Search Results (244)

Localized ¹H magnetic resonance spectroscopy (MRS) is a powerful tool in pre-clinical and clinical neurological research, offering non-invasive insight into neurochemical composition in localized brain regions. Zebrafish (Danio rerio) are increasingly being utilized as models in neurological disorder research, providing valuable insights into disease mechanisms. However, the small size of the zebrafish brain and limited MRS sensitivity at low magnetic fields hinder comprehensive neurochemical analysis of localized brain regions. Here, we investigate the potential of ultra-high-field (UHF) MR systems, particularly 28.2 T, for this purpose. This present study pioneers the application of localized ¹H spectroscopy in zebrafish brain at 28.2 T. Point resolved spectroscopy (PRESS) sequence parameters were optimized to reduce the impact of chemical shift displacement error and to enable molecular level information from distinct brain regions. Optimized parameters included gradient strength, excitation frequency, echo time, and voxel volume specifically targeting the 0–4.5 ppm chemical shift regions. Exceptionally high-resolution cerebral metabolite spectra were successfully acquired from localized regions of the zebrafish brain in voxels as small as 125 nL, allowing for the identification and quantification of major brain metabolites with remarkable spectral clarity, including lactate, myo-inositol, creatine, alanine, glutamate, glutamine, choline (phosphocholine + glycerol-phospho-choline), taurine, aspartate, N-acetylaspartyl-glutamate (NAAG), N-acetylaspartate (NAA), and γ-aminobutyric acid (GABA). The unprecedented spatial resolution achieved in a small model organism enabled detailed comparisons of the neurochemical composition across distinct zebrafish brain regions, including the forebrain, midbrain, and hindbrain. This level of precision opens exciting new opportunities to investigate how specific diseases in zebrafish models influence the neurochemical composition of specific brain areas. Full article

Soybean–corn intercropping in the hilly–mountainous regions of Southwest China poses unique challenges to mechanized harvesting because of complex topography and agronomic constraints. Addressing the soybean-harvesting bottleneck in these fields requires advanced sensing and perception rather than purely mechanical redesigns. Prior reviews emphasized flat-terrain machinery or single-crop systems, leaving a gap in sensor-centric solutions for intercropping on steep, irregular plots. This review analyzes how sensors enable the next generation of intelligent harvesters by linking field constraints to perception and control. We frame the core failures of conventional machines—instability, inconsistent cutting, and low efficiency—as perception problems driven by low pod height, severe slope effects, and header–row mismatches. From this perspective, we highlight five fronts: (1) terrain-profiling sensors integrated with adaptive headers; (2) IMUs and inclination sensors for chassis stability and traction on slopes; (3) multi-sensor fusion of LiDAR and machine vision with AI for crop identification, navigation, and obstacle avoidance; (4) vision and spectral sensing for selective harvesting and impurity pre-sorting; and (5) acoustic/vibration sensing for low-damage, high-efficiency threshing and cleaning. We conclude that compact, intelligent machinery powered by sensing, data fusion, and real-time control is essential, while acknowledging technological and socio-economic barriers to deployment. This review outlines a sensor-driven roadmap for sustainable, efficient soybean harvesting in challenging terrains. Full article

Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support. Full article

This study addresses the challenges in health monitoring and safety assessment of retaining structures by developing an innovative damage identification system based on the Frequency-Optimized Wavelet Packet Transform (FOWPT) algorithm. The system introduces the Impulse Response Function (IRF) and optimized energy feature characterization to achieve precise damage localization (error ≤ 5%) and quantitative severity assessment. Recognizing the limitations of traditional dynamic methods in explaining damage mechanisms and spatial specificity, this research proposes a Wavelet Packet–Curvature Fusion (WPCF) model that integrates dynamic response signals with static topographic features. Through experimental validation, the WPCF model demonstrates a strong spatial correlation between terrain curvature and damage indicators, enabling damage prediction based solely on topographic data. The results show that the fusion approach significantly improves the accuracy of damage diagnosis and facilitates a transition from post-diagnosis to pre-prediction, offering a reliable technical framework for the intelligent monitoring and maintenance of retaining structures. Full article

Alzheimer’s disease (AD) drug development faces persistent challenges from blood–brain barrier limitations and inadequate integration of medicinal chemistry considerations with computational predictions. We developed a comprehensive Central Nervous System (CNS)-focused network medicine framework integrating machine-learning-validated BBB penetration prediction (95.7% accuracy, 0.992 AUC-ROC), modality-specific tractability assessment, and transparent evidence classification to identify viable drug repurposing candidates. CNS-specific pre-filtering refined 24,474 DGIdb compounds to 8247 CNS-relevant drugs, analyzed through multi-dimensional network scoring and systematic pharmaceutical property assessment. Modality stratification generated separate rankings for small molecules (3667 candidates), peptides (73 candidates), and biologics (3 candidates), acknowledging distinct BBB penetration mechanisms. Analysis revealed 64.8% of small molecules achieving Class I (Highly Tractable) status, with 83.6% demonstrating favorable BBB penetration. Plerixafor emerged as the top-ranked small molecule (score: 1.170), while trofinetide achieved the highest peptide ranking (score: 1.387), though classified as speculative, pending AD-specific validation. Successful identification of the FDA-approved AD therapeutics memantine and donepezil among the top candidates validated the computational performance, while the predominance of mechanistic evidence classifications (86.7%) highlighted that network predictions represent hypothesis-generating tools requiring systematic experimental validation rather than definitive therapeutic recommendations. The framework bridges computational predictions with pharmaceutical development requirements, providing actionable prioritization for systematic preclinical investigation addressing AD intervention. Full article

Hurricane Maria crossed Puerto Rico with winds as high as 250 km/h, resulting in widespread damages and loss of weather station data, thus limiting direct weather measurements of wind variability. Here, we identified more than 155 million trees to estimate the distribution of wind speed over 9000 km² of land from island-wide LiDAR point clouds collected before and after the hurricane. The point clouds were classified and rasterized into the canopy height model to perform individual tree identification and perform change detection analysis. Individual trees’ stem diameter at breast height were estimated using a function between delineated crown and extracted canopy height, validated using the records from Puerto Rico’s Forest Inventory 2003. The results indicate that approximately 35.7% of trees broke at the stem (below the canopy center) and 28.5% above the canopy center. Furthermore, we back-calculated the critical wind speed, or the minimum speed to cause breakage, at individual tree level this was performed by applying a mechanical model using the estimated diameter at breast height, the extrapolated breakage height, and pre-Hurricane Maria canopy height. Individual trees were then aggregated at 115 km² cells to summarize the critical wind speed distribution of each cell, based on the percentage of stem breakage. A vertical wind profile analysis was then applied to derive the hurricane wind distribution using the mean hourly wind speed 10 m above the canopy center. The estimated wind speed ranges from 250 km/h in the southeast at the landfall to 100 km/h in the southwest parts of the islands. Comparison of the modeled wind speed with the wind gust readings at the few remaining NOAA stations support the use of tree breakages to model the distribution of hurricane wind speed when ground readings are sparse. Full article

As oil and gas exploration and development advance into deep and ultra-deep areas, kick accidents are becoming more frequent during drilling operations, posing a serious threat to construction safety. Traditional kick monitoring methods are limited in their multivariate coupling modeling. These models rely too heavily on single-feature weights, making them prone to misjudgment. Therefore, this paper proposes a drilling kick risk prediction method based on image modality. First, a sliding window mechanism is used to slice key drilling parameters in time series to extract multivariate data for continuous time periods. Second, data processing is performed to construct joint logging curve image samples. Then, classical CNN models such as VGG16 and ResNet are used to train and classify image samples; finally, the performance of the model on a number of indicators is evaluated and compared with different CNN and temporal neural network models. Finally, the model’s performance is evaluated across multiple metrics and compared with CNN and time series neural network models of different structures. Experimental results show that the image-based VGG16 model outperforms typical convolutional neural network models such as AlexNet, ResNet, and EfficientNet in overall performance, and significantly outperforms LSTM and GRU time series models in classification accuracy and comprehensive discriminative power. Compared to LSTM, the recall rate increased by 23.8% and the precision increased by 5.8%, demonstrating that its convolutional structure possesses stronger perception and discriminative capabilities in extracting local spatiotemporal features and recognizing patterns, enabling more accurate identification of kick risks. Furthermore, the pre-trained VGG16 model achieved an 8.69% improvement in accuracy compared to the custom VGG16 model, fully demonstrating the effectiveness and generalization advantages of transfer learning in small-sample engineering problems and providing feasibility support for model deployment and engineering applications. Full article

This study investigated the mechanical performance and durability of concretes produced with varying proportions of recycled coarse aggregate from construction and demolition waste (CDW), ranging from 0% to 100% replacement of natural coarse aggregate, using recycled aggregates derived from crushed concrete and mortar debris, characterized by lower density and high water absorption (~9%) compared to natural aggregates. A key contribution of this research lies in the inclusion of intermediate replacement levels (20%, 25%, 45%, 50%, and 65%), which are less explored in the literature and allow a more refined identification of performance thresholds. Fresh-state parameters (slump), axial compressive strength (7 and 28 days), total immersion water absorption, sorptivity, and chloride ion penetration depth (after 90 days of immersion in a 3.5% NaCl solution) were evaluated. The results indicate that, up to 50% CDW content, the concrete maintains slump (≥94 mm), characteristic strength (≥37.2 MPa at 28 days), and chloride penetration (≤14.1 mm) within the limits for moderate exposure conditions, in accordance with ABNT: NBR 6118. Water absorption doubled from 4.5% (0% CDW) to 9.5% (100% CDW), reflecting the higher porosity and adhered mortar on the recycled aggregate, which necessitates adjustments to the water–cement ratio and SSD pre-conditioning to preserve workability and minimize sorptivity. Concretes with more than 65% CDW exhibited chloride penetration depths exceeding 15 mm, potentially compromising durability without additional mitigation. The judicious incorporation of CDW, combined with optimized mix design practices and the use of supplementary cementitious materials (SCMs), demonstrates technical viability for reducing environmental impacts without significantly impairing the structural performance or service life of the concrete. Full article

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

Community detection is a fundamental task for understanding network structures, crucial for identifying groups of nodes with close connections. However, existing methods generally treat all connections in networks as equally important, overlooking the inherent inequality of connection strengths in social networks, and often require large quantities of labeled data. To address these challenges, we propose a few-shot community detection framework, Strong Triadic Closure Community Detection with Prompt (STC-CDP), which combines the Strong Triadic Closure (STC) principle, Graph Neural Networks, and prompt learning. The STC principle, derived from social network theory, states that if two nodes share strong connections with a third node, they are likely to be connected with each other. By incorporating STC constraints during the pre-training phase, STC-CDP can differentiate between strong and weak connections in networks, thereby more accurately capturing community structures. We design an innovative prompt learning mechanism that enables the model to extract key features from a small number of labeled communities and transfer them to the identification of unlabeled communities. Experiments on multiple real-world datasets demonstrate that STC-CDP significantly outperforms existing state-of-the-art methods under few-shot conditions, achieving higher F1 scores and Jaccard similarity particularly on Facebook, Amazon, and DBLP datasets. Our approach not only improves the precision of community detection but also provides new insights into understanding connection inequality in social networks. Full article

Acute kidney injury (AKI) is a frequent and severe complication in trauma patients, affecting up to 28% of intensive care unit (ICU) admissions and contributing significantly to morbidity, mortality, and long-term renal impairment. Trauma-related AKI (TRAKI) arises from diverse mechanisms, including hemorrhagic shock, ischemia–reperfusion injury, systemic inflammation, rhabdomyolysis, nephrotoxicity, and complex organ crosstalk involving the brain, lungs, and abdomen. Pathophysiologically, TRAKI involves early disruption of the glomerular filtration barrier, tubular epithelial injury, and renal microvascular dysfunction. Inflammatory cascades, oxidative stress, immune thrombosis, and maladaptive repair mechanisms mediate these injuries. Trauma-related rhabdomyolysis and exposure to contrast agents or nephrotoxic drugs further exacerbate renal stress, particularly in patients with pre-existing comorbidities. Traditional markers such as serum creatinine (sCr) are late indicators of kidney damage and lack specificity. Emerging structural and stress response biomarkers—such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin-18 (IL-18), C-C motif chemokine ligand 14 (CCL14), Dickkopf-3 (DKK3), and the U.S. Food and Drug Administration (FDA)-approved tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein 7 (TIMP-2 × IGFBP-7)—allow earlier detection of subclinical AKI and better predict progression and the need for renal replacement therapy. Together, functional indices like urinary sodium and fractional potassium excretion reflect early microcirculatory stress and add clinical value. In parallel, risk stratification tools, including the Renal Angina Index (RAI), the McMahon score, and the Haines model, enable the early identification of high-risk patients and help tailor nephroprotective strategies. Together, these biomarkers and risk models shift from passive AKI recognition to proactive, personalized management. A new paradigm that integrates biomarker-guided diagnostics and dynamic clinical scoring into trauma care promises to reduce AKI burden and improve renal outcomes in this critically ill population. Full article

Small-diameter lead-rubber bearings (LRBs) are widely employed in shaking table tests of isolated structures, particularly reinforced concrete base-isolated structures. Accurately determining their mechanical properties and identifying their restoring force model parameters are essential for seismic response analysis and numerical simulation of scaled models. In this study, quasi-static tests and shaking table tests were conducted to obtain the compression–shear hysteresis curves of LRBs under various loading amplitudes and frequencies, as well as the hysteresis curves under seismic wave excitation. The variation patterns of mechanical performance indicators were systematically analyzed. A parameter identification method was developed to determine the restoring force model of small-diameter LRBs using a genetic algorithm, and the effects of pre-yield stiffness and yield force of the isolation layer on structural response were investigated based on an equivalent two-degree-of-freedom model. By incorporating appropriately identified restoring force model parameters, a damping modeling method for the reinforced concrete high-rise over-track structures with an inter-story isolation system was proposed. The results indicate that, when the maximum bearing deformation reached 150% shear strain, the post-yield stiffness and horizontal equivalent stiffness under seismic excitation increased by 11.97% and 19.40%, respectively, compared with the compression–shear test results, while the equivalent damping ratio increased by 18.18%. Directly adopting mechanical parameters obtained from quasi-static tests would lead to an overestimation of the isolation layer displacement response. The discrepancies in the mechanical indicators of the small-diameter LRB between the theoretical hysteresis curve, obtained using the identified Bouc–Wen model parameters, and the compression–shear test results are less than 10%. In OpenSees, the seismic response of the scaled model can be accurately simulated by combining a segmented damping model with an isolation-layer hysteresis model in which the pre-yield stiffness is amplified by a factor of 1.15. Full article

Driven by the demand for cost-effective vehicle access, enhanced flexibility, and sustainable transportation practices, smart car-sharing has emerged as a prominent alternative to traditional vehicle rental systems. Leveraging the Internet of Vehicles (IoV) and wireless communication, these systems feature dynamic renter-vehicle mappings, enabling users to access any available vehicle rather than being restricted to a specific one pre-assigned by the service provider. However, many existing schemes in the IoV field conflate users and vehicles, complicating the identification and tracking of the vehicle’s actual driver. Moreover, most current authentication protocols rely on a strict, initial binding between a user and a vehicle, rendering them unsuitable for the dynamic nature of car-sharing environments. To address these challenges, we propose an enhanced certificateless signature scheme tailored for smart car-sharing. By employing a biometric fuzzy extractor and the Chinese Remainder Theorem, our scheme provides a fine-grained authentication mechanism that eliminates the need for local computations on the user’s side, meaning users do not require a smartphone or other digital device. Furthermore, our scheme introduces category identifiers to facilitate vehicle selection based on specific classes within car-sharing contexts. A formal security analysis demonstrates that our scheme is existentially unforgeable against adversaries under the random oracle model. Finally, a comprehensive evaluation shows that our proposed scheme achieves competitive performance in terms of computational and communication overhead while offering enhanced practical functionalities. Full article

Background and Objectives: Despite the potential benefits for the patient, aerospace interventions pose significant risks. Pre-hospital triage and patient transport are two essential elements for achieving an organized system of trauma. The advantages and disadvantages of using land transport from the scene of the accident to the trauma centers have been extensively studied, but there are gaps for air transport, and their exact level of efficiency is not known. Materials and Methods: The present study includes a total number of 77 patients, present at SMURD Galați air service for polytraumas caused by various mechanisms, with pluri-regional involvement. The identification of patients, as well as the selection of the most important anamnestic data, was performed after signing a confidentiality agreement; subsequently, all this information was introduced in centralized tables made in the statistical program IBM SPSS Statistics V24. Results: Out of the total of 77 polytraumatized patients who needed air transfer, an average age of 17.3 years will be noted, with a predominance of males in a 2:1 ratio. Most polytraumas are due to road accidents (74%) and patients with minimal tri-regional damage (51.4%). Conclusions: Taking into account the existing statistics in this research, it is important to implement prevention elements, designed based on the profile of the polytraumatized patient. Thus, accessing the most important characteristics of these patients can be an extremely important starting point in reducing the incidence of polytrauma or even patient deaths. Full article

Knowledge tracing, the computational modeling of student learning progression through sequential educational interactions, represents a critical component for adaptive learning systems and personalized education platforms. However, existing approaches face a fundamental trade-off between predictive accuracy and interpretability: deep sequence models excel at capturing complex temporal dependencies in student interaction data but lack transparency in their decision-making processes, while probabilistic graphical models provide interpretable causal relationships but struggle with the complexity of real-world educational sequences. We propose a hybrid architecture that integrates transformer-based sequence modeling with structured Bayesian causal networks to overcome this limitation. Our dual-pathway design employs a transformer encoder to capture complex temporal patterns in student interaction sequences, while a differentiable Bayesian network explicitly models prerequisite relationships between knowledge components. These pathways are unified through a cross-attention mechanism that enables bidirectional information flow between temporal representations and causal structures. We introduce a joint training objective that simultaneously optimizes sequence prediction accuracy and causal graph consistency, ensuring learned temporal patterns align with interpretable domain knowledge. The model undergoes pre-training on 3.2 million student–problem interactions from diverse MOOCs to establish foundational representations, followed by domain-specific fine-tuning. Comprehensive experiments across mathematics, computer science, and language learning demonstrate substantial improvements: 8.7% increase in AUC over state-of-the-art knowledge tracing models (0.847 vs. 0.779), 12.3% reduction in RMSE for performance prediction, and 89.2% accuracy in discovering expert-validated prerequisite relationships. The model achieves a 0.763 F1-score for early at-risk student identification, outperforming baselines by 15.4%. This work demonstrates that sophisticated temporal modeling and interpretable causal reasoning can be effectively unified for educational applications. Full article