Search Results (369)

Information on the relationship between facial thermal responses and emotional state is valuable for sensing emotion. Yet, previous research has typically relied on linear methods of analysis based on regions of interest (ROIs), which may overlook nonlinear pixel-wise information across the face. To address this limitation, we investigated the use of machine learning (ML) for pixel-level analysis of facial thermal images to estimate dynamic emotional arousal ratings. We collected facial thermal data from 20 participants who viewed five emotion-eliciting films and assessed their dynamic emotional self-reports. Our ML models, including random forest regression, support vector regression, ResNet-18, and ResNet-34, consistently demonstrated superior estimation performance compared to traditional simple or multiple linear regression models for the ROIs. To interpret the nonlinear relationships between facial temperature changes and arousal, saliency maps and integrated gradients were used for the ResNet-34 model. The results show nonlinear associations of arousal ratings in nose = tip, forehead, and cheek temperature changes. These findings imply that ML-based analysis of facial thermal images can estimate emotional arousal more effectively, pointing to potential applications of non-invasive emotion sensing for mental health, education, and human–computer interaction. Full article

In the field of structural engineering, accurate identification of modal damping ratio is the key to structural dynamic response analysis. In order to accurately identify the modal damping ratio of the structure, this study proposes a method to identify the first-order modal damping ratio of the structure by analyzing the free attenuation response of the acceleration signal. By intercepting the free attenuation section from the structural dynamic response output, the amplitude is extracted, and the logarithmic estimation slope of the amplitude is fitted by the least square method to establish a theoretical model for identifying the first-order modal damping ratio. The results show that the method has high accuracy and good stability when the modal damping ratio is in the range of 0.00500~0.06400, and different nodes have little effect on the accuracy of identification. When the modal damping ratio is in the range of 0.06400~0.07000, the accuracy of the method is relatively low and the stability is relatively poor, but it is still within the acceptable range. When the damping ratio is greater than 0.07000 or less than 0.00500, the accuracy may be reduced. In order to further verify the effectiveness of the method, it is applied to the damping identification of a steel arch bridge project. The dynamic response of the bridge under random excitation and El Centro seismic wave excitation is analyzed by using the recommended value and identification value of the first-order damping ratio. The results show that the method can accurately and reliably identify the first-order modal damping ratio, which is significantly different from the empirical modal damping ratio. The identified modal damping ratio can more accurately describe the dynamic response of the structure after long-term use, while the recommended value is not applicable. This method can be applied to the modal damping ratio identification of other structural types, which reflects that the modal damping ratio identification method proposed in this study has certain engineering significance. It is worth noting that the accuracy of identification will be reduced when the modal damping ratio is less than 0.00500 or more than 0.07000, and it may not even be applicable if the modal damping ratio is too small or too large. This method has higher requirements for acceleration signals. In engineering, it may be affected by noise and other factors, resulting in reduced identification accuracy. In practical engineering, it is necessary to improve the identification accuracy of first-order modal damping ratio by changing the interception point of the free attenuation section of the acceleration signal and the screening of the amplitude. Full article

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones. Full article

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind speed, and wind direction—collected from topographically similar sites in Alor Setar (tropical) and Vercelli, Williams, and Ashburton (temperate) between 2012 and 2015. Logistic regression and random forest models were used to predict rainfall occurrence as a binary outcome. Key variables were identified using Wald’s statistics and p-values in the logistic regression models, while the random forest models relied on mean decrease accuracy for ranking variable importance. The results reveal that rainfall in temperate climates is significantly more predictable than in tropical regions, with the Williams model demonstrating the highest accuracy. Atmospheric pressure consistently emerged as the dominant predictor in temperate regions but was not significant in the tropical model, reflecting the greater atmospheric variability and complexity in tropical rainfall mechanisms. Crucially, the study highlights that as global warming continues to alter temperate climate patterns—bringing increased variability and more convective rainfall—these regions may experience the same predictive uncertainties currently observed in tropical climates. These findings underscore the urgency of developing robust, climate-specific rainfall prediction models that account for changing atmospheric dynamics, with critical implications for weather forecasting, disaster preparedness, and climate resilience planning. Full article

Accurate side road detection is essential for traffic management, urban planning, and vehicle navigation. However, existing research mainly focuses on road network construction, lane extraction, and intersection identification, while fine-grained side road detection remains underexplored. Therefore, this study proposes a road segment-level side road detection method based on crowdsourced trajectory data: First, considering the geometric and dynamic characteristics of trajectories, SRDet introduces a trajectory lane-change pattern recognition method based on mutation intervals to distinguish the heterogeneity of lane-change behaviors between main and side roads. Secondly, combining geometric features with spatial statistical theory, SRDet constructs multimodal features for trajectories and road segments, and proposes a potential side road segment classification model based on random forests to achieve precise detection of side road segments. Finally, based on mutation intervals and potential side road segments, SRDet utilizes density peak clustering to identify main and side road access points, completing the fitting of side roads. Experiments were conducted using 2021 Beijing trajectory data. The results show that SRDet achieves precision and recall rates of 84.6% and 86.8%, respectively. This demonstrates the superior performance of SRDet in side road detection across different areas, providing support for the precise updating of urban road navigation information. Full article

Effective postharvest cooling of olive fruit is increasingly critical under rising harvest temperatures driven by climate change. This study models passive cooling dynamics using a trait-based, mixed-effects statistical framework. Ten olive groups—representing seven cultivars and different ripening or size stages—were subjected to controlled cooling conditions. Surface temperature was recorded using infrared thermal imaging, and morphological and compositional traits were quantified. Temperature decay was modeled using Newton’s Law of Cooling, extended with a quadratic time term to capture nonlinear trajse thectories. A linear mixed-effects model was fitted to log-transformed, normalized temperature data, incorporating trait-by-time interactions and hierarchical random effects. The results confirmed that fruit weight, specific surface area (SSA), and specific heat capacity (SHC) are key drivers of cooling rate variability, consistent with theoretical expectations, but quantified here using a trait-based statistical model applied to olive fruit. The quadratic model consistently outperformed standard exponential models, revealing dynamic effects of traits on temperature decline. Residual variation at the group level pointed to additional unmeasured structural influences. This study demonstrates that olive fruit cooling behavior can be effectively predicted using interpretable, trait-dependent models. The findings offer a quantitative basis for optimizing postharvest cooling protocols and are particularly relevant for maintaining quality under high-temperature harvest conditions. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

This paper presents a comprehensive model for detecting and addressing concept drift in network security data using the Isolation Forest algorithm. The approach leverages Isolation Forest’s inherent ability to efficiently isolate anomalies in high-dimensional data, making it suitable for adapting to shifting data distributions in dynamic environments.Anomalies in network attack data may not occur in large numbers, so it is important to be able to detect anomalies even with small batch sizes. The novelty of this work lies in successfully detecting anomalies even with small batch sizes and identifying the point at which incremental retraining needs to be started. Triggering retraining early also keeps the model in sync with the latest data, reducing the chance for attacks to be successfully conducted. Our methodology implements an end-to-end workflow that continuously monitors incoming data and detects distribution changes using Isolation Forest, then manages model retraining using Random Forest to maintain optimal performance. We evaluate our approach using UWF-ZeekDataFall22, a newly created dataset that analyzes Zeek’s Connection Logs collected through Security Onion 2 network security monitor and labeled using the MITRE ATT&CK framework. Incremental as well as full retraining are analyzed using Random Forest. There was a steady increase in the model’s performance with incremental retraining and a positive impact on the model’s performance with full model retraining. Full article

Although treeline ecotones are significant components of vulnerable mountain ecosystems and key indicators of climate change, treelines of the Southern Hemisphere remain largely outside of research focus. In this study, we investigate, for the first time, the current and future distribution of the treeline species Nothofagus pumilio in the Southern Andes using a Species Distribution Modelling approach. The lack of modelling studies in this region can be contributed to missing occurrence data for the species. In a preliminary study, both point and raster data were generated using a novel Instagram ground truthing approach and remote sensing. Here we tested the performance of the two datasets: a typical binary species dataset consisting of occurrence points and pseudo-absence points and a continuous dataset where species occurrence was determined by supervised classification. We used a Random Forest (RF) classification and a RF regression approach. RF is applicable for both datasets, has a very good performance, handles multicollinearity and remains largely interpretable. We used bioclimatic variables from CHELSA as predictors. The two models differ in terms of variable importance and spatial prediction. While a temperature variable is the most important variable in the RF classification, the RF regression model was mainly modelled by precipitation variables. Heat deficiency is the most important limiting factor for tree growth at treelines. It is evident, however, that water availability and drought stress will play an increasingly important role for the future competitiveness of treeline species and their distribution. Modelling with binary presence–absence point data in the RF classification model led to an overprediction of the potential distribution of the species in summit regions and in glacier areas, while the RF regression model, trained with continuous raster data, led to a spatial prediction with small-scale details. The time-consuming and costly acquisition of complex species information should be accepted in order to provide better predictions and insights into the potential current and future distribution of a species. Full article

Background/Objectives: Daily caloric restriction (DCR) is a common dietary weight loss strategy, but leads to metabolic and behavioral adaptations, including maladaptive eating behaviors and dysregulated appetite. Intermittent fasting (IMF) may mitigate these effects by offering diet flexibility during energy restriction. This secondary analysis compared changes in eating behaviors and appetite-related hormones between 4:3 intermittent fasting (4:3 IMF) and DCR and examined their association with weight loss over 12 months. Methods: Adults with overweight or obesity were randomized to 4:3 IMF or DCR for 12 months. Both randomized groups received a matched targeted weekly dietary energy deficit (34%), comprehensive group-based behavioral support, and a prescription to increase moderate-intensity aerobic activity to 300 min/week. Eating behaviors were assessed using validated questionnaires at baseline and months 3, 6, and 12. Fasting levels of leptin, ghrelin, peptide YY, brain-derived neurotrophic factor, and adiponectin were measured at baseline and months 6 and 12. Linear mixed models and Pearson correlations were used to evaluate outcomes. Results: Included in this analysis were 165 adults (mean ± SD; age 42 ± 9 years, BMI 34.2 ± 4.3 kg/m², 74% female) randomized to 4:3 IMF (n = 84) or DCR (n = 81). At 12 months, binge eating and uncontrolled eating scores decreased in 4:3 IMF but increased in DCR (p < 0.01 for between-group differences). Among 4:3 IMF, greater weight loss was associated with decreased uncontrolled eating (r = −0.27, p = 0.03), emotional eating (r = −0.37, p < 0.01), and increased cognitive restraint (r = 0.35, p < 0.01) at 12 months. There were no between-group differences in changes in fasting appetite-related hormones at any time point. Conclusions: Compared to DCR, 4:3 IMF exhibited improved binge eating and uncontrolled eating behaviors at 12 months. This may, in part, explain the greater weight loss achieved by 4:3 IMF versus DCR. Future studies should examine mechanisms underlying eating behavior changes with 4:3 IMF and their long-term sustainability. Full article

Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R²) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R² exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species. Full article

Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. All three models exhibit strong predictive performance, with R² values exceeding 0.9 for both EUI and cost. XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m², while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m². In terms of computational efficiency, the surrogate models significantly outperform a physics-based simulation model, with MLP running approximately 340 times faster and XGBoost and RF achieving over 200 times speedup. This study also examines the effect of training dataset size on model performance, identifying a point of diminishing returns where further increases in data size yield minimal accuracy gains but substantially higher training times. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. Overall, the findings demonstrate that machine learning-based surrogate models can serve as fast, accurate, and interpretable alternatives to traditional simulation methods, supporting efficient decision-making during early-stage building design. Full article

Background: Frozen shoulder (FS) is a common musculoskeletal condition with significant socioeconomic impact. Despite its prevalence, the condition lacks a definitive understanding and universally effective treatment approach. Objective: To evaluate the effects of an intervention combining manual therapy, conventional exercises, and strategies to improve sleep quality and circadian rhythm on recovery and biomarkers in patients with FS. Methods: A single-blind, randomized, controlled trial was conducted with 34 participants divided into control and experimental groups (n = 17 each). Both groups received manual therapy and conventional exercises, while the experimental group (EG) also received sleep and circadian rhythm optimization instructions. Biomarkers (fasting glucose, insulin, Homeostasis Model Assessment of Insulin Resistance (HOMA) index, leptin, triglycerides, total cholesterol, HDL cholesterol, uric acid, CRP, IL-1β, IL-6, IL-17, IL-10, IL-33, HMGB1, and TNF-α) and functional outcomes (SPADI, ROM, and PSQI) were assessed pre- and post-intervention. Results: After six weeks, the EG showed significant improvements in IL-10 levels (mean change: 2.5 pg/mL vs. 0.5 pg/mL in the control group (CG), p = 0.03), IL-6 reduction (−1.8 pg/mL vs. −0.4 pg/mL, p = 0.02), and HOMA index (−0.8 vs. −0.2, p = 0.04). ROM improved by 20 degrees in the EG versus 10 degrees in the CG (p = 0.01), SPADI scores decreased by 25 points versus 15 points (p = 0.03), and PSQI improved by 4 points compared to 2 points (p = 0.05). Conclusion: The integration of sleep quality and circadian rhythm optimization into conventional rehabilitation significantly enhances recovery, particularly IL-10 modulation, but these did not translate into superior clinical improvements within the study period. Further long-term studies are needed to confirm whether early biological effects lead to sustained functional recovery in FS patients. Full article

The Dynamic Multi-objective Optimization Problem (DMOP) is one of the common problem types in academia and industry. The Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) is an effective way for solving DMOPs. Despite the existence of many research works proposing a variety of DMOEAs, the demand for efficient solutions to DMOPs in drastically changing scenarios is still not well met. To this end, this paper is oriented towards DMOEA and innovatively proposes to explore the correlation between different points of the optimal frontier (PF) to improve the accuracy of predicting new PFs for new environments, which is the first attempt, to our best knowledge. Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. The proposed method was evaluated by experiments on the CEC 2018 test suite, and the experimental results show that the proposed method can obtain the optimal MIGD value on six DMOPs and the optimal MHVD value on five DMOPs, compared with six recent research results. Full article

Traditional park designs no longer meet the diverse needs of young users amid rising visitor numbers and environmental challenges. Exploring the impact of mountain city parks on youth is crucial, yet localised studies on their spatial perceptions in such unique environments are lacking. Landscape design based on spatial perception evaluation offers a promising approach for renewing mountain parks to address these complex needs. Therefore, a pilot study was conducted in Chongqing’s Pipa Mountain and Eling Parks, involving questionnaire surveys and on-site spatial data collection. Using principal component analysis to select the visual and auditory indicators most related to environmental satisfaction in the overall park and various types of gathering spaces, the results showed that the first principal component of the visual environment in the entrance platform and key nodes (r = 0.41, r = 0.45), as well as the first principal component of the auditory environment in the entrance platform, path platform, and elevated points (r = 0.67, r = 0.85, r = 0.68), all showed significant positive correlations with environmental satisfaction (p < 0.01). Moreover, naturalness and aesthetics were identified as the main factors influencing environmental satisfaction. A random forest model analysed nonlinear relationships, ranking spatial factors by importance. Simultaneously, SHAP analysis highlighted the effects of key factors like elevation changes, green view index, colour diversity, and natural elements. Elevation changes were positively correlated with satisfaction at elevated points but showed a negative correlation in the overall park environment and other gathering spaces. This study explored space-perception dynamics in mountain city parks, proposing strategies to improve environmental quality in various gathering spaces and the park. These findings support creating liveable mountainous environments and guide “human-centred health,” quality enhancement, and sustainable development in renewing mountain city parks. Full article