Search Results (203)

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average. Full article

Biological measurements that predict injury risk are crucial diagnostic tools. Yet, research on improving diagnostic accuracy in detecting accidents is insufficient. Combining multiple predictors and assessing them via ROC curves can enhance this accuracy. This study aimed to (1) evaluate the importance of lower limb muscle mass asymmetry and body composition (BMI and FMI) as predictors of injuries, (2) explore the role of the most effective body composition index in the relationship between muscle asymmetry and injury, and (3) assess the prognostic potential of combined predictors. Cross-sectional sampling was used to select students from a university. The sample included 237 physically active young adults (44% males). The independent variables were inter-limb muscle mass asymmetry (absolute asymmetry, AA), BMI, and FMI; the dependent variable was the number of injuries in the past year. Using zero-inflated Poisson regression, we examined the relationships, including a moderation analysis (moderated multiple ZIP regression). The mediation by body composition was tested using ZIP and logistic regression. The predictive power was assessed via ROC curves. The significance level was set at an α-value of 0.05. No significant difference in injury incidence between males and females was found (χ² = 2.12, p = 0.145), though the injury types varied. Males had more muscle strains, while females had more bone fractures (χ² = 6.02, p = 0.014). In males, the inter-limb asymmetry and FMI predicted injuries; in females, the BMI and FMI did, but not asymmetry. No moderating or mediating effects of body composition were found. In males, combined asymmetry and the FMI better predicted injuries (AUC = 0.686) than separate predictors (AA: AUC = 0.650, FMI: AUC = 0.458). For females, the FMI was the best predictor (AUC = 0.662). The most predictive factors for injuries in males were both muscle asymmetry and the FMI (as combined predictors), while in females, it was the single FMI. The hypothesis regarding the mediating role of body composition indicators was rejected, as no moderation or mediation by the FMI was detected in the relationship between absolute asymmetry (AA) and injuries. For clinical practice, the findings suggest that practitioners should incorporate assessments of both muscle asymmetry and body composition into routine screenings for physically active individuals. Identifying those with both high asymmetry and an elevated FMI can help target preventative interventions more effectively. Tailored strength training and conditioning programs aimed at reducing asymmetry and managing body composition may reduce the risk of injury, particularly in populations identified as high-risk. Full article

Background: Several studies suggest that environmental and climatic factors are linked to the risk of mortality due to cardiovascular and respiratory diseases; however, it is still unclear which are the most influential ones. This study sheds light on the potentiality of a data-driven statistical approach by providing a case study analysis. Methods: Daily admissions to the emergency room for cardiovascular and respiratory diseases are jointly analyzed with daily environmental and climatic parameter values (temperature, atmospheric pressure, relative humidity, carbon monoxide, ozone, particulate matter, and nitrogen dioxide). The Random Forest (RF) model and feature importance measure (FMI) techniques (permutation feature importance (PFI), Shapley Additive exPlanations (SHAP) feature importance, and the derivative-based importance measure (${\kappa }^{ALE}$)) are applied for discriminating the role of each environmental and climatic parameter. Data are pre-processed to remove trend and seasonal behavior using the Seasonal Trend Decomposition (STL) method and preliminary analyzed to avoid redundancy of information. Results: The RF performance is encouraging, being able to predict cardiovascular and respiratory disease admissions with a mean absolute relative error of 0.04 and 0.05 cases per day, respectively. Feature importance measures discriminate parameter behaviors providing importance rankings. Indeed, only three parameters (temperature, atmospheric pressure, and carbon monoxide) were responsible for most of the total prediction accuracy. Conclusions: Data-driven and statistical tools, like the feature importance measure, are promising for discriminating the role of environmental and climatic factors in predicting the risk related to cardiovascular and respiratory diseases. Our results reveal the potential of employing these tools in public health policy applications for the development of early warning systems that address health risks associated with climate change, and improving disease prevention strategies. Full article

Most past studies focused on the associations of prenatal risk factors with the risks of childhood overweight/obesity. Instead, more postnatal risk factors are modifiable, with less knowledge of their cumulative effects on childhood obesity. We analyzed data of 1869 children in an Australian birth cohort. Key postnatal risk factors included: maternal and paternal overweight/obesity during the child’s infancy, tobacco exposure, low family socioeconomic score, breastfeeding duration < 6 months, early introduction of solid foods, and rapid weight gain during infancy. The risk score was the sum of the number of risk factors. The primary outcome is overweight/obesity in late childhood (11–12 years); secondary outcomes are high-fat mass index (FMI), body fat percentage (BF%), and waist-to-height ratio (WHtR). Poisson regression models were used in the analyses. Children with higher risk scores had higher risks of overweight/obesity (p-for-trends < 0.001). After adjusting covariates, compared with those with 0–1 risk factors, children with 4–6 risk factors had 4.30 (95% confidence interval: 2.98, 6.21) times higher risk of being overweight/obesity; the relative risks for high FMI, BF%, and WHtR were 7.31 (3.97, 13.45), 4.41 (3.00, 6.50), and 6.52 (3.33, 12.74), respectively. Our findings highlighted that multiple postnatal risk factors were associated with increased risks of being overweight/obesity in late childhood. Full article

The fusion of infrared and visible images aims to leverage the strengths of both modalities, thereby generating fused images with enhanced visible perception and discrimination capabilities. However, current image fusion methods frequently treat common features between modalities (modality-commonality) and unique features from each modality (modality-distinctiveness) equally during processing, neglecting their distinct characteristics. Therefore, we propose a DDFNet-A for infrared and visible image fusion. DDFNet-A addresses this limitation by decomposing infrared and visible input images into low-frequency features depicting modality-commonality and high-frequency features representing modality-distinctiveness. The extracted low and high features were then fused using distinct methods. In particular, we propose a hybrid attention block (HAB) to improve high-frequency feature extraction ability and a base feature fusion (BFF) module to enhance low-frequency feature fusion ability. Experiments were conducted on public infrared and visible image fusion datasets MSRS, TNO, and VIFB to validate the performance of the proposed network. DDFNet-A achieved competitive results on three datasets, with EN, MI, VIFF, ${\mathrm{Q}}^{\mathrm{AB}/\mathrm{F}}$, FMI, and ${\mathrm{Q}}_{\mathrm{s}}$ metrics reaching the best performance on the TNO dataset, achieving 7.1217, 2.1620, 0.7739, 0.5426, 0.8129, and 0.9079, respectively. These values are $2.06\%$, $11.95\%$, $21.04\%$, $21.52\%$, $1.04\%$, and $0.09\%$ higher than those of the second-best methods, respectively. The experimental results confirm that our DDFNet-A achieves better fusion performance than state-of-the-art (SOTA) methods. Full article

The characteristics of South American biomass burning (BB) aerosols transported over northern and central Argentina were investigated from July to December 2019. This period was chosen due to the high aerosol optical depth values found in the region and because simultaneously intensive biomass burning took place over the Amazon. More specifically, a combination of remote sensing observations with simulated air parcel back trajectories was used to link the optical and physical properties of three BB aerosol events that affected Pilar Observatory (PO, Argentina, 31°41′S, 63°53′W, 338 m above sea level), with low-level atmospheric circulation patterns and with types of vegetation burned in specific fire regions. The lidar observations at the PO site were used for the first time to characterize the vertical extent and structure of BB aerosol plumes as well as their connection with the planetary boundary layer, and dust particles. Based mainly on the air-parcel trajectories, a local transport regime and a long transport regime were identified. We found that in all the BB aerosol event cases studied in this paper, light-absorbing fine-mode aerosols were detected, resulting mainly from a mixture of aging smoke and dust particles. In the remote transport regime, the main sources of the BB aerosols reaching PO were associated with Amazonian rainforest wildfires. These aerosols were transported into northern and central Argentina within a strong low-level jet circulation. During the local transport regime, the BB aerosols were linked with closer fires related to tropical forests, cropland, grassland, and scrub/shrubland vegetation types in southeastern South America. Moreover, aerosols carried by the remote transport regime were associated with a high aerosol loading and enhanced aging and relatively smaller particle sizes, while aerosols associated with the local transport pattern were consistently less affected by the aging effect and showed larger sizes and low aerosol loading. Full article

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m² are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration. Full article

To assess the correlation between vitamin D status and body composition variables in adult women of childbearing age, a cross-sectional study was conducted involving women aged 20–49 years. The participants were categorized based on their vitamin D status and further divided according to body mass index (BMI). Anthropometric and biochemical data were collected to compute body composition indices, specifically body fat and muscle mass. The sample included 124 women, with 63.70% exhibiting vitamin D inadequacy. Women with inadequate vitamin D status demonstrated a higher waist-to-height ratio (WHtR) and body adiposity index (BAI), along with a lower BMI-adjusted muscle mass index (SMI _BMI), compared to those with adequate levels of vitamin D (p = 0.021; p = 0.019; and p = 0.039, respectively). A positive correlation was observed between circulating concentrations of 25(OH)D and SMI _BMI, while a negative correlation existed between circulating concentrations of 25(OH)D and waist circumference (WC), WHtR, conicity index (CI), fat mass index (FMI), body fat percentage (% BF), and fat-to-muscle ratio (FMR). These findings suggest that inadequate vitamin D status may impact muscle tissue and contribute to higher body adiposity, including visceral adiposity. It is recommended that these variables be incorporated into clinical practice, with a particular emphasis on WHtR and SMI _BMI, to mitigate potential metabolic consequences associated with vitamin D inadequacy. Full article

Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information. Full article

Currently, innovations in mechatronic products often occur at the system level, requiring consideration of component interactions throughout the entire development process. In the earlier phases of development, this is accomplished by coupling virtual prototypes such as simulation models. As the development progresses and real prototypes of certain system components become available, real-virtual prototypes (RVPs) are established with the help of network communication. However, network effects—all of which can be interpreted as latencies in simplified terms—distort the system behavior of RVPs. To reduce these distortions, we propose a coupling method for RVPs that compensates for latencies. We present an easily applicable approach by introducing a generic coupling algorithm based on error space extrapolation. Furthermore, we enable online learning by transforming coupling algorithms into feedforward neural networks. Additionally, we conduct a frequency domain analysis to assess the impact of coupling faults and algorithms on the system behavior of RVPs and derive a method for optimally designing coupling algorithms. To demonstrate the effectiveness of the coupling method, we apply it to a hybrid vehicle that is productively used as an RVP in the industry. We show that the optimally designed and trained coupling algorithm significantly improves the credibility of the RVP. Full article

NAFLD has become the leading cause of chronic liver disease in children, as a direct consequence of the high prevalence of childhood obesity. This study aimed to characterize body composition trajectories from childhood to adolescence and their association with the risk of developing nonalcoholic fatty liver disease (NAFLD) during adolescence. The participants were part of the ‘Chilean Growth and Obesity Cohort Study’, comprising 784 children who were followed prospectively from age 3 years. Annual assessments of nutritional status and body composition were conducted, with ultrasound screening for NAFLD during adolescence revealing a 9.8% prevalence. Higher waist circumference measures were associated with NAFLD from age 3 years (p = 0.03), all skin folds from age 4 years (p < 0.01), and DXA body fat measurements from age 12 years (p = 0.01). The fat-free mass index was higher in females (p = 0.006) but not in males (p = 0.211). The second and third tertiles of the fat mass index (FMI) had odds ratios for NAFLD during adolescence of 2.19 (1.48–3.25, 95% CI) and 6.94 (4.79–10.04, 95% CI), respectively. Elevated waist circumference, skin folds, and total body fat were identified as risk factors for future NAFLD development. A higher FMI during childhood was associated with an increased risk of NAFLD during adolescence. Full article

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation. Full article

Forest therapy is associated with several health advantages, such as stress reduction and improved psychological health. Mindfulness, an important component of forest therapy, is also associated with improved health outcomes. However, few studies have empirically evaluated mindfulness in forest therapy settings. This study translated the Freiburg Mindfulness Inventory (FMI) in the context of forest therapy into Korean and then validated it. (1) Methods: This study included 352 individuals. Four other psychometric tools were administered to ensure criterion validity. Exploratory and confirmatory factor analyses were implemented to determine the factor structure. Furthermore, item validity was assessed using item response theory. (2) Findings: A two-factor structure of the FMI, comprising acceptance and presence, was the most suitable. However, excluding item 13 enhanced the model fit (χ² [df] = 169.9 [64], comparative fit index = 0.93, Tucker-Lewis index = 0.92, root mean square error of approximation = 0.069). The FMI had satisfactory psychometric properties. (3) Conclusion: The FMI was translated into Korean and validated, serving as a valuable instrument for assessing mindfulness in the context of forest therapy. We identified that item 13 should be excluded. Our results demonstrate the potential effects of mindfulness on mental health in forest therapy. Full article

Background: Cardiac cachexia (CC) in chronic heart failure with reduced ejection fraction (HFrEF) is characterized by catabolism and inflammation predicting poor prognosis. Levels of responsible transcription factors like signal transducer and activator of transcription (STAT)1, STAT3, suppressor of cytokine signaling (SOCS)1 and SOCS3 in peripheral blood cells (PBC) are underinvestigated in CC. Expression of mediators was related to patients’ functional status, body composition (BC) and metabolic gene expression in skeletal muscle (SM). Methods: Gene expression was quantified by qRT-PCR in three cohorts: non-cachectic patients (ncCHF, n = 19, LVEF 31 ± 7%, BMI 30.2 ± 5.0 kg/m²), cachectic patients (cCHF; n = 18, LVEF 27 ± 7%, BMI 24.3 ± 2.5 kg/m²) and controls (n = 17, LVEF 70 ± 7%, BMI 27.6 ± 4.6 kg/m²). BC was assessed by dual-energy X-ray absorptiometry. Blood inflammatory markers were measured. We quantified solute carrier family 2 member 4 (SLC2A4) and protein degradation by expressions of proteasome 20S subunit beta 2 and calpain-1 catalytic subunit in SM biopsies. Results: TNF and IL-10 expression was higher in cCHF than in ncCHF and controls (all p < 0.004). cCHF had a lower fat mass index (FMI) and lower fat-free mass index (FFMI) compared to ncCHF and controls (p < 0.05). STAT1 and STAT3 expression was higher in cCHF vs. ncCHF or controls (1.1 [1.6] vs. 0.8 [0.9] vs. 0.9 [1.1] RU and 4.6 [5.5] vs. 2.5 [4.8] vs. 3.0 [4.2] RU, all ANOVA-p < 0.05). The same applied for SOCS1 and SOCS3 expression (1.1 [1.5] vs. 0.4 [0.4] vs. 0.4 [0.5] and 0.9 [3.3] vs. 0.4 [1.1] vs. 0.8 [0.9] RU, all ANOVA-p < 0.04). In cCHF, higher TNF and STAT1 expression was associated with lower FMI (r = 0.5, p = 0.053 and p < 0.05) but not with lower FFMI (p > 0.4). In ncCHF, neither cytokine nor STAT/SOCS expression was associated with BC (all p > 0.3). SLC2A4 was upregulated in SM of cCHF vs. ncCHF (p < 0.03). Conclusions: Increased STAT1, STAT3, SOCS1 and SOCS3 expression suggests their involvement in CC. In cCHF, higher TNF and STAT-1 expression in PBC were associated with lower FMI. Increased SLC2A4 in cachectic SM biopsies indicates altered glucose metabolism. Full article

Renewable energy sources, distributed generation, multi-energy carriers, distributed storage, and low-temperature district heating systems, among others, are demanding a change in the way thermal networks are conceived, understood, and operated. Governments around the world are moving to increase the renewable share in energy distribution networks through legislation like the European Directive 2012/27 in Europe, and solar energy integration into district heating systems is arising as an interesting option to reduce operation costs and carbon footprint. This conveys an important investment that adds complexity to the management of thermal networks and often delays the return on investment due to the unpredictability of renewable energy sources, like solar radiation. To this end, this paper presents an optimisation methodology to aid in the operative control of an existing solar district heating system located in the northwest of France. The modelling of the system, which includes a large-scale solar field, a biomass boiler, a gas boiler, and thermal energy storage, was previously built in Dymola. The optimisation of this network was performed using MATLAB’s genetic algorithm (GA) and running the Dymola model as functional mock-up units, FMUs, using Simulink’s FMI Kit. The results show that the methodology presented here can reduce the current operation costs and improve the use of the daily storage of the DH system by a combination of mass flow control and the implementation of a flexibility function for the end-users. The cost-per-kWh was reduced by as much as 16% in a single day, and the share of heat supplied by the solar field on this day was increased by 5.22%. Full article