pre-anthesis translocation of assimilate and N. Assimilate translocation peaked at 105 kg N ha⁻¹, increasing by 8.5–133.7% compared to other treatments. With increasing N input, N absorption efficiency and N partial factor productivity declined. The highest N agronomic use efficiency was observed under N3, which was 19.5–176.34% higher than other treatments. Overall, moderate N input (≈105 kg ha⁻¹) optimizes yield and N-use efficiency, offering guidance for sustainable N management in dryland spring wheat production. Full article

Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz region, with observations spanning from 2006 to 2019. These five products were selected because they offer the finest spatial resolution (around 1 km or less) among freely downloadable global ET datasets, making them well-suited for comparison with local EC flux tower data. The study area was chosen for its reliable ground-truth EC stations, extensive knowledge of local irrigation practices, and a semi-arid climate that provides a rigorous testbed for ET model evaluation in water-limited conditions. Precipitation observations were included to assess each product’s sensitivity to soil moisture and precipitation-driven ET variations, particularly to identify which models respond to rainfall and irrigation inputs (i.e., differences between rainfed and irrigated fields). Results indicate that PMLv2 achieved the best agreement with EC (R² up to 0.65, RMSE as low as 0.4 mm/day, and PBIAS under 10% at most sites), followed by WaPOR and SSEBop which captured seasonal ET patterns (R² ~0.3–0.5) with moderate bias (~20–30%). In contrast, ETMonitor and MOD16 underperformed, showing larger errors (RMSE ~1–2.5 mm/day) and substantial underestimation biases (e.g., MOD16 PBIAS ~50–80% in irrigated sites). These findings underscore the impact of algorithmic differences and highlight PMLv2, SSEBop, and WaPOR as more reliable options for estimating ET in semi-arid agricultural regions lacking in situ measurements. Full article

The effect of Ni content on the improvement of low-temperature impact toughness and microstructure refinement in a simulated coarse-grained heat-affected zone (CGHAZ) of high-strength steel was studied. The impact toughness tests revealed that as the heat input increased from 20 to 50 kJ/cm, both low-nickel (L-Ni) steel and high-nickel (H-Ni) steel exhibited a rapid decline in the impact toughness of their coarse-grained heat-affected zones (CGHAZ), though the H-Ni steel consistently demonstrated significantly higher impact toughness than the L-Ni steel. Microstructural characterization showed that the microstructure of L-Ni steel gradually transitioned from lath bainite (LB) to granular bainite (GB) with increasing heat input, which accounted for its reduced impact toughness. Conversely, H-Ni steel underwent a phase transformation from lath martensite (LM) to LB with increasing heat input, showing an unexpected trend opposite to the conventional understanding of toughness enhancement. Notably, the martensitic structure obtained in H-Ni steel at 20 kJ/cm exhibited substantially higher impact energy (59.6 J) than both the LB structures of L-Ni steel (44.6 J) and those of H-Ni steel (37.8 J) observed at 20 and 50 kJ/cm heat inputs. This phenomenon is attributed to the increased Ni content significantly refining the packet of LM, thereby enhancing its resistance to brittle crack propagation. Although LB structures obtained under different conditions exhibited refined blocks, their parallel arrangement within coarse packets resulted in less effective obstruction of brittle crack propagation compared to the refined packet with interlocking arrangement. Full article

Extreme heat disrupts labour, infrastructure, and health systems, yet most response frameworks intervene after clinical impact is confirmed. This review analyzes documented cases across sectors and regions to determine where heat effects are first detected and why intervention timing varies. The analysis used institutional reports, epidemiological summaries and occupational data to map how early functional signals appear across systems. A conceptual matrix is proposed to permit action to be authorized at the earliest sign of functional stress, using mortality, productivity, service instability, vulnerability, and adaptive capacity as operational inputs rather than retrospective outcomes. The analysis suggests that heat becomes observable first through reduced work capacity or infrastructure strain, not through hospital data, and that systems with predefined activation criteria engage earlier and with less irreversible loss. The matrix provides a transferable basis for integrating occupational, infrastructural, and clinical information into a unified heat response mechanism. This approach supports a transition from post-impact validation to forward-based decision logic, particularly in settings where vulnerable workers remain outside formal surveillance. Full article