Search Results (854)

College student military training is an organized, high-intensity, short-term militarized activity in China; this study aims to explore the differences in thermal perception between different intensities of military training. Questionnaires and microclimate measurements were conducted during summer military training in cold regions, including the Protective and Rescue Training and Assessment (PRTA), Formation Training (FT), the Shooting and Tactical Training and Assessment (STTA), the Route March (RM), and Dagger Practice (DP). The results indicated that (1) there was no significant correlation between the intensity of the activity and votes on thermal perception. The strongest thermal sensations, the lowest comfort, and the lowest thermal acceptability were experienced during FT, with a lower activity intensity. (2) Air temperature (T_a), globe temperature (T_g), relative humidity (RH), mean radiant temperature (T_mrt), and solar radiation (G) had significant effects on the TSV. (3) FT involved the lowest neutral temperatures (NUTCI/NPET), while DP and RM training had the highest NUTCI and NPET values, respectively. The neutral temperature range during military training was narrower compared to that in other aerobic activities. This study reveals, for the first time, the non-traditional correlation between military training intensity and thermal perception, confirming the specificity of thermal sensations in mandatory training and providing a theoretical basis for optimizing military training arrangements and developing thermal protection strategies. Full article

A key discovery of this study is the strong correlation (r = 0.96) between excitation and emission maxima across chemically distinct clusteroluminogens. All 157 evaluated peaks fall along a single regression line (Ex = 0.844 Em − 12 nm), a pattern that was not valid for conventional fluorophores. This suggests a general principle of clusteroluminescence. We show that in lignocellulosic materials, peak positions reflect chemical interactions: isolated lignin and cellulose showed short excitation and emission wavelengths, while native wood exhibited longer wavelengths. Fungal or photoinduced degradation led to a further red-shift. These effects are attributed to increased molecular heterogeneity, reducing the effective energy gap within the lignocellulosic complex. We conclude that the spectral position reflects the degree of molecular interaction rather than the chemical structure of individual molecules. It may serve as a novel analytical parameter for assessing purity and degradation in a wide range of polymers. Full article

Background/Objectives: Consuming comfort food is a common experience in daily life, but the underlying motives for engaging in comfort eating remain unclear. This study examined people’s expectations regarding their comfort food and investigated whether these expectations are associated with their frequency of comfort eating. As an exploratory aim, we also examined whether there are gender differences in preference for different categories of comfort food (i.e., sweet or savory) and the frequency of engaging in comfort eating. Methods: Through an online survey, participants (n = 214) reported their primary comfort food, the frequency of comfort eating in the short term (i.e., the past two weeks), and the general trend over the long term. They also rated statements related to their primary comfort food based on five expectation subscales (i.e., Manage Negative Affect; Pleasurable and Rewarding; Enhances Cognitive Competence; Alleviates Boredom; Positive Feelings). Results: Although Pleasurable and Rewarding and Positive Feelings received the strongest level of endorsement, their associations with the frequency-related variables were weak in both correlational and regression analyses. In contrast, Manage Negative Affect, Alleviates Boredom, and Enhances Cognitive Competence were positively associated with all frequency-related variables, with Alleviates Boredom showing the most consistent pattern. There were no significant gender differences in preferences for sweet or savory comfort food, and no significant gender differences in the frequency of eating comfort food. Conclusions: These findings suggest people believe they can gain a range of expected benefits from consuming comfort foods and perceive themselves as consuming comfort food primarily for rewarding themselves or gaining positive feelings. However, it is the expectations of managing negative affect, alleviating boredom, and enhancing cognitive competence that motivate them to engage in comfort eating. Full article

As designers push engine efficiency closer to thermodynamic limits, the analysis of flow instabilities developed in a high-pressure turbine (HPT) is crucial to minimizing aerodynamic losses and optimizing secondary air systems. Purge flow, while essential for protecting turbine components from thermal stress, significantly impacts the overall efficiency of the engine and is strictly connected to cavity modes and rim-seal instabilities. This paper presents an experimental investigation of these instabilities in an HPT stage, tested under engine-representative flow conditions in the short-duration turbine rig of the von Karman Institute. As operating conditions significantly influence instability behavior, this study provides valuable insight for future turbine design. Fast-response pressure measurements reveal asynchronous flow instabilities linked to ingress–egress mechanisms, with intensities modulated by the purge rate (PR). The maximum strength is reached at PR = 1.0%, with comparable intensities persisting for higher rates. For lower PRs, the instability diminishes as the cavity becomes unsealed. An analysis based on the cross-power spectral density is applied to quantify the characteristics of the rotating instabilities. The speed of the asynchronous structures exhibits minimal sensitivity to the PR, approximately 65% of the rotor speed. In contrast, the structures’ length scale shows considerable variation, ranging from 11–12 lobes at PR = 1.0% to 14 lobes for PR = 1.74%. The frequency domain analysis reveals a complex modulation of these instabilities and suggests a potential correlation with low-engine-order fluctuations. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions. Full article

This study investigated how factors associated with Sinner’s principle—namely temperature, time, and the chemical composition of detergents—affected the antimicrobial efficacy of domestic dishwashers, particularly during short cycles. These are of particular interest, because many consumers refrain from using long cycles while it is still unclear if short cycles can provide a sufficient level of hygiene. Thus, we chose a range of bacterial strains, including standard test strains such as Micrococcus luteus and Enterococcus faecium, as well as important foodborne pathogens such as Escherichia coli, Staphylococcus aureus, and Salmonella enterica. To account for the complexity of dishwasher cycles, we correlated hygiene efficacy with area under the curve (AUC) measurements derived from the respective cycle profiles. Our findings revealed that the reductions in M. luteus and E. faecium were minimally affected by the reference detergent. In contrast, a high-tier market detergent demonstrated a significant decrease in bacterial counts. Notably, both strains exhibited reduced efficacy at a main cycle temperature of 45 °C, suggesting that temperatures below 50 °C might represent a critical threshold at which the hygiene efficacy of domestic dishwashing processes declines. However, since food-related pathogens were more susceptible to the dishwashing process, even lower temperatures might deliver a sufficient level of hygiene. Plotting the logarithmic reduction/AUC ratio against the AUC indicated that the main cycle contributed approximately 10-fold more to microbial reduction than the rinse cycle. Furthermore, the antimicrobial impact of detergents was greatest at the lowest AUC values (i.e., during short cycles). Taken together, our results suggest that the applied chemistry may help to enhance antimicrobial performance especially in short dishwashing cycles. Full article

Physical activity plays a critical role in health and well-being, particularly during students’ academic development. This study explores the relationship between self-efficacy and physical activity among physiotherapy students, recognizing self-efficacy as a key factor influencing exercise behavior. Despite awareness of physical activity’s benefits, academic demands may hinder participation, reducing confidence in maintaining an active lifestyle. A total of 244 physiotherapy students (mean age 24.44 ± 7.56 years) completed the General Self-Efficacy Scale (GSES) and the International Physical Activity Questionnaire—Short Form (IPAQ-SF). The results showed that the self-efficacy scores ranged from 17 to 40, with a mean of 30.44 (±3.93), indicating moderate to high levels. In terms of activity, 40.3% of students reported sufficient activity (high level), 51.7% reported moderate activity (meeting minimum guidelines), and 8.05% reported insufficient (low) activity. Self-efficacy positively correlated with age (r = 0.199, p < 0.01) and education level (r = 0.191, p < 0.01), and negatively with employment (r = –0.171, p < 0.05). Physical activity was significantly associated with self-efficacy (r = 0.217, p < 0.01). These findings underscore the importance of fostering self-efficacy to promote physical activity, highlighting the need for targeted strategies within academic settings to support student well-being and healthier lifestyle choices. Full article

The earth’s surface has an uneven solar energy density that is sufficient to stimulate solar photovoltaic (PV) production. This causes variations in a solar plant’s output, which are impacted by geometrical elements and atmospheric conditions that prevent it from passing. Motivated by the focus on encouraging increased PV production efficiency, the goal was to use machine learning models (MLM) to map the distribution of solar energy in Mozambique in a regressive literal and geospatial–temporal manner on a short measurement scale. The clear-sky index $\left(K_t^{*}\right)$ theoretical approach was applied in conjunction with MLM that emphasized random forest (RF) and artificial neural networks (ANNs). Solar energy mapping was the result of the methodology, which involved statistically calculating $K_t^{*}$ for the analysis of solar energy in correlational and causal terms of the space-time distribution. Utilizing data from PVGIS, NOAA, NASA, and Meteonorm, a sample of solar energy was gathered at 11 measurement stations in Mozambique over a period of 1 to 10 min between 2012 and 2014 as part of the FUNAE and INAM measurement programs. The statistical findings show a high degree of solar energy incidence, with increments $\left(∆K_t^{*}\right)$ in the average order of −0.05 and $K_t^{*}$ mostly ranging between 0.4 and 0.9. In 2012 and 2014, $K_t^{*}$ was 0.8956 and 0.6986, respectively, because clear days had a higher incident flux and intermediate days have a higher frequency of ${∆K}_t^{*}$ on clear days and a higher occurrence density. There are more cloudy days now 0.5214 as opposed to 0.3569. Clear days are found to be influenced by atmospheric transmittance because of their high incident flux, whereas intermediate days exhibit significant variations in the region’s solar energy. Full article

This study presents a comprehensive numerical framework for Background-Oriented Schlieren (BOS) to systematically evaluate its performance and reconstructive capabilities under complex flow conditions. This framework integrates two stages: forward modeling, using ray tracing to simulate image degradation, and inverse processing, using optical flow and a conjugate gradient algorithm to extract displacements and reconstruct phase information. This method is first validated using turbulent flow fields in the Johns Hopkins Turbulence Database, where the reconstructed phase screens closely match the original data, with relative errors below 4% and structural similarity indices above 0.75 in all cases, providing a possible restoration method for degraded flow field images. It is then applied to shock wave fields with varying Mach numbers; this method achieves meaningful reconstruction at short ranges but fails under long-range imaging due to severe wavefront distortions. However, even in degraded conditions, the extracted optical flow fields preserve structural features correlated with the underlying shock patterns, indicating potential for BOS-based target recognition. These findings highlight both the capabilities and limitations of BOS and suggest new pathways for extending its use beyond traditional flow visualization. Full article

McComas et al. (2025) introduced a numerical experiment, where ordinary uncorrelated collisions between collision pairs are followed by other, controlled (correlated) collisions, shedding light on the emergence of kappa distributions through particle correlations in space plasmas. We extend this experiment by introducing correlations indicating that (i) when long-range correlations are interwoven with collision pairs, the resulting thermodynamic kappa are described as that corresponding to an ‘interatomic’ potential interaction among particles; (ii) searching for a closer description of heliospheric plasmas, we found that pairwise short-range correlations are sufficient to lead to appropriate values of thermodynamic kappa, especially when forming correlated clusters; (iii) multi-particle correlations do not lead to physical stationary states; finally, (iv) an optimal model arises when combining all previous findings. In an excellent match with space plasmas observations, the thermodynamic kappa that describes the stationary state at which the system is stabilized behaves as follows: (a) When correlations are turned off, kappa is turning toward infinity, indicating the state of classical thermal equilibrium (Maxwell-Boltzmann distribution), (b) When collisions are turned off, kappa is turning toward the anti-equilibrium state, the furthest state from the classical thermal equilibrium (−5 power-law phase-space distribution), and (c) the finite kappa values are generally determined by the competing factor of collisions and correlations. Full article

Adverse childhood experiences (ACEs) are linked to a higher risk of criminal behaviour, while positive childhood experiences (PCEs) may offer a protective effect by mitigating the negative impact of ACEs. Both ACEs and PCEs play a significant role in shaping emotional regulation. However, research on the influence of PCEs within incarcerated populations remains limited. This study aimed to examine the associations between ACEs, PCEs, and emotional regulation difficulties in a prison sample, and to explore whether PCEs moderate the relationship between ACEs and emotional regulation difficulties in adulthood. The analysis considered both the overall emotional regulation difficulties score and its specific dimensions—strategies, non-acceptance, impulse, goals, awareness, and clarity. The study included 283 men, with a mean age of 40.03 (SD = 11.64), ranging from 19 to 84 years, who were incarcerated in seven prisons in northern Portugal. Data were collected using the Adverse Childhood Experiences Scale, the Benevolent Childhood Experiences Scale, and the Difficulties in Emotional Regulation Scale—Short Form. The results revealed statistically significant positive correlations between ACEs and overall emotional regulation difficulties, as well as with nearly all subscales (strategies, impulse, goals, awareness, and clarity). Conversely, PCEs were significantly negatively correlated with overall emotional regulation difficulties and most subscales (impulse, goals, awareness, and clarity). However, PCEs did not moderate the relationship between ACEs and emotional regulation difficulties. These findings may be influenced by the characteristics of the sample, highlighting the need for further research—preferably longitudinal studies—to better assess the potential moderating role of PCEs. Such research could enhance the effectiveness of prevention and intervention strategies for incarcerated populations. Full article

Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. Traditional methods for FCR optimization rely on experience and long-term data collection, which are time-consuming and inefficient. This study explores the feasibility of predicting long-term FCR using short-term FCR data based on machine learning techniques. We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. The results show that the Gradient Boosting model demonstrated superior performance, achieving a coefficient of determination (R²) of 0.72 and a correlation of 0.85 between predicted and actual values when the testing interval exceeded 40 kg. Therefore, we recommend a minimum feeding measurement interval of 40 kg. Furthermore, when the testing interval was set to 40 kg and further refined to the range of 50–90 kg, the model achieved an R² of 0.81 and a correlation of 0.90 for FCR prediction in the 30–105 kg range. Among the 19 machine learning algorithms tested, Gradient Boosting, LightGBM, and CatBoost showed superior performance, with Gradient Boosting achieving the best results. Considering practical production requirements, the 50–90 kg feeding stage proved to be the most ideal for FCR testing. This study provides a more effective method for predicting feed efficiency and offers robust data support for precision livestock farming. Full article

The ionospheric total electron content (TEC) has complex spatiotemporal variations, making its spatiotemporal prediction challenging. Capturing long-range spatial dependencies is of great significance for improving the spatiotemporal prediction accuracy of TEC. Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) can selectively store and focus on long-range spatial dependencies, but it requires the input length and output length to be the same due to its “n vs. n” structure, limiting its application. To solve this problem, this paper proposes an encoder-decoder SA-ConvLSTM, abbreviated as ED-SA-ConvLSTM. It can effectively capture long-range spatial dependencies using SA-ConvLSTM and achieve unequal input-output lengths through encoder–decoder structure. To verify its performance, the proposed ED-SA-ConvLSTM was compared with C1PG, ConvLSTM, and PredRNN from multiple perspectives in the area of 12.5° S–87.5° N, 25° E–180° E, including overall quantitative comparison, comparison across different months, comparison at different latitude regions, visual comparisons, and comparison under extreme situations. The results have shown that, in the vast majority of cases, the proposed ED-SA-ConvLSTM outperforms the comparative models. Full article

The increasing global oil consumption has led to significant soil contamination by hydrocarbons, notably diesel-range hydrocarbons. Soil bioremediation through bacterial bioaugmentation is an alternative to increase the degradation of organic pollutants such as petroleum products. Bioremediation is a sustainable practice that contributes to the Sustainable Development Goals (SDGs) because it is environmentally friendly, reduces the impact of human activities, and avoids the use of invasive and destructive methods in soil restoration. This study examines the bioremediation potential of hydrocarbonoclastic bacteria isolated from soil close to areas with a risk of spills due to pipelines carrying hydrocarbons. Among the isolated strains, Arthrobacter globiformis, Pantoea agglomerans, and Nitratireductor soli exhibited hydrocarbonoclast activity, achieving diesel removal of up to 90% in short-chain alkanes and up to 60% in long-chain hydrocarbons. The results from in silico studies, which included molecular docking and molecular dynamics simulations, suggest that the diesel removal activity can be explained by the bioavailability of the linear alkanes and their affinity for alkane monooxygenase AlkB present in the studied microorganisms, since long-chain hydrocarbons had lower enzyme affinity and lower aqueous solubility. The correlation of the experimental results with the computational analysis allows for greater insight into the processes involved in the microbial degradation of hydrocarbons with varying chain lengths. Furthermore, this methodology establishes a cost-effective approximation tool for the evaluation of the feasibility of using different microorganisms in bioremediation processes. Full article