Search Results (5,421)

Cognitive flexibility—the ability to adapt cognitive strategies and behavioral responses in changing environments—is a key component of executive function, supporting rule updating and conflict resolution. Individuals with substance addiction often exhibit behavioral rigidity and reduced adaptability, reflecting impairments in this domain. This study examined cognitive flexibility in individuals with methamphetamine dependence through three behavioral tasks—intra-dimensional task switching, extra-dimensional task switching, and the Wisconsin Card Sorting Test (WCST)—in combination with a subjective self-report measure. Results showed that, compared to healthy controls, methamphetamine-dependent individuals demonstrated elevated reaction time switch costs in Intra-dimensional Task Switching and increased accuracy switch costs in Extra-dimensional Task Switching, as well as more perseverative and non-perseverative errors in the WCST. These findings suggested not only reduced performances in explicitly cued rule updating and strategic shifting but also deficits in feedback-driven learning and inflexibility in cognitive set shifting on methamphetamine-dependent individuals. Moreover, their self-reported cognitive flexibility scores were aligned with their objective performance, significantly lower than healthy controls. In summary, these findings revealed consistent cognitive flexibility impairments at both behavioral and subjective levels in individuals with methamphetamine dependence, indicating a core executive dysfunction that may undermine adaptive functioning in real-life contexts. The study offers critical insights into the cognitive mechanisms underlying addiction and provides a theoretical foundation for targeted cognitive interventions. Full article

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset—a 2.4 percentage point improvement over the baseline YOLO11—while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions. Full article

Alcohol Use Disorder (AUD) profoundly impacts individuals and society, driven by neurobiological adaptations that sustain chronicity and relapse. Emerging research highlights neuroinflammation, particularly microglial activation, as a central mechanism in AUD pathology. Ethanol engages microglia—the brain’s immune cells—through key signaling pathways such as Toll-like receptor 4 (TLR4) and the NLRP3 inflammasome, triggering the release of proinflammatory cytokines (IL-1β, TNF-α, IL-6). These mediators alter synaptic plasticity in addiction-related brain regions, including the ventral tegmental area, nucleus accumbens, amygdala, and lateral habenula, thereby exacerbating cravings, withdrawal symptoms, and relapse risk. Rodent models reveal that microglial priming disrupts dopamine signaling, heightening impulsivity and anxiety-like behaviors. Human studies corroborate these findings, demonstrating increased microglial activation markers in postmortem AUD brains and neuroimaging analyses. Notably, sex differences influence microglial reactivity, complicating AUD’s neuroimmune landscape and necessitating sex-specific research approaches. Microglia-targeted therapies—including minocycline, ibudilast, GLP-1 receptor agonists, and P2X7 receptor antagonists—promise to mitigate neuroinflammation and reduce alcohol intake, yet clinical validation remains limited. Addressing gaps such as biomarker identification, longitudinal human studies, and developmental mechanisms is critical. Leveraging multi-omics tools and advanced neuroimaging can refine microglia-based therapeutic strategies, offering innovative avenues to break the self-sustaining cycle of AUD. Full article

The article explores how pro-environmental action relates to knowledge processes and fosters transformative changes in vulnerable settings. Drawing on eleven pro-environmental initiatives in five countries (Bulgaria, Hungary, Portugal, Romania and Türkiye), the study focuses on locally embedded actions responding to environmental threats, biodiversity loss and traditional practices. Based on 71 semi-structured interviews with citizens, we captured how environmental stewardship is shaped through lived experience, situated knowledge and shifting roles of actors under variable, often adverse governance conditions. We found that knowledge emerges as a co-produced and relational process, blending scientific, traditional, experiential and process-related knowledge. This supports participation and legitimacy and enables transformative (or behavioural) change. Transformative outcomes appear as behavioural shifts, self-empowerment, increased community agency and broader societal signals evolving from participation. The article contributes to the debate on sustainability transformation as it showcases potentially uncharted factors in current sustainability transition studies, i.e., emotional, political and relational dimensions of local pro-environmental actions in vulnerable settings. Even if systemic conditions limit transformative processes, this practical knowledge might be scaled up or adapted to other local or regional contexts to confront dominant socio-economic models and propose more inclusive, just and sustainable alternatives. Full article

The integration of bacterial biotechnology into construction and geotechnical practices is redefining approaches to material sustainability, infrastructure longevity, and environmental resilience. Over the past two decades, research activity in construction biotechnology has expanded rapidly, with more than 350 publications between 2000 and 2024 and a five-fold increase in annual output since 2020. Beyond bibliometric growth, technical studies have demonstrated the remarkable performance of bacterial systems: for example, microbial-induced calcium carbonate precipitation (MICP) can increase the compressive strength of treated soils by 60–70% and reduce permeability by more than 90% in field-scale trials. In concrete applications, bacterial self-healing has been shown to seal cracks up to 0.8 mm wide and improve water tightness by 70–90%. Similarly, biofilm-mediated corrosion barriers can extend the durability of reinforced steel by significantly reducing chloride ingress, while bacterial biopolymers such as xanthan gum and curdlan enhance soil cohesion and water retention in eco-grouting and erosion control. The novelty of this review lies in its interdisciplinary scope, integrating microbiological mechanisms, materials science, and engineering practice to highlight how bacterial processes can transition from laboratory models to real-world applications. By combining quantitative evidence with critical assessment of scalability, biosafety, and regulatory challenges, this paper provides a comprehensive framework that positions construction biotechnology as a transformative pathway towards low-carbon, adaptive, and resilient infrastructure systems. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article

Existing fault diagnosis methods assume the identical distribution of training and test data, failing to adapt to source–target domain differences in industrial scenarios and limiting generalization. They also struggle to explore inter-domain correlations with scarce labeled target samples, leading to poor convergence and generalization. To address this, our paper proposes a cross-domain few-shot intelligent fault diagnosis method based on Mixup data augmentation. Firstly, a Mixup data augmentation method is used to linearly combine source domain and target domain data in a specific proportion to generate mixed-domain data, enabling the model to learn correlations and features between data from different domains and improving its generalization ability in cross-domain few-shot learning tasks. Secondly, a feature decoupling module based on the self-attention mechanism is proposed to extract domain-independent features and domain-related features, allowing the model to further reduce the domain distribution gap and effectively generalize source domain knowledge to the target domain. Then, the model parameters are optimized through a multi-task learning mechanism consisting of sample classification tasks and domain classification tasks. Finally, applications in classification tasks on multiple sets of equipment fault datasets show that the proposed method can significantly improve the fault recognition ability of the diagnosis model under the conditions of large distribution differences in the target domain and scarce labeled samples. Full article

To address the insufficient adaptability of imported peanut harvesting equipment’s soil-engaging components to the specific soil conditions in Xinjiang, this study conducted Discrete Element Method (DEM)-based calibration of soil mechanical parameters using field soil samples with 1–20% moisture content from typical peanut cultivation areas in southern Xinjiang. Through the EDEM simulation platform, a comprehensive approach integrating the Hertz–Mindlin with the JKR adhesion model and Hertz–Mindlin with the Bonding model was employed to systematically calibrate nine key parameters: coefficient of restitution, static friction coefficient, rolling friction coefficient, JKR surface energy, normal/tangential stiffness per unit area, critical normal/tangential force, and soil bonding disk radius. Adopting static angle of repose (SAOR) and unconfined compressive force (UCF) as dual-response indicators, a hybrid experimental design strategy combining Central Composite Design (CCD), Plackett–Burman (PB) screening, and Box–Behnken Design (BBD) optimization was implemented. Regression models for SAOR and UCS were established, yielding six sets of soil parameters optimized for different moisture conditions through parameter optimization. Field validation demonstrated the following: ≤3.27% error in SAOR, ≤1.46% error in UCF, and ≤5.05% error in drawbar resistance validation for field digging shanks. Experimental results confirm that the model demonstrates strong prediction accuracy for soils in typical peanut harvesting regions of southern Xinjiang, thereby providing key parameter references for the future self-developed, highly adaptive soil-engaging components with drag reduction optimization in peanut harvesters for the Xinjiang region. Full article

Background: Digital self-efficacy is a crucial determinant of healthcare professionals’ ability to adapt to technological innovations. Understanding its predictors among nurses and nursing students is essential for workforce readiness. Objectives: To assess the level of digital self-efficacy and examine demographic, educational, and experiential factors associated with inadequate self-efficacy. Methods: This cross-sectional study involved 1081 Italian nurses and nursing students. The Digitech-S scale was used to measure digital self-efficacy, with ≥70/100 indicating adequacy. Logistic regression was performed to identify predictors of inadequate self-efficacy. Results: Only 47.1% of participants demonstrated adequate self-efficacy. Females had twice the odds of inadequate self-efficacy compared to males (OR = 2.038, p < 0.001). Nurses with bachelor’s degrees had 2.5 times higher odds than students (OR = 2.450, p < 0.001), while post-graduate education showed no effect. Early technology adoption before age 14 reduced the odds (OR = 0.675, p = 0.027). Each additional year of work experience decreased the odds by 4% (OR = 0.955, p < 0.001). Conclusions: Gender disparities persist in digital self-efficacy, and unexpectedly, students outperformed bachelor-level nurses. Findings highlight educational gaps and the importance of early exposure to technology. Tailored interventions are needed to strengthen digital readiness, which may improve care quality and healthcare system efficiency in the digital era. Full article

Event-based cameras, as a revolutionary class of dynamic vision sensors, offer transformative advantages for capturing transient mechanical phenomena through their asynchronous, per-pixel brightness change detection mechanism. These neuromorphic sensors excel in challenging industrial environments with their microsecond-level temporal resolution, ultra-low power requirements, and exceptional dynamic range that significantly outperform conventional imaging systems. In this way, the event-based camera provides a promising tool for machine vibration sensing and fault diagnosis. However, the dynamic vision data from the event-based cameras have a complex structure, which cannot be directly processed by the mainstream methods. This paper proposes a dynamic vision-based non-contact machine fault diagnosis method. The Eagle Vision Transformer (EViT) architecture is proposed, which incorporates biologically plausible computational mechanisms through its innovative Bi-Fovea Self-Attention and Bi-Fovea Feedforward Network designs. The proposed method introduces an original computational framework that effectively processes asynchronous event streams while preserving their inherent temporal precision and dynamic response characteristics. The proposed methodology demonstrates exceptional fault diagnosis performance across diverse operational scenarios through its unique combination of multi-scale spatiotemporal feature analysis, adaptive learning capabilities, and transparent decision pathways. The effectiveness of the proposed method is extensively validated by the practical condition monitoring data of rotating machines. By successfully bridging cutting-edge bio-inspired vision processing with practical industrial monitoring requirements, this work creates a new paradigm for dynamic vision-based non-contact machinery fault diagnosis that addresses critical limitations of conventional approaches. The proposed method provides new insights for predictive maintenance applications in smart manufacturing environments. Full article

High-resolution remote sensing images suffer from large intra-class variance, high inter-class similarity, and significant scale variations, leading to incomplete segmentation and imprecise boundaries. To address these challenges, Transformer-based methods, despite their strong global modeling capability, often suffer from feature confusion, weak detail representation, and high computational cost. Moreover, existing multi-scale fusion mechanisms are prone to semantic misalignment across levels, hindering effective information integration and reducing boundary clarity. To address these issues, a Category-Guided Transformer (CIGFormer) is proposed. Specifically, the Category-Information-Guided Transformer Module (CIGTM) integrates global and local branches: the global branch combines window-based self-attention (WSAM) and window adaptive pooling self-attention (WAPSAM), using class predictions to enhance global context modeling and reduce intra-class and inter-class confusion; the local branch extracts multi-scale structural features to refine semantic representation and boundaries. In addition, an Adaptive Wavelet Fusion Module (AWFM) is designed, which leverages wavelet decomposition and channel-spatial joint attention for dynamic multi-scale fusion while preserving structural details. Extensive experiments on the ISPRS Vaihingen and Potsdam datasets demonstrate that CIGFormer, with only 21.50 M parameters, achieves outstanding performance in small object recognition, boundary refinement, and complex scene parsing, showing strong potential for practical applications. Full article

Drilling fluid losses into formation voids are among the major issues that lead to increases in the costs and nonproductive time of operations. Lost circulation materials have been widely used to stop or mitigate losses. In most cases, the size of the loss zone is not known, making conventional lost circulation materials unsuitable for plugging the loss zone. In this study, novel temperature-sensitive LCM (TS-LCM) particles composed of diglycidyl ether of bisphenol A (DGEBA) and 4,4′-diaminodiphenyl methane were prepared. It is a thermal-response shape-memory polymer. The molecular structure was analyzed by Fourier transform infrared spectroscopy. The glass transition temperature (T_g) was tested by Different scanning calorimetry (DSC). The shape-memory properties were evaluated by a bend-recovery test instrument. The expansion and mechanical properties of particles were investigated under high temperature and high pressure. Fracture sealing testing apparatus was used to evaluate sealing performance. The mechanism of sealing fracture was discussed. Research results indicated that the T_g of the TS-LCM was 70.24 °C. The shape fixation ratio was more than 99% at room temperature, and the shape recovery ratio was 100% above the T_g. The particle was flaky before activation. It expanded to a cube shape, and the thickness increased when activated. The rate of particle size increase for D90 was more than 60% under 120 °C and 20 MPa. The activated TS-LCM particles had high crush strength. The expansion of the TS-LCM particles could self-adaptively bridge and seal the fracture without knowing the width. The addition of TS-LCM particles could seal the tapered slot with entrance widths of 2 mm, 3 mm and 4 mm without changing the lost circulation material formulation. The developed TS-LCM has good compatibility with local saltwater-based drilling fluid. In field tests in the Yan’an area of the Ordos Basin, 15 shale oil horizontal wells were plugged with excellent results. The equivalent circulating density of drilling fluid leakage increased by an average of 0.35 g/cm³, and the success rate of plugging malignant leakage increased from 32% to 82.5%. The drilling cycle was shortened by an average of 14.3%, and the effect of enhancing the pressure-bearing capacity of the well wall was significant. The prepared TS-LCM could cure fluid loss in a fractured formation efficiently. It has good prospects for promotion. Full article

Background/Objectives: In Spain, a high proportion of children do not meet the recommended daily levels of physical activity (PA), which highlights the urgent need to understand the motivational factors that could influence PA behavior. Self-Determination Theory is a widely used approach for assessing motivation toward exercise, employing instruments such as the Behavioral Regulation in Exercise Questionnaire (BREQ-3). However, despite the cognitive and linguistic differences that limit its direct application, this tool has not yet been adapted for children aged 6–12 years. This study aimed to adapt the BREQ-3 for use with Spanish schoolchildren and to evaluate its validity and reliability in this age group. Methods: The BREQ-3 for children (BREQ-3C) was linguistically and culturally adapted. Comprehension was tested through cognitive interviews, and reliability was assessed via a test–retest with 125 Spanish schoolchildren. Statistical analyses: Confirmatory factor analysis (CFA), Cronbach’s alpha, and the intraclass correlation coefficient (ICC) were used to evaluate validity and reliability. Results: CFA supported the factorial structure of the adapted BREQ-3 for primary schoolchildren, showing acceptable model fit indices (chi-square minimum discrepancy/degrees of freedom (CMIN/df) = 1.552, root mean square error of approximation (RMSEA) = 0.053, comparative fit index (CFI) = 0.891, Tucker-Lewis index (TLI) = 0.870). Internal consistency ranged from poor to excellent for all items and the total score of the questionnaire (Cronbach’s alpha (α): 0.535 to 0.911), except for items 3, 13, 20, and 21, where the internal consistency was unacceptable. Test–retest reliability was generally satisfactory, with ICC values indicating fair to excellent temporal stability (ICC: 0.248 to 0.911). The measurement error indicators (standard error of measurement percentage (SEM%) and minimal detectable change percentage (MDC%)) varied widely, particularly for the less reliable items. Most item scores were not significantly different between the test and retest groups, although items 2, 3, 5, 9, 17, 19, and 20 were significantly different. Conclusions: The BREQ-3C has promising psychometric properties for assessing exercise motivation in children aged 6–12 years. This tool shows potential for use in research, education, and health interventions to understand and promote physical activity motivation in primary schools. Full article

In this paper, we present a set of experimental data (SESD) from Capsicum annuum with two different pigmentations, obtained using a self-made computed tomography spectrometer (CTIS), which adapt to the optical model of radiative transfer. An optical model is based on the directional-hemispheric reflectance and transmittance of a turbid medium with plane-parallel layers. To estimate the fruit’s primary pigments (Chlorophyll, Carotenoids, Capsanthin, and Capsorubin), we use the optical model combined with a numerical search and optimization method based on a robust and efficient multi-objective genetic algorithm (GA), allowing us to find the closest solution to the global minimum; and the inverse problem is solved by obtaining the best fit of the analytical function defined in the SESD optical model. Values of pigment concentrations retrieved with the proposed GA show a total difference of 2.51% for green pepper and 5.60% for red pepper compared with those reported in the literature. Full article

Precise and robust localization for micro Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments is hindered by the lack of diverse datasets and the limited real-world performance of existing visual matching methods. To address these gaps, we introduce two contributions: (1) the SatLoc dataset, a new benchmark featuring synchronized, multi-source data from varied real-world scenarios tailored for UAV-to-satellite image matching, and (2) SatLoc-Fusion, a hierarchical localization framework. Our proposed pipeline integrates three complementary layers: absolute geo-localization via satellite imagery using DinoV2, high-frequency relative motion tracking from visual odometry with XFeat, and velocity estimation using optical flow. An adaptive fusion strategy dynamically weights the output of each layer based on real-time confidence metrics, ensuring an accurate and self-consistent state estimate. Deployed on a 6 TFLOPS edge computer, our system achieves real-time operation at over 2 Hz, with an absolute localization error below 15 m and effective trajectory coverage exceeding $90\%$, demonstrating state-of-the-art performance. The SatLoc dataset and fusion pipeline provide a robust and comprehensive baseline for advancing UAV navigation in challenging environments. Full article