Search Results (6,184)

Geosynchronous satellite maneuver detection is critical for enhancing space situational awareness and inferring satellite intent. However, traditional methods often require high-quality orbital sequence data and heavily rely on hand-crafted features, limiting their effectiveness in complex real-world environments. While recent neural network-based approaches have shown promise, they are typically trained in scene or task-specific settings, resulting in limited generalization and adaptability. To address these challenges, we propose MC-MD, a pre-training framework that integrates Masked and Clustered learning strategies to improve the robustness and transferability of geosynchronous satellite Maneuver Detection. Specifically, we introduce a masked prediction module that applies both time- and frequency-domain masking to help the model capture temporal dynamics more effectively. Meanwhile, a cluster-based module guides the model to learn discriminative representations of different maneuver patterns through unsupervised clustering, mitigating the negative impact of distribution shifts across scenarios. By combining these two strategies, MC-MD captures diverse maneuver behaviors and enhances cross-scenario detection performance. Extensive experiments on both simulated and real-world datasets demonstrate that MCMD achieves significant performance gains over the strongest baseline, with improvements of 8.54% in Precision and 7.8% in F1-Score. Furthermore, reconstructed trajectories analysis shows that MC-MD more accurately aligns with the ground-truth maneuver sequence, highlighting its effectiveness in satellite maneuver detection tasks. Full article

Inspired by the self-organizing optimization mechanisms in nature, the leaf venation of the royal water lily exhibits a hierarchically branched fractal network that combines excellent mechanical performance with lightweight characteristics. In this study, a structural bionic approach was adopted to systematically investigate the venation architecture through macroscopic morphological observation, experimental testing, 3D scanning-based reverse reconstruction, and finite element simulation. The influence of key fractal geometric parameters under vertical loading on the mechanical behavior and energy absorption capacity was analyzed. The results demonstrate that the leaf venation of the royal water lily exhibits a core-to-margin gradient fractal pattern, with vein thickness linearly decreasing along the radial direction. At each hierarchical bifurcation, the vein width is reduced to 65–75% of the preceding level, while the bifurcation angle progressively increases with branching order. During leaf development, the fractal dimension initially decreases and then increases, indicating a coordinated functional adaptation between the stiff central trunk and the compliant peripheral branches. The veins primarily follow curved trajectories and form a multidirectional interwoven network, effectively extending the energy dissipation path. Finite element simulations reveal that the fractal venation structure of the royal water lily exhibits pronounced nonlinear stiffness behavior. A smaller bifurcation angle and higher fractal branching level contribute to enhanced specific energy absorption and average load-bearing capacity. Moreover, a moderate branching length ratio enables a favorable balance between yield stiffness, ultimate strength, and energy dissipation. These findings highlight the synergistic optimization between energy absorption characteristics and fractal geometry, offering both theoretical insights and bioinspired strategies for the design of impact-resistant structures. Full article

This paper presents an automatic oil sampling system designed for vertical cylindrical oil tanks on land, focusing primarily on the structural design and control optimization for oil level measurement and liquid sampling inside the tank. First, the key structure and control architecture of the automatic sampler are introduced, explaining the collaborative working principles of its components to ensure good stability in system structure and motion control. On this basis, an improved Sparrow Search Algorithm (CSSA) is proposed, which integrates the Coati Optimization Algorithm (COA) and the traditional Sparrow Search Algorithm (SSA). This algorithm is used to optimize the parameters of the Proportional–Integral–Derivative (PID) control system in the oil sampler, aiming to address issues such as response delay, large overshoot, and insufficient stability that commonly occur in traditional PID control under complex conditions. This method achieves consistent response behavior over time and adaptiveness in the control process by dynamically adjusting the PID parameters in real time. To verify the effectiveness of the proposed control strategy, system simulations were conducted in the MATLAB 2024B environment, and a physical experimental platform was built for testing. The simulation compares the CSSA-PID controller with traditional PID, COA-PID, and SSA-PID control methods. In addition, a load disturbance was introduced at 300 ms to perform anti-interference comparative simulations. The results show that under CSSA-PID control, the system response time was shortened by up to 112 ms, the convergence speed improved by 72.3%, the global optimization capability was significantly enhanced, and the anti-interference ability was stronger. In the actual tests, the average error was reduced by approximately 45.3%. These results indicate that CSSA-PID can significantly enhance the stability and response speed of the control system. The efficient control of the automatic oil sampler will greatly enhance the intelligence and efficiency of oil level detection in tanks and reduce labor costs, having significant implications for the development of the grain and oil storage industry. Full article

Traditional book recommendation methods predominantly rely on collaborative filtering and context-based approaches. However, existing methods fail to account for the order of users’ book borrowings and the duration they hold them, both of which are crucial indicators reflecting users’ book preferences. To address this challenge, we propose a book recommendation framework called DPBD, which disentangles preferences based on borrowing duration, thereby explicitly modeling temporal patterns in library borrowing behaviors. The DPBD model adopts a dual-path neural architecture comprising the following: (1) The item-level path utilizes self-attention networks to encode historical borrowing sequences while incorporating borrowing duration as an adaptive weighting mechanism for attention score refinement. (2) The feature-level path employs gated fusion modules to effectively aggregate multi-source item attributes (e.g., category and title), followed by self-attention networks to model feature transition patterns. The framework subsequently combines both path representations through fully connected layers to generate user preference embeddings for next-book recommendation. Extensive experiments conducted on two real-world university library datasets demonstrate the superior performance of the proposed DPBD model compared with baseline methods. Specifically, the model achieved 13.67% and 15.75% on HR@1 and 15.75% and 12.90% on NDCG@1 across the two datasets. Full article

With the continuous growth of network threats, intrusion detection systems need to have robustness and adaptability to effectively identify malicious behaviors. However, factors such as noise interference, class imbalance, and complex attack pattern recognition have posed significant challenges to traditional systems. To address these issues, this paper proposes a dynamic preprocessing-enhanced DyP-CNX framework. The framework designs a sliding window dynamic interquartile range (IQR) standardization mechanism to effectively suppress the temporal non-stationarity interference of network traffic. It also combines a random undersampling strategy to mitigate the class imbalance problem. The model architecture adopts a CNN-XGBoost collaborative learning framework, combining a dual-channel convolutional neural network (CNN) and two-stage extreme gradient boosting (XGBoost) to integrate the original statistical features and deep semantic features. On the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the method achieved F1 values of 91.57% and 99.34%, respectively. The experimental results show that the DyP-CNX method has the potential to handle the feature drift and pattern confusion problems in complex network environments, providing a new technical solution for adaptive intrusion detection systems. Full article

Background/Objectives: Leadership in physical education plays a critical role in the holistic development of students, influencing variables such as satisfaction, group cohesion, and performance. Despite the abundance of cross-sectional studies, there is a paucity of longitudinal evidence exploring the temporal stability of these perceptions in adolescent populations, which limits the current understanding of leadership development in educational settings. This longitudinal study investigates how secondary and high school students perceive and prefer different leadership styles in PE and how these relate to gender, academic level, and sport participation, grounded in the multidimensional leadership model. The analysis is further contextualized by recent research emphasizing adaptive, evidence-based pedagogical approaches in physical education, the influence of competitive environments on leadership expectations, and the role of emotional support in training contexts. Methods: Using validated questionnaires (LSS-1 and LSS-2), five dimensions were assessed: Training and Instruction, democratic behavior, autocratic behavior, Social Support, and positive feedback, considering variables such as gender, academic level, and extracurricular sport participation. Data were collected at two time points over a 12-month interval, enabling the identification of temporal patterns in students’ perceptions and preferences. Sampling procedures were clearly defined to enhance transparency and potential replicability, and the choice of a convenience sample from two private schools was justified by accessibility and continuity in longitudinal tracking. Although no a priori power analysis was conducted, the sample size (n = 370) was deemed adequate for the non-parametric analyses employed, with an estimated statistical power ≥ 0.80 for medium effect sizes (Cohen’s d = 0.3–0.5). Results: The results revealed a marked preference for leadership styles emphasizing social support and positive feedback, particularly among students engaged in sports. Statistically significant differences (p < 0.05) were identified based on gender and academic maturity, with female students favoring democratic behavior and students in the fourth year of compulsory secondary education showing a stronger inclination toward styles prioritizing emotional support. Trends toward statistical significance (p < 0.10) were also reported, following precedents in the sport psychology and sport sciences literature, as they provide potentially relevant indications for future research directions. The congruence between perceived and preferred leadership emerged as a key factor in student satisfaction, confirming that adaptive leadership enhances students’ learning experiences and overall well-being. However, this satisfaction was inferred from congruence measures, rather than directly assessed, representing a key methodological limitation. Conclusions: This study underscores the importance of physical education teachers tailoring their leadership styles to the individual and group characteristics of their students. The findings align with methodological approaches used in preference hierarchy analyses in sport contexts and support calls for individualized pedagogical strategies observed in sports medicine and training research. By providing longitudinal evidence on leadership perception stability and integrating recent cross-disciplinary findings, the study makes an original contribution to bridging the gap between educational theory and practice. The results address a gap in the literature concerning the temporal stability of leadership perceptions among adolescents, offering a theoretically grounded basis for future research and the design of pedagogical innovations in PE. Full article

Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous methodologies and technologies employed in the real-time identification and mitigation of fraudulent transactions, including traditional statistical techniques, machine learning algorithms and advanced artificial intelligence strategies. It enhances the need to combine anomaly detection structures with behavioral analytics to enhance detection accuracy while addressing challenges like data privacy, the need to balance false positives and negatives and the need for adaptive systems. By evaluating the most recent developments and case studies, this study provides a comprehensive assessment of what is happening in bank transaction fraud detection and presents future directions for enhancing safety features. Full article

The thermal energy simulation of residential buildings involves estimating electricity consumption from both household appliances and air conditioning systems, whose use is influenced by the ambient temperatures of each municipality. However, existing mathematical simulation models face limitations in accurately reproducing electricity consumption patterns in homes across different climate types. This study proposes an enhanced CDH method, developed through a new function aimed at improving the accuracy of residential cooling demand estimation by incorporating behavioral and climatic variability. The function introduces the use of adaptive comfort temperature thresholds specific to each climate type and a time-selective activation mechanism that calculates cooling demand only during the hours when ambient temperature exceeds the adaptive threshold. These activation periods are determined analytically using a Fourier-based temperature model. A representative sample of 35 municipalities in Mexico was selected, covering different climate types and domestic electricity rates. The construction characteristics and average energy use habits of typical dwellings were defined using national housing and energy data to support the simulations. The results show that integrating adaptive thresholds into the CDH equations reduces the simulation error to below 10% when compared to actual residential electricity consumption. The proposed model is applicable across all Mexican municipalities, regardless of climate variability. Full article

The photochemical efficiency of photosystem II (PSII) was monitored in two-year-old seedlings from six Chilean woody sclerophyllous species differing in foliage habits (evergreen, deciduous, semi-deciduous) and leaf orientation. A common garden experiment was established in July 2020 in a Mediterranean-type climate site under two watering regimes (2 L⁻¹ seedling⁻¹ week⁻¹ for 5 months versus no irrigation). Chlorophyll a fluorescence rise kinetics (OJIP) and JIP test analysis were monitored from December 2021 to January 2022. The semi-deciduous Colliguaja odorifera (leaf angle of 65°) exhibited the highest performance in processes such as absorption and trapping photons, heat dissipation, electron transport, and level of photosynthetic performance (i.e., parameters PI_ABS F_V/F_M, F_V/F₀, and ΔV_IP). In contrast, the evergreen Peumus boldus (leaf rolling) exhibited the opposite behavior for the same parameters. On the other hand, the deciduous Vachelia caven (small compound leaves and leaf angle of 15°) showed the lowest values for minimal and maximal fluorescence (F₀ and F_M) and the highest area above the OJIP transient (S_m) during the study period. Irrigation decreased S_m and the relative contribution of electron transport (parameter ΔV_IP) by 22% and 17%, respectively, but no clear effects of the irrigation treatments were observed among species and dates of measurement. Overall, V. caven and C. odorifera exhibited the highest photosynthetic performance, whereas P. boldus seemed to be more prone to photoinhibition. We conclude that different foliar adaptations among species influence light protection mechanisms more than irrigation treatments. Full article

Flexibility is increasingly recognized as a key competence in addressing current challenges and transitions. It is a multidimensional construct, discussed across various disciplines, encompassing cognitive, behavioral, and emotional dimensions. The European LifeComp framework offers one of the most recent and comprehensive definitions of this competence, emphasizing its role in enabling individuals to adapt to uncertainty, manage complexity, and foster transformative learning. This study investigates the assessment tools available to evaluate flexibility competence, focusing on their alignment with the LifeComp framework. A systematic literature review was conducted using the Scopus and WoS databases, based on inclusion criteria for language, publication type, disciplinary area, research topic, and target population, identifying 22 eligible articles. Following a quality assessment of the articles, a critical analysis revealed the presence of 22 tools and scales, including the actively open-minded thinking (AOT) scale, the resistance to change (RTC) scale, and the flexible thinking in learning (FTL) questionnaire. The findings show overlaps among flexibility and related constructs, such as learning agility and intellectual humility. However, most tools are context-specific and fail to address the multidimensional nature of flexibility competence. Future research should prioritize the development of comprehensive instruments to support educational initiatives, policy development, and professional training. Full article

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by social communication deficits, repetitive behaviors, and frequent gastrointestinal comorbidities. Emerging research suggests gut microbiota alterations contribute to ASD symptoms and gastrointestinal dysfunction, but detailed microbial profiles and clinical correlations remain underexplored. Methods: This study analyzed gut microbiota in 45 children aged 2–18 years diagnosed with ASD. Stool samples underwent 16S rRNA gene sequencing. Clinical assessments included ASD diagnostic subtype, adaptive functioning using the Vineland Adaptive Behavior Scale, gastrointestinal symptoms as per the Rome IV criteria, dietary patterns, and demographic variables. Statistical analyses correlated microbiota profiles with clinical features. Results: Gut microbiota composition was significantly influenced by delivery mode, age, sex, and diet. Vaginally delivered children had higher beneficial SCFA-producing bacteria, whereas Cesarean section was linked to increased pathogenic Clostridiales. High-calorie and protein-rich diets correlated with shifts toward pro-inflammatory taxa. Microbial diversity and specific genera correlated with adaptive behavior domains (communication, socialization, motor skills) and severity of gastrointestinal symptoms. Both pro-inflammatory and anti-inflammatory bacteria variably impacted neurodevelopmental outcomes. Conclusions: Gut microbiota composition in children with ASD is shaped by multifactorial influences and connected to neurobehavioral and gastrointestinal phenotypes. The findings of this study support the potential of microbiota-targeted interventions to ameliorate ASD-associated symptoms and improve quality of life. Full article

This study presents a comprehensive analysis of climate evolution in the Porto region (Northern Portugal) using 148 years (1863–2010) of continuous meteorological data from the Serra do Pilar weather station (WMO station 08546). The research employs both traditional linear statistical methods and advanced non-linear analysis techniques, including polynomial trend fitting and multidecadal oscillation analysis, to accurately characterize long-term climate patterns. Results reveal that linear trend analysis is misleading for this dataset, as both temperature and precipitation follow parabolic (U-shaped) distributions with minima around 1910–1970. The early period (1863–1900) exhibited higher values than the recent period, contradicting linear trend interpretations. Advanced analysis shows that the mean temperature follows a parabolic pattern (R² = 0.353) with the minimum around 1935, while precipitation exhibits similar behavior (R² = 0.053) with the minimum around 1936. Multidecadal oscillations are detected with dominant periods of 46.7, 15.6, and 10.0 years for temperature, and 35.0, 17.5, and 4.5 years for precipitation. Maximum temperatures show complex oscillatory behavior with a severe drop around 1890. Seasonal analysis reveals distinct patterns across all seasons: winter (+0.065 °C/decade) and autumn (+0.059 °C/decade) show warming trends in maximum temperatures, while spring (−0.080 °C/decade) and summer (−0.079 °C/decade) demonstrate cooling trends in minimum temperatures, with no significant trends in spring (+0.012 °C/decade) and summer (+0.003 °C/decade) maximum temperatures or winter (−0.021 °C/decade) and autumn (−0.035 °C/decade) minimum temperatures. The study identifies a significant change point in mean temperature around 1980, which occurs approximately one decade earlier than the global warming acceleration typically observed in the 1990s, suggesting regional Atlantic influences may precede global patterns. Extreme event analysis indicates stable frequencies of hot days (averaging 3.6 days/year above 25.0 °C) and heavy precipitation events (averaging 1.2 days/year above 234.6 mm) throughout the study period. These findings demonstrate that the Porto region’s climate is characterized by natural multidecadal variability rather than monotonic trends, with the climate system showing oscillatory behavior typical of Atlantic-influenced coastal regions. The results contribute to understanding regional climate variability and provide essential baseline data for climate change adaptation strategies in Northern Portugal. The results align with broader patterns of natural climate variability in the Iberian Peninsula while highlighting the importance of non-linear analysis for comprehensive climate assessment. Full article

This study examined how digital technology facilitated early childhood music learning in multi-sensory, engaging experiences. In a 16-week quasi-experimental, mixed-method study that used the Holistic Music Educational Approach for Young Children (HMEAYC) with 103 children and 36 pre-service teachers in Taiwan, sensor-based audio devices and responsive technologies were used instead of screens. Observations and video analysis showed that after an initial phase of adaptation, children exhibited growth in spontaneous and imitative musical behaviors, sensory integration, motor coordination, and creativity. Full article

Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition accuracy, traditional implantable electronic devices such as RFID (i.e., Radio Frequency Identification) can cause some degree of harm or stress reactions to cows. Image-based methods are widely used due to their non-invasive advantages, but these methods have poor adaptability to different environments and target size, and low detection accuracy in complex scenes. To solve these issues, this study designs a Dy_Conv (i.e., dynamic convolution) module and innovatively constructs a Dynamic_Bottleneck module based on the Dy_Conv and S2Attention (Sparse-shift Attention) mechanism. On this basis, we replaces the first and fourth bottleneck layers of Resnet50 with the Dynamic_Bottleneck to achieve accurate extraction of local features and global information of cows. Furthermore, the QAConv (i.e., query adaptive convolution) module is introduced into the front end of the backbone network, and can adjust the parameters and sizes of convolution kernels to adapt to the scale changes in cow targets and input images. At the same time, NAM (i.e., normalization-based attention module) attention is embedded into the backend of the network to achieve the feature fusion in the channels and spatial dimensions, which contributes to better distinguish visually similar individual cows. The experiments are conducted on the public datasets collected from different cowsheds. The experimental results showed that the Rank-1, Rank-5, and mAP metrics reached 96.8%, 98.9%, and 95.3%, respectively. Therefore, the proposed model can effectively capture and integrate multi-scale features of cow body appearance, enhancing the accuracy of individual cow identification in complex scenes. Full article

Drug abuse is a chronic, relapsing disorder marked by compulsive drug-seeking behavior and profound neurobiological consequences. Each year, millions of individuals face serious social and legal repercussions due to addiction. This review synthesizes findings from both preclinical and clinical studies to examine how chronic exposure to substances such as alcohol, cocaine, methamphetamine, and opioids affects the central nervous system. Specifically, it explores the epigenetic modifications induced by these substances, including DNA methylation, histone modifications, and noncoding RNA regulation. The literature was selected using a thematic approach, emphasizing substance-specific mechanisms and their effects on gene expression, synaptic plasticity, and the brain’s reward circuitry. Emerging evidence links these epigenetic changes to long-term behavioral adaptations and even transgenerational inheritance. This review underscores the complex molecular pathways contributing to addiction, vulnerability, and relapse, offering insights into potential therapeutic targets. Full article