Search Results (476)

The standard first-level trigger in the Pierre Auger Observatory surface detectors (data analysis in FPGAs immediately after digitization in ADCs) was developed when FPGAs were relatively simple and expensive. Thus, the algorithms developed in the 1990s are relatively simple. Substantial progress in electronics now allows the implementation of very sophisticated mathematical algorithms in very efficient systems and relatively inexpensive FPGAs. A neural network was recently developed as an alternative trigger for recognizing neutrino-induced showers, providing relatively high efficiency and allowing signal profiles from Auger photomultiplier tubes of water-Cherenkov detectors originating from atmospheric showers induced by high background neutrinos to be distinguished from other showers. The chemical composition of ultra-high-energy cosmic rays (UHECR) is complex and still not fully known. Additional tools for online, real-time analysis of potential chemical composition could help address this problem. We simulated a large dataset using the CORSIKA package (for simulating the development of extensive air showers in the atmosphere) and OffLine (for generating Cherenkov radiation in surface detectors and digitizing photomultiplier signals in an analog-to-digital converter). These data served as input to a neural network (using MATLAB tools) that attempted to identify the type of initiating particle. Ultimately, the neural network was implemented on an Arria 10 FPGA to generate real-time neural network triggers directly on the pampas in the surface detector. Both simulations and measurements on the Arria 10 development kit confirmed a high degree of reliability. Full article

Neural tube defects (NTD) are congenital malformations that lead to structural abnormalities of the brain or spine. Mouse embryos deficient in Scavenger Receptor Class B Type 1 (SR-B1 KO), the main receptor for high-density lipoproteins, exhibit a high incidence of anterior NTD, which is associated with vitamin E deficiency and elevated levels of reactive oxygen species (ROS). Maternal supplementation with vitamin E, a micronutrient with antioxidant properties, completely prevents the occurrence of NTD and normalizes ROS levels in SR-B1 KO embryos, suggesting a contribution of oxidative stress to NTD in this model. In this work, we showed that SR-B1 KO embryos at gestational day E9.5 display higher levels of lipoperoxidative damage markers. Analysis of data obtained through shotgun lipidomics evidenced a selective and coordinated reorganization of fatty acid distribution, characterized by altered polyunsaturated and monounsaturated composition, together with reduced phosphatidylcholine and increased lysophosphatidylcholine levels, and diversion of fatty acids into triacylglyceride storage. Transcriptomic analysis revealed a coordinated upregulation of genes involved in phospholipid synthesis and remodeling, consistent with the altered lipid homeostasis observed in SR-B1 KO embryos. Together, these results provide novel information showing a potential link between oxidative stress and disruptions in mammalian embryonic lipid metabolism, highlighting phospholipid remodeling as a potential determinant of susceptibility to NTD. Full article

An encephalocele is a rare congenital or acquired cranial defect characterized by herniation of intracranial tissue through a defect in the skull base. In human and veterinary medicine, these lesions are frequently associated with abnormalities in neural tube development or structural weakness of the cranial bones, resulting in the protrusion of brain tissue and meninges through anatomical openings such as the cribriform plate. Although this condition has been extensively described in human neurosurgical research, reports on dogs remain limited, and the clinical significance of surgical intervention in cases with communication to the nasal cavity remains unclear. In this case, a young American Cocker Spaniel presented with seizures, prompting advanced diagnostic evaluation. Magnetic resonance imaging revealed a protrusion of the intracranial tissue through a defect in the cribriform plate extending into the nasal cavity. Surgical resection of the protruding tissue was performed, followed by skull base reconstruction. Histopathological examination demonstrated nervous tissue with chronic inflammatory changes without evidence of neoplasia. The patient recovered uneventfully after surgery and remained free of seizure recurrence during follow-up. Surgical management may represent a viable treatment option for seizure disorders in young dogs, particularly when persistent cranio-nasal communication is present, and provides a clinically relevant comparative model for similar cranial base defects described in human pathology. Full article

In this paper, an intelligent localization framework based on deep learning is proposed to address the limitations of insufficient accuracy and robustness in defect identification and localization during the ultrasonic guided-wave non-destructive testing (NDT) of receiver tubes in tower-type molten salt Concentrated Solar Power (CSP) stations. In the proposed method, a 1D convolutional neural network (1D-CNN) initially processes raw time-series-guided wave signals, achieving coarse identification and preliminary localization of defective segments. Then, Mel spectrograms are employed to exploit multi-dimensional features in the time–frequency domain and transform 1D signals into 2D representations, thereby enriching feature diversity. A regression-based 2D-CNN was designed to predict the start and end points of defect segments, enabling precise interval localization. Furthermore, the Tree Seed Algorithm (TSA) was integrated to jointly optimize key hyperparameters, enhancing training efficiency and prediction accuracy. Experimental validation on a dataset of ultrasonic guided-wave signals from molten salt receiver tubes demonstrates that the TSA-optimized Mel+2D-CNN model achieves superior performance, with a Mean Absolute Error (MAE) of 75.11 sampling points and a Coefficient of Determination (R²) of 0.90. At an Intersection over Union (IoU) threshold of 0.3, the model achieves a hit rate of 89.21%, exhibiting significantly higher localization accuracy and stability compared to the 1D-CNN baseline model. These findings indicate that the proposed method effectively enhances the accuracy and robustness of guided wave-based defect localization in slender structures. While promising, the model’s generalization capability remains dependent on the data distribution and operating conditions; future work will focus on validating its engineering applicability across diverse, multi-scenario industrial environments. Full article

The phenomenon of CO₂ corrosion of downhole tubing is widespread in oil and gas extraction. Currently, there is a lack of applicable prediction methods for the corrosion rates of different metal tubing in the liquid phase CO₂ environment. To address this issue, this paper systematically investigates the anti-corrosion mechanisms and influencing factors of different metal casings and proposes a deep learning model combining convolutional neural networks and long short-term memory networks. Based on laboratory corrosion experimental data, the model extracts spatial features of parameters affecting the corrosion rate through CNN and captures their temporal dependencies through LSTM. This paper builds a pipe corrosion rate prediction model based on the TensorFlow framework and compares the prediction results with those of the traditional D-W empirical model and the SRV machine learning model. The results showed that the CNN-LSTM model maintained high prediction accuracy regardless of high or low chromium content, with R² reaching 0.83 and 0.94 respectively, solving the problem that existing models have difficulty effectively simulating complex corrosion behavior under flowing corrosive media conditions. The model was verified using the remaining wall thickness of the actual application casing in the field, and the accuracy was over 80%. The established prediction method can be extended to predict the corrosion rate of pipes under similar corrosion conditions. Full article

The pitot tube is the core sensor for aircraft to obtain external atmospheric data, and its failure has a very important impact on flight safety. However, as its structure and principle are relatively simple, all manufacturers have not adopted available monitoring methods for its health status due to the perspective of cost and complexity reduction. The pitot tube fault warning method is conducted in this paper with a fully connected neural network (FCNN) method based on the data collected by the pitot tube itself. By constructing and selecting parameters and extracting fault features from flight record data, a pitot tube fault warning model based on an FCNN is constructed. The effectiveness of the proposed method is verified through pitot tube fault warning experiments based on actual flight record data, which can provide technical reference for pitot tube fault warning during aircraft route operation in the future. Full article

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

Objective: Increased nuchal translucency (NT) thickness at 10–14 weeks’ gestation is a well-established marker of chromosomal abnormalities, foetal structural defects, genetic syndromes, and foetal death; however, its association with foetal central nervous system (CNS) abnormalities has not been systematically evaluated. This study aimed to review and synthesise existing evidence on the relationship between first-trimester increased NT and prenatal ultrasound–detected foetal CNS abnormalities. Methods: A systematic literature search of MEDLINE, Embase, and CINAHL was conducted in accordance with PRISMA guidelines and registered in PROSPERO. Studies reporting increased NT in singleton pregnancies and structural abnormalities of the foetal CNS identified on prenatal ultrasound were included. Study selection, data extraction, and quality assessment were performed independently by two reviewers. Results: Twenty-three studies, including 15,592 euploid pregnancies with increased NT, met the inclusion criteria. Definitions of increased NT varied across studies, most commonly >95th centile or ≥3.5 mm. The pooled prevalence of CNS anomalies was 1.16% (95% CI 0.68–1.95; I² = 80%). In three comparative studies including 6040 pregnancies with increased NT and 152,682 with normal NT, increased NT was associated with higher odds of CNS anomalies (OR 3.22, 95% CI 1.52–6.80; I² = 74.1%). Conclusions: These findings suggest that euploid foetuses with increased NT may have a higher risk of CNS abnormalities. Full article

When ground transportation is disrupted by natural disasters, airdropped rescue vehicles require energy-absorbing cushioning devices to prevent landing impact damage. Thin-walled circular tubes are preferred for their high energy absorption capacity and structural efficiency. However, to reduce platform force fluctuations and decrease residual stroke after compression, thereby avoiding unbalanced loading and ensuring post-landing mobility, slots are introduced into the tube wall, which renders the mean crushing force (MCF) difficult to predict accurately using conventional methods. To address this issue, this paper proposes a physics–data-driven method for predicting the energy absorption characteristics of slotted thin-walled circular tubes. The engineering scenario is introduced, followed by comparative validation via drop weight tests and impact simulations to obtain a sample set via design of experiments (DOE). A multi-layer perceptron (MLP) neural network then augments the samples to generate a dataset. Dimensional analysis yields candidate MCF prediction equations, whose forms and coefficients are determined via a physics–data-driven approach. Weighted graph encoding transforms the equation-solving problem into a graph optimization problem to reduce the computational complexity, and an improved differential evolution (DE) algorithm with a dual-adaptive mutation operator (DSADE) adjusts the parameters and accelerates convergence. The resulting MCF prediction formula, combined with drop test requirements as the optimization objective, achieves a simulation relative error below 5%. These parameters also satisfy engineering requirements in actual airdrop tests, confirming the method’s effectiveness in predicting the energy absorption characteristics of slotted thin-walled tubes. Full article

Background: Etiologies of neural tube defects (NTDs) are multifactorial. Genetic, epigenetic and environmental factors may contribute to their reported variation in prevalence across the globe. Ethiopia has among the highest reported NTD prevalence globally, making investigation of genetic determinants in this high-risk population particularly important for advancing the understanding of NTD etiology. Genes involved in folate metabolism, such as the reduced folate carrier 1 (RFC1), have been investigated for the potential associations with NTDs, but findings throughout the literature remain inconsistent and inconclusive. Objective: The aim of this study was to determine an association of RFC-1 polymorphism at rs1131596 and rs1051266 loci (functional variants previously implicated in folate transport efficiency and NTD susceptibility) among mothers with the occurrence of NTDs in their offspring in Ethiopia. Methods: A case–control study involving 250 mothers (187 controls and 63 cases) of children with or without NTDs was conducted in Addis Ababa, Ethiopia from April 2022, to September 2024. A total of 250 maternal whole blood samples were systematically collected and subjected to genetic analysis at loci rs1131596 and rs1051266 by polymerase chain reaction (PCR) and Sanger sequencing. Results: Detection of heterozygous (TC) and homozygous (CC) genotypes for SNP rs1131596 (−43T>C) in the RFC1 gene was 27.2%, with heterozygous (TC) comprising 10.4% and homozygous (CC) 16.8%. In contrast, for the rs1051266 (80A>G), the prevalence of the AG polymorphism was 28% while the GG polymorphism was 16.4%, resulting in a cumulative prevalence of 44.4%. The presence of maternal RFC-1 polymorphism at these two locations were not associated with significantly (p = 0.601 & p = 0.225 respectively) higher odds for NTD births. Conclusions: This study did not reveal significant association between maternal RFC1 gene polymorphisms and NTD-affected births. Comprehensive whole-genome sequencing of affected off-spring is essential to identify specific mutations or polymorphisms that may individually or collaboratively affect the risk of NTDs in the Ethiopian context. Full article

Automated detection of suspicious human activities in complex and crowded environments remains a critical challenge in modern surveillance systems due to high false-positive rates, poor contrast and generalization across diverse scenes. We propose a GM_CNN3D Model for the classification of suspicious activity based on a Deep Fused Feature Block (DFFB) framework that integrates handcrafted spatial descriptors (PCA-HOG and Motion-HOG) with deep spatiotemporal features extracted from 3D Convolution Neural Network (3D-CNN). Motion regions are first localized using a Gaussian Mixture Model (GMM), after which handcrafted and deep features are concatenated in a dimensionality-normalized fusion stage, followed by a fully connected layer and softmax classification. The system is evaluated on five diverse and publicly available datasets: Violent Crowd, Hockey Fight, Kaggle Fight, Movies Fight, and Custom Annotated YouTube Clips, achieving up to 99.12% accuracy, 98.7% F1-score, and a ROC-AUC of 0.992, outperforming state-of-the-art CNN, LSTM, and SlowFast models. All datasets include real world scenarios with varying lighting, crowd density, and camera viewpoints, with annotations created manually where unavailable. The proposed method demonstrates robust cross-scene performance, enabling automated alarming and reduced false positives in real-time security operations. Full article

Decarbonizing downstream steel logistics remains underexplored in sustainable supply chain management. This study proposes a machine learning-enhanced tri-objective optimization framework for the ship stowage planning problem (SSPP). The framework handles heterogeneous finished steel products, including coils, plates, ingots, tubes, and sections. The model simultaneously maximizes deadweight utilization and minimizes a novel Adaptive Weighted Moment Balance (AWMB) index. It also minimizes voyage carbon emissions through a trim-and-heel resistance penalty. A spatial-to-sequential discretization strategy transforms the NP-hard placement problem into a tractable permutation optimization. A deep neural network (DNN) surrogate achieves a 3.57-fold speedup with only 1.52% hypervolume degradation. An improved NSGA-III algorithm with adaptive operators ensures Pareto front exploration. Embedded step-wise moment verification guarantees dynamic stability throughout loading and unloading. Validated on real data from a Chinese steel enterprise, the framework achieves 99.88% deadweight utilization, reduces transverse and longitudinal imbalance by 48.27% and 90.54%, and cuts CO₂ emissions by 95.5% per voyage. SOLAS constraints, load line limits, and CII/FuelEU targets are addressed through embedded stability and capacity constraints. Multi-route and weather-dependent validation remains necessary before fleet-scale deployment. Full article

In polar environments, icing on ship deck surfaces severely compromises navigation safety. Conventional electric trace heating systems operate in continuous heating mode, resulting in high energy consumption. This study proposes an intelligent periodic heating control method that integrates physics-informed neural networks (PINNs) and deep reinforcement learning (DRL) for energy-efficient anti-icing of circular pipe components on polar vessels. Using a polar coupled environment simulation platform, experiments were conducted on electric heating anti-icing for circular pipe components. Temperature data under various heating modes were collected, and a physically constrained PINN temperature prediction model was constructed, achieving high prediction accuracy with limited samples (test set R² = 0.9091; 5-fold cross-validation R² = 0.8877 ± 0.0312). The DRL agent trained in this virtual environment autonomously optimized the heating strategy, yielding optimal cycle parameters: heating ratio D = 0.722 and cycle duration τ = 88 s. While maintaining surface temperatures above 0 °C against a −10 °C ambient baseline, this strategy achieved a unit energy consumption of 0.27 kJ/°C, representing a 63% reduction compared to conventional continuous heating. This study provides a data-physics fusion control approach for polar vessel anti-icing systems, demonstrating strong potential for engineering applications. Full article

The shrinkage deformation is a significant risk to self-compacting concrete (SCC)-filled steel tube structures. It was essential to understand the concrete autogenous shrinkage strain before being regulated in order to determine compensation shrinkage measures. In this study, A PSO-BPNN model was constructed, which is based on the Particle Swarm Optimization-Back Propagation Neural Networks (PSO-BPNN), and the autogenous shrinkage strain of SCC was predicted based on PSO-BPNN before being regulated. Moreover, some experiments about compensating for shrinkage by expansion and by a combination of expansion and contraction were investigated. Based on this prediction, a series of experiments was conducted on the regulation of the shrinkage deformation of SCC for an actual bridge project. The results indicated that a good consistency of PSO-BPNN between predicted and measured values, demonstrating that PSO-BPNN is a model with high accuracy in predicting concrete autogenous shrinkage strain before regulation, and as a guidance for regulation to compensate for shrinkage. The prediction error was less than 10% for 28-day self-shrinkage, and the experimental workload was reduced. The PSO-BPNN is a convenient tool for predicting the shrinkage of SCC, enabling the determination of dosages of expansion agent and reducing shrinkage agent to achieve SCC’s shrinkage regulation. Full article

In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination $\left(R^2\right)$ are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and $R^2$ values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems. Full article