Blast effects and energy transfer in near-ground explosions differ significantly from underground scenarios, particularly in terms of ground shock propagation and energy coupling mechanisms across various geological conditions. This study employs centrifuge modeling to simulate near-ground explosions in sandy soil, including surface explosions and airbursts. The focus was on blast-induced cratering, ground shock effects, and energy coupling in sandy foundations. Scaling laws for crater dimensions and ground shock parameters were established and validated based on experimental results. The "modeling of models" series showed good consistency in crater measurements, leading to an empirical formula for estimating crater radius in dry sand. For surface explosions, soil acceleration responses showed single peaks in the central zone (horizontal standoff distance < 0.6 m/(kg)1/3) and dual peaks in the near-surface zone (0.79–1.2 m/(kg)1/3) due to combined effects of direct and airburst-induced ground shock. Empirical methods were developed to predict peak acceleration distributions in sandy foundations. Utilizing crater measurements and ground shock propagation laws, a computational approach for evaluating energy transmission in soil foundations was proposed. The study also developed prediction curves for ground shock energy coupling coefficients with scaled blast depth/height, providing a unified model for both underground and near-ground explosions in sandy foundations. The research findings can enhance the methodologies for simulating blast effects and offer a scientific basis for optimizing weapon effectiveness and protective engineering design.

The underground silo has a wide range of applications in both civil and military engineering, and is vulnerable to intense loadings such as explosion in some special service scenarios. This study focuses on the dynamic response of underground concrete silo against adjacent buried explosion loads. Three groups of centrifuge model tests of buried explosion near the silo structure in dry sand are designed and conducted. The characteristic parameters of excavated cratering, blast loadings, and structure vibration are recorded in the tests, and the effect of charge DoB (depth of burial) and stand-off distance are analyzed. The distribution pattern of blast loadings on the silo front is investigated, and a general formula is derived to predict the peak blast overpressure along the silo front based on dimensional analysis and test results. The blast-induced structure vibration inside the silo is monitored, and the mechanism of interior structure motion under external explosion loadings is discussed. The time-frequency analysis of the interior acceleration response is conducted using the HHT (Hilbert-Huang Transform) method. The silo exhibits a high-frequency forced vibration pattern within the positive overpressure duration, whereafter falls into the low-frequency sinusoidal free vibration stage. The tolerance and fragility assessment of personnel and accessory equipment inside the silo is further performed based on the peak acceleration and shock response spectrum criteria. The results show that despite no apparent damage being observed on the concrete silo under the explosion conditions in this study (TNT equivalent of 1200 kg and stand-off distance close to 5.3 m in prototype), the blast-induced structure vibration would pose a significant threat to the interior personnel and precision instruments such as computers and communication devices. The research findings can benefit the prediction of blast loadings and dynamic response of concrete silos subjected to external explosion, and provide a robust experimental basis for underground protective engineering design.

In recent decades, researchers have struggled to develop computational models capable of simultaneously simulating the effects of underground explosions on both soil and structures. Traditional mesh-based methods, such as the finite element method (FEM), fail to achieve this due to challenges related to large deformations and discontinuities in soil and structures. To address this limitation, we develop a coupled peridynamics-smoothed particle hydrodynamics (PD-SPH) model, both based on meshfree particle methods. By introducing a robust data exchange algorithm between PD and SPH domains, incorporating a dynamic contact model for different materials in PD domains, and integrating a modified Drucker-Prager plasticity model for soil along with a rate-dependent damage model for concrete, the PD-SPH model effectively captures explosive gas-soil interactions, soil-structure interactions, large soil deformations, and rate-dependent damage and fracture processes in concrete structures. The effects of underground explosions on soil are then first modeled, demonstrating that the model successfully captures the severe soil ejections and excavation crater formations caused by shallow-buried explosions, as well as subsidence crater formations due to deep-buried explosions. Comprehensive qualitative and quantitative comparisons between PD-SPH simulations and centrifuge tests are provided, with errors below 15 %. In the meantime, the PD-SPH model can also accurately reproduce the damage effects of blast waves in soil on nearby concrete slabs and silo structures. The damage and fracture processes of these structures are analyzed and validated against experimental results. Furthermore, this study also explores the influence of nearby structures on soil ejection and cratering processes. The successful applications of the model to various explosion scenarios demonstrate that the developed PD-SPH model is capable of consistently and accurately capturing the effects of underground explosions on both soil and structures.

Electrocardiography (ECG) is a cornerstone of cardiac diagnostics, detecting cardiac pathologies ranging from arrhythmias to myocardial infarction. To enhance diagnostic accuracy and efficiency, deep learning models have been developed that now match or surpass human performance in ECG interpretation. However, their opaque reasoning hinders clinical trust and regulatory approval. This challenge is particularly acute for ECG signals because, unlike structured feature data, they are sequential, variable, and noise-prone, making interpretability both more difficult and more essential for clinical adoption. This review systematically evaluates ECG-specific explainable AI techniques using PRISMA guidelines. We screened 380 records across six databases and included 45 peer-reviewed studies examining diverse explainability methods including perturbation-based, gradient-based, intrinsically interpretable, sequence-aware, and counterfactual approaches. Our analysis reveals that perturbation-based techniques designed for structured data prove suboptimal for ECG signals because they treat features as independent rather than temporally dependent. In contrast, methods that transparently reveal model attention to physiologically meaningful ECG intervals such as the P wave, QRS complex, and ST segment demonstrate superior performance across localization accuracy, fidelity, and robustness metrics. While explainable AI in ECG interpretation has advanced substantially, it remains fragmented and insufficiently validated for clinical deployment. The most promising methods reveal what the model attends to in relation to known physiologic features, yet significant challenges persist in stability, computational efficiency, and regulatory readiness. Accelerating clinical translation requires open, multi-institutional benchmarks with cardiologist-annotated explanations and clinician-in-the-loop validation studies that can strengthen trust and support meaningful integration of explainable AI into patient care.

Genetic oscillators have been widely used in modeling key processes of biological systems, especially cell cycles and circadian rhythms. In particular, repressilatory genetic oscillators have been employed in modeling the dynamics of mRNA and protein interactions with transcriptional and translational feedback loops at the molecular level. In addition, synchronization of these oscillators is crucial for understanding the underlying mechanisms of the associated biological processes. In this paper, models of fractional-order genetic oscillators and their coupling are established, where the aspects of time delay, coupling strength, noise, and stability are all taken into consideration. Communication in the proposed coupling model is based on quorum sensing. The synchronization of the fractional-order repressilator model has been examined through simulations which show three main findings. Firstly, the synchronization of the fractional-order repressilator model can be optimized through coupling weight selection. Secondly, the synchronization can be enhanced by increasing the fractional order and decreasing the time delay and the noise intensity. Finally, transitions between the states of the fractional-order repressilatory oscillator can be achieved through varying the fractional order. The simulation results verify the biological relevance of the genetic oscillator models, and their potential for explaining the underlying mechanisms of the associated biological processes.

Biological experiments have confirmed that the locomotion system includes the nervous and musculoskeletal systems, and central pattern generator (CPG). The motor unit (MU) is the core part of the musculoskeletal system. Based on the biological experiments, it is confirmed that the CPG can generate and distribute the motor rhythms and patterns to different MUs through flexible weights, and the MUs feedback can affect the output of CPG. Then, the MU model, the CPG model, and their coupling model are investigated to explain the biological phenomena. Within this work, it was found that there is consistency between the shape of states generated by the three MUs in this work and the experimental results in previous studies. When the MUs correspond to the parts in limbs, and the CPG connects to the MUs via synapses, the locomotion pattern can be adjusted by CPG to use the variable synaptic weights. Besides, the model with fractional order (FO) has better efficiency than the model with integer order. Therefore, the model investigates the relationship between the CPG and the MU and provides a novel technique to introduce the FO coupling model for controlling the locomotion state.

To accurately calculate the shock-wave pressure curve of the underwater explosion of slender tapered charge, this research constructs a two-dimensional axisymmetric numerical model. In this foundation, this research further investigates the influence of the computational domain size, computational domain symmetry, gauge grid size, charge grid size, and biaxial grid uniformity on the shock-wave pressure curve. As a whole, this research reveals the following several findings. To begin with, increasing the size of the computational domain is helpful to weaken the curve oscillation, and so is maintaining the symmetry of the computational domain. Secondly, an excessive ratio of the number of two-dimensional grids, which is manifested by the non-uniformity of biaxial grids, leads to the grid being too slender at the boundary and the oscillation amplitude of the pressure curve increasing obviously. Furthermore, the gauge grid size exerts a notable influence on the peak pressure of the shock wave. Lastly, in the case of a fine grid covering the gauge, the charge grid size generates no obvious influence on the peak pressure.

Abstract To clarify the influence of the computational domain size on the underwater explosion bubble pulsation, a one-dimensional axisymmetric model of the underwater explosion was established. The computational model was verified using empirical formulas. Further research was conducted on the variation patterns of the bubble pulsation period and maximum radius with changes in the proportional computational domain radius. The results indicate that as the proportional computational domain radius increases, the maximum radius of the bubble and the bubble pulsation period gradually increase and stabilize. The proportional computational domain radius required to achieve stability is related to the static water pressure of the computational domain. Increasing static water pressure makes the required computational domain size for achieving high numerical accuracy smaller. When the proportional computational domain radius reaches around 1600, the calculation results are highly accurate under different static water pressures at different depths of explosion.

Background: The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system. Methods: In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli. Results and Conclusions: These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.

A series of centrifuge model tests of buried explosions in dry sand were designed and performed to investigate the blast effects in diverse underground explosion (UE) scenarios. The typical ground deformation pattern and blast wave propagation in different types of UE events were modelled. The excavated cratering was observed in shallow-buried excavation explosion tests. Surface collapse and subsidence craters were formed in the partially contained explosion tests, in which the initial ground motion direction turned from upward to downward with the increase of burial depth. The scaling law for centrifuge modelling of blast wave propagation was derived and examined. The critical burial depth for complete coupling of ground shock energy is within the range of 0.4 m/kg1/3 and 0.8 m/kg1/3, and the gravity effect on blast wave propagation can be disregarded under centrifugal accelerations of 62 g and 106 g (g is the Earth's gravity). The elastic blast wave velocity in dry sand was determined in the model test, and it increased with the burial depth. The attenuation laws for peak pressure and peak scaled acceleration in dry sand were calibrated, and the scaling coefficient for peak pressure estimation increased with the medium's acoustic impedance, while the attenuation coefficient showed a contrasting trend.

The central pattern generator (CPG) is a micro circuit in neural system and it can generate rhythmic signals to regulate locomotion. The researchers have investigated the features of the CPG, and they have paid more attentions to the the programmable characteristic. In this address, a new learnable CPG based on Wilson-Cowan oscillator is established. The sine signal, the complex signal, the chaotic signal and angles of compass-like robot are used as input to test the new programmable central pattern generator. The simulations present that the learnable CPG has the ability to learn different signals effectively. These results are the significant contribution to the research of the programmable CPG.

The wave velocity of Soil-Rock Mixture (SRM) is an important index for seismic effect analysis of natural SRM site and quality evaluation of artificial SRM project. In order to clarify the compression wave velocity determination mechanism of SRM, a ternary mesoscale model of SRM including soil matrix, rock blocks and Soil-Rock Interface (SRI) was established based on the "Divide & Fill" method and calculated with 2D FEM code. The contact stiffness model was used to characterize SRI, and three typical contact models were assumed: ideal contact, ITZ (Interfacial Transition Zone) contact and cracked contact. The calculation formula of the compression wave velocity of SRM under ideal contact condition was given. The influence mechanisms of ITZ and crack on wave velocity under non-ideal contact conditions were expounded and the multi-factor competition mechanism for the influence of macro parameters such as rock content on the wave velocity was further clarified. These conclusions were verified by the wave velocity test experiments of loess-sand mixture under different pressure conditions. Finally, the damage variable of SRM was calculated according to the compression wave velocity under ideal and actual SRI, and was proposed to be one of the indexes to evaluate the quality of SRM.

余弦分布载荷的化爆加载技术是高空核爆软X射线辐照下空间结构动态响应考核的主要手段.为适应新型空间飞行器结构考核的复杂构型、高同步性和低比冲量载荷设计要求,提出了一种用十字形超细药条离散群同步起爆实现超低比冲量加载的方法.实验结果验证表明:(1)所制作的十字形超细药条,最小截面尺寸为0.33 mm×0.5 mm,传爆性能稳定,并可通过直径0.5 mm的柔爆索直接起爆;(2)与相同布药密度的条状布药方式相比,布药空间均匀度提高了76.7％;(3)所采用的21点柔爆索同步起爆网络,起爆率达100％,起爆不同步性小于1 μs.进一步建立了离散片炸药加载数值计算模型,分析了离散片炸药群同步起爆加载的比冲量空间分布和匀化规律,将匀化过程分为扩散段、叠加段和均匀段3个阶段;对比了方形、十字形、短条形3种形状药片阵列的比冲量演化过程,发现十字形药片所需匀化距离最短、均匀度最高,仅需约0.8倍布药间距即可使比冲量均匀度偏差降至10％以下.

Background: In recent years, identifying players with injury risk through physical fitness assessment has become a hot topic in sports science research. Although practitioners have conducted many studies on the relationship between physical fitness and the likelihood of injury, the relationship between the two remains indeterminate. Consequently, this study utilized machine learning to preliminary investigate the relationship between individual physical fitness tests and injury risk, aiming to identify whether patterns of physical fitness change have an impact on injury risk.Methods: This study conducted a retrospective analysis by extracting the records of 17 young female basketball players from the sport-specific physical fitness monitoring and injury registration database in Fujian Province. Sports-specific physical fitness tests included physical performance, physiological, biochemical, and subjective perceived responses. The data for each player was standardized individually using Z-scores. Synthetic minority over-sampling techniques and edited nearest neighbor algorithms were used to sample the training set to address the negative impact of class imbalance on model performance. Feature extraction was performed on the dataset using linear discriminant analysis, and the prediction model was constructed using the cost-sensitive neural network.Results: The 10 replicate 5-fold stratified cross-validation showed that the lower limb non-contact injury prediction model based on the cost-sensitive neural network had achieved good discrimination and calibration (average Precision: 0.6360; average Recall: 0.8700; average F2-Score: 0.7980; average AUC: 0.8590; average Brier-score: 0.1020), which could be well applied in training practice. According to the attribution analysis, agility and speed were important physical attributes that affect youth female basketball players’ non-contact lower limb injury risk. Specifically, there was enhance in the performance of the 1-min double under, accompanied by an increase in urinary ketone and urinary blood levels following the agility test. The 3/4 basketball court sprint performance improved, while urinary protein and RPE levels decreased after the speed test.Conclusion: The sport-specific physical fitness change pattern can impact the lower limb non-contact injury risk of young female basketball players in Fujian Province, specifically in terms of agility and speed. These findings will provide valuable insights for planning athletes’ physical training programs, managing fatigue, and preventing injuries.

Centrifuge model tests are conducted to investigate the dynamic response of dry sand under blast loading. The characteristics and propagation mode of blast waves in dry sand are studied. The Coriolis effect on blast-induced cratering is carefully scrutinised, and both the theoretical and experimental results are provided and agree with each other. In the explosion-induced cratering process, the sand ejecta is subjected to horizontal and vertical Coriolis forces simultaneously; the former directly determines the horizontal motion offset, while the latter affects the particle motion by altering the flight time, and the Coriolis effect on cratering can only be observed apparently for soil ejecta with a relatively small launch angle. Redistribution of the static earth pressure (blast-induced arching effect) in deep-buried, fully confined explosion events under hypergravity is observed. The friction between sand particles is significantly enhanced by the hypergravity to serve as the supporting arch springing. Conceptual analysis is conducted to further reveal the mechanism of the blast-induced arching effect based on the trapdoor test, from the three aspects of displacement mode, stress development and post-detonation stress distribution.

High myopia has long been highly prevalent worldwide with a largely yet unexplained genetic contribution. To identify novel susceptibility genes for axial length (AL) in highly myopic eyes, a genome-wide association study (GWAS) was performed using the genomic dataset of 350 deep whole-genome sequencing data from highly myopic patients. Top single nucleotide polymorphisms (SNPs) were functionally annotated. Immunofluorescence staining, quantitative polymerase chain reaction, and western blot were performed using neural retina of form-deprived myopic mice. Enrichment analyses were further performed. We identified the four top SNPs and found that ADAM Metallopeptidase With Thrombospondin Type 1 Motif 16 ( ADAMTS16 ) and Phosphatidylinositol Glycan Anchor Biosynthesis Class Z ( PIGZ ) had the potential of clinical significance. Animal experiments confirmed that PIGZ expression could be observed and showed higher expression level in form-deprived mice, especially in the ganglion cell layer. The messenger RNA (mRNA) levels of both ADAMTS16 and PIGZ were significantly higher in the neural retina of form-deprived eyes ( p = 0.005 and 0.007 respectively), and both proteins showed significantly upregulated expression in the neural retina of deprived eyes ( p = 0.004 and 0.042, respectively). Enrichment analysis revealed a significant role of cellular adhesion and signal transduction in AL, and also several AL-related pathways including circadian entrainment and inflammatory mediator regulation of transient receptor potential channels were proposed. In conclusion, the current study identified four novel SNPs associated with AL in highly myopic eyes and confirmed that the expression of ADAMTS16 and PIGZ was significantly upregulated in neural retina of deprived eyes. Enrichment analyses provided novel insight into the etiology of high myopia and opened avenues for future research interest.

The electromagnetic particle velocity measurement technology is an important measurement method in the study of stress wave propagation. This article analyzes the problem that the pulsed-magnetic-field-based electromagnetic particle velocity measurement technology will introduce additional induced electromotive force and proposes an electromagnetic particle velocity measurement technology based on superconducting magnet. After considering the factors of electromagnetism, structure, and heat distribution, a superconducting magnet with 500-mm room-temperature bore was constructed, and the proposed electromagnetic particle velocity measurement system based on the superconducting magnet was designed subsequently. The experiment of filled explosion in polymethyl methacrylate (PMMA) was carried out, and high-quality particle velocity and displacement signals were obtained, which verified the measurement performance of the system. The proposed method can provide a more powerful test means for the study of stress wave propagation.

There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia detection, and to the classification performance, we propose a 12-lead electrocardiogram signal automatic classification model based on model fusion (CBi-DF-XGBoost) to focus on representative features along both the spatial and temporal axes. The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. Finally, eXtreme Gradient Boosting (XGBoost) is used to fuse the 12-lead models and domain-specific features to obtain the classification results. The 5-fold cross-validation results show that in classifying nine categories of electrocardiogram signals, the macro-average accuracy of the fusion model is 0.968, the macro-average recall rate is 0.814, the macro-average precision is 0.857, the macro-average F1 score is 0.825, and the micro-average area under the curve is 0.919. Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. We also conducted ablation studies to verify the effect of the complementary information from the 12 leads and the auxiliary information of domain-specific features on the classification performance of the model. We demonstrated the feasibility and effectiveness of the XGBoost-based fusion model to classify 12-lead electrocardiogram records into nine common heart rhythms. These findings may have clinical importance for the early diagnosis of arrhythmia and incite further research. In addition, the proposed multichannel feature fusion algorithm can be applied to other similar physiological signal analyses and processing.

A two-dimensional axisymmetric model of the underwater explosion is established in this paper. After verification by empirical formula, the variation law of the reflection coefficient of the shock wave with different boundary sizes under different boundary conditions is studied. The results show that the peak pressure of the near-field shock wave is relatively close to the empirical formula. The calculated value of the far-field shock wave peak pressure is smaller than the empirical value, and refining the mesh can make the near-field shock wave peak pressure closer to Cole’s empirical formula; when the “Flow-out” boundary condition is set, with the increase of the proportional boundary size, the boundary pressure decreases and the reflection coefficient increases. When the proportional boundary size reaches 5, the reflection coefficient of the boundary is the same as that without the “Flow-out” boundary, which is close to 90%.

PURPOSE:To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCS III).SETTING:Eye and Ear, Nose, and Throat Hospital, Fudan University, Shanghai, China.DESIGN:AI training.METHODS:Advanced deep-learning algorithms, including Faster R-CNN and ResNet, were applied to the localization and analysis of the region of interest. An internal dataset from the EENT Hospital of Fudan University and an external dataset from the Pujiang Eye Study were used for AI training, validation, and testing. The datasets were automatically labeled on the AI platform regarding the capture mode and cataract grading based on the LOCS III.RESULTS:The AI program showed reliable capture mode recognition, grading, and referral capability for nuclear and cortical cataract grading. In the internal and external datasets, 99.4% and 100% of automatic nuclear grading, respectively, had an absolute prediction error of ≤1.0, with a satisfactory referral capability (area under the curve [AUC]: 0.983 for the internal dataset; 0.977 for the external dataset); 75.0% (internal dataset) and 93.5% (external dataset) of the automatic cortical grades had an absolute prediction error of ≤1.0, with AUCs of 0.855 and 0.795 for referral, respectively. Good consistency was observed between automatic and manual grading when both nuclear and cortical cataracts were evaluated. However, automatic grading of posterior subcapsular cataracts was impractical.CONCLUSIONS:The AI program proposed in this study showed robust grading and diagnostic performance for both nuclear and cortical cataracts, based on LOCS III.