Search Results (26,008)

Cylindrical charges are widely used in engineering blasting, yet the three-dimensional propagation mechanism of the associated stress waves remains inadequately understood. This study aims to investigate the effects of key wave source parameters on stress wave propagation and rock damage in cylindrical charge blasting. A semi-analytical solution for spherical stress wave propagation in a full elastic space is developed to theoretically describe the stress field, and a computational model for cylindrical charges is established based on the superposition principle of equivalent spherical charges. Numerical simulations using the RHT constitutive model are then performed to verify the theoretical predictions and further investigate stress wave propagation and rock damage. The results show that the attenuation index of radial stress decreases from 1.5 to 1 as the loading rate increases. Higher loading rates produce more but shorter cracks, whereas lower rates result in fewer but longer cracks. The blast-induced damage region shifts from the detonation direction toward the horizontal plane with increasing detonation velocity, and the resulting rock damage exhibits a conical distribution controlled by the initiation point. These findings provide practical guidance for optimizing cylindrical charge blasting and controlling crack patterns in engineering applications. Full article

Expanded polystyrene (EPS) concrete, with excellent properties such as light weight, thermal insulation, and soundproofing, is widely applied in construction engineering. However, its complex heterogeneous internal structure makes it difficult to quickly and accurately assess compressive strength. Existing testing methods struggle to meet the real-time demands of on-site quality control in terms of both operational efficiency and accuracy. To address this, the present study proposes a method for predicting the compressive strength of EPS concrete based on image processing and Deep Convolutional Neural Networks (DCNN). By constructing a dataset consisting of 5600 preprocessed concrete slice images and addressing the issue of parameter redundancy in fully connected layers, the Broad Learning System (BLS) was employed to reconstruct and optimize the network architecture, thereby improving computational efficiency and enhancing prediction accuracy. The experimental results indicate that after introducing the BLS and related training optimization mechanisms, the training time was reduced by approximately 15%. Among all models, the BLS-Xception model performed the best, requiring only 1.9 s per training image. The coefficient of determination (R²) on the test set reached 0.95, representing an 18.7% improvement over traditional models. The study also indicates that the appropriate incorporation of coal ash, silica fume, and mineral powder significantly enhances the compressive strength of EPS concrete, with smaller EPS particles contributing more substantially to strength improvement. The model demonstrates excellent accuracy and reliability in predictions, providing an effective method for the rapid, non-destructive evaluation of the compressive strength of EPS concrete on construction sites. Full article

Mutants with a bright green appearance due to wax synthesis or deposition defects have been reported in various plants such as Arabidopsis thaliana, corn, and rice, but they are relatively rare in broccoli (a brassicaceae crop). Here, we describe SY03, a natural mutant of broccoli with a glossy green phenotype owing to epidermal wax deficiency. Genetic analysis indicated that the leaf luster trait of SY03 was controlled by a single recessive gene. By using the F₂ generation and combining bulked segregant analysis and molecular marker techniques, the candidate gene BoFAR3a, homologous to the Arabidopsis FAR gene, was identified within a 96.678 kb interval of chromosome C01. The A→G point mutation in exon 1 of the BoFAR3a coding sequence substitutes the canonical ATG start codon with GTG, which is predicted to abrogate or severely reduce translation initiation. RT-qPCR indicated that the expression levels of BoFAR3a were significantly decreased in the leaves of the glossy green phenotype mutant. Heterologous expression of BoFAR3a in A. thaliana restored the phenotype of A. thaliana mutant FAR3. The discovery of BoFAR3a is of great significance for breeding lustrous and commercially appealing broccoli varieties. This study systematically analyzed the molecular basis of the lustrous green phenotype in broccoli, providing new insights into the epidermal waxy regulatory network of cruciferous crops. In the future, the wax synthesis pathway can be precisely improved through gene editing technology, achieving a coordinated enhancement of the appearance quality and stress resistance of broccoli. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

Focusing on the construction of a 58-m-diameter double-layer steel space frame dome at the Han Culture Museum Assembly Hall, this study addresses scheme selection and safety control challenges in staggered jacking of large-span spatial structures. A three-dimensional finite element model in MIDAS Gen simulated the three-stage jacking process to compare three temporary support layouts. Numerical evaluation metrics included maximum vertical displacements, peak internal forces, the proportion of members undergoing stress state transitions, and spatio-temporal evolution of stress concentrations. Scheme B demonstrated superior performance, reducing peak vertical displacement by 44% under critical conditions, lowering peak stresses, and enabling more uniform internal force redistribution—effectively mitigating tension–compression cycling and buckling risks. Crucially, only nodal displacements and support elevations were monitored in situ using a 3D system based on magnetic prisms and total stations; no strain or force measurements were conducted due to practical constraints during construction. Monitoring data show good agreement with simulated displacements and support elevations under Scheme B, validating the model’s deformation response. However, localized deviations—including a 29 mm deflection discrepancy and elevation errors up to 28 mm—reveal the influence of uneven boundary conditions, with potential implications for long-term structural behavior. The findings confirm that numerical predictions of deformation are reliable, while internal forces remain unvalidated by field data. The integrated approach of “scheme comparison–construction simulation–full-process displacement monitoring” proves effective for safety control and decision-making in complex jacking operations, offering a transferable framework for similar large-span double-layer space frame projects. Full article

This study aims to assess the evolution of land cover in the Lam Phra Phloeng (LPP) watershed and predict future land use patterns. By employing the Gray Level Co-occurrence Matrix (GLCM) and several spectral indices, high classification accuracy (>92%) was achieved using the Random Forest (RF) algorithm. Based on classified land use maps from 2003 and 2023, future land use predictions for 2030, and 2050 were generated using the CA-Markov chain model. The predictions suggest a gradual trend toward deforestation and the expansion of croplands, driven by population growth and increased anthropogenic activity in the region. The Sediment Delivery Ratio (SDR) model, part of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite, was used to simulate soil loss in the LPP watershed. The results indicate minimal soil loss in vegetated areas and significant erosion in regions adjacent to water bodies, primarily due to rainfall erosivity. This research highlights the social, ecological, and economic implications of land use change. Furthermore, best management practices (BMPs) are identified as effective strategies for land restoration and erosion reduction. The study also discusses three widely adopted soil erosion control techniques, providing recommendations for reforestation and erosion mitigation programmes. Full article

Background: Obstructive sleep apnea (OSA) is associated with slower response speed, yet conventional severity classification based on the apnea–hypopnea index (AHI) shows limited ability to predict cognitive outcomes. The AHI aggregates distinct pathophysiological processes, including intermittent hypoxemia and sleep fragmentation. Within emerging precision sleep medicine frameworks, disentangling these mechanisms is critical for improved phenotyping and personalized risk assessment. This study aimed to replicate prior findings using a Go/No-Go Continuous Visual Attention Test (CVAT) and to identify the most informative polysomnographic predictor of attentional performance in OSA. Methods: In this cross-sectional study, participants underwent full-night type I polysomnography and the CVAT. After exclusions, 84 patients with OSA and 22 polysomnographically normal controls were analyzed. The sample sizes for mean differences and correlational analyses were adequate. Attentional performance was indexed by standardized reaction time (RT), referenced to a normative database (n = 1244). Within the OSA group, linear regression with backward elimination evaluated hypoxemia and sleep fragmentation metrics. Results: Patients with OSA demonstrated significantly slower RTs than controls (p = 0.005). Within OSA, the AHI was not associated with attentional performance (p = 0.398). In the final regression model, sleep stage shifts—reflecting sleep–wake instability—emerged as the sole independent predictor of attentional slowing (β = 0.27, p = 0.013), whereas all hypoxemia indices were excluded. Conclusions: Sleep stage instability represents a cognitive vulnerability marker in OSA, independent of respiratory events. Integrating fragmentation metrics into precision sleep medicine models may enhance individualized phenotyping, identify patients at higher neurocognitive risk, and inform targeted interventions focused on stabilizing sleep architecture rather than relying solely on the AHI. Full article

This study investigated the effects of calcium carbide slag (CCS) (0–12 wt%) incorporation on the workability, electrochemical properties, durability, and microstructure evolution of sulfoaluminate cement (SAC) mortar. Results showed that increasing CCS content reduced mortar fluidity and shortened setting time, indicating that CCS accelerates early hydration. A 9% CCS content was determined to be the optimal dosage; at 28 days, compared to the control group, this dosage group exhibited a 6.53% increase in compressive strength, a 22.47% decrease in drying shrinkage, and a 0.279% decrease in mass loss. These performance improvements stemmed from CCS’s ability to inhibit pore connectivity and limit moisture migration. Electrochemical analysis further revealed that the 9% CCS dosage group had the highest charge transfer resistance and resistivity (30.00% higher than the control group), reflecting a denser matrix and greater ion transport resistance. Consequently, chloride ion permeability was significantly reduced, with electrical flux and diffusion coefficient decreasing by 39.98% and 28.89%, respectively. Microstructural observations confirmed that CCS promotes the formation and densification of hydration products, effectively improving the internal pore structure. While 9% CCS can serve as an effective functional supplementary material, its long-term durability and sustainability still face practical application challenges. Future research should focus on establishing predictive models for chloride ion permeation lifetime and conducting quantitative sustainability assessments of CCS-SAC composites, particularly evaluating material cost, energy consumption, and carbon dioxide emissions. Full article

To meet the requirements for the automatic alignment, insertion, and inspection of guide-tube opening pins on the upper core plate in a component pool during refueling outages of nuclear power units, this paper proposes a cognition-enhanced visual-servoing framework that integrates geometric cognition-based compensation, observation-confidence modeling, and constraint-aware optimal control. The framework addresses the key challenge posed by the coexistence of long-term geometric drift and underwater observation uncertainty. Specifically, historical closed-loop data are leveraged to learn and compensate for systematic geometric errors online, substantially improving coarse-positioning accuracy. In addition, an explicit confidence model is introduced to quantitatively assess the reliability of visual measurements. Building on these components, a confidence-driven, finite-horizon, constrained model predictive control strategy is designed to achieve safe and efficient finite-step convergence while strictly respecting actuator physical constraints. Ground experiments and deep-water component-pool validations demonstrate that the proposed method reduces coarse-positioning error by approximately 75%, achieves stable sub-millimeter alignment with an ample engineering safety margin, and effectively decreases erroneous insertions and the need for manual intervention. These results confirm the engineering applicability and safety advantages of the proposed cognition-enhanced visual-servoing framework for underwater alignment tasks in nuclear component pools. Full article

Recently, recommendation systems have actively integrated Transformers to capture real-time context. However, these systems often suffer from generalization imbalance, where predictions are biased toward popular (head) items due to the sparsity and volatility inherent in session-based data. To address this challenge, this paper proposes MoE-SLMRec, a Mixture-of-Experts (MoE)-based recommendation model that selects expert networks based on session-level contextual information. The proposed model extracts a session latent representation, h, through a session-aware controller and forms balanced predictive characteristics across the entire data distribution via dynamic routing. Experimental results demonstrate that MoE-SLMRec significantly outperforms the baseline SLMRec, improving accuracy by 1.51 percentage points (from 18.76% to 20.27%). Furthermore, the model achieved state-of-the-art performance in Recall@20 (0.8358) and MRR@20 (0.3455), validating simultaneous improvements in both retrieval capability and ranking quality. Notably, the model effectively stabilized the performance for head items while coordinating the generalization trade-off between head and tail segments. By ensuring a favorable capacity–cost trade-off while maintaining robust performance, this study presents a promising alternative under session-based recommendation settings, facilitating scalable deployment in real-time recommendation services. Full article

With the increase in human space activities, there is a significant amount of space debris as well as defunct satellites that seriously threaten the safety of spacecraft in their orbits. The laser ranging technique is one of the most accurate methods of ground-based space target observation. Therefore, it is very meaningful to study efficient tracking and observation methods for defunct satellites and space debris. In this paper, time bias, which is in advance of the actual observation, was added by analyzing the deviation of the orbit prediction such as the time bias and range bias of the runaway space objects. A new method was used for the determination of the TB and RB in real-time tracking and in data processing. The data produced from the observation of the out-of-control targets, such as the Topex satellite and CZ-2C Long March Launch Vehicle, were presented and analyzed. Taking the laser ranging data of the Topex satellite obtained on 19 November 2019, as an example, the result of the first observation circle provided the initial time bias value for the second observation circle, proving that the laser ranging method for runaway space objects is effective. The results of this paper can effectively improve the acquisition efficiency of the defunct satellites. Full article

Reliable multivariable control is critical for industrial sectors where processes exhibit severe nonlinearities and interactions. A Continuous Stirred Tank Reactor (CSTR) is a rigorous benchmark for testing control strategies addressing these complexities. This work first establishes a linear MIMO mathematical framework to define the specific structure of such interactive systems. Analysis via phase planes and steady-state analysis reveals low controllability, bistability, and strong coupling, leading to the collapse of traditional decoupled control schemes. To address these issues via multivariable control, we propose a centralized MIMO RST control structure synthesized via a Matrix Fraction Description (MFD) and the extended Bézout equation. Simulations for performance evaluation and comparison highlight the following key findings: (1) the centralized RST maintains stability and tracking precision in regions where decentralized RST loops fail; (2) it exhibits performance comparable to the Augmented State Pole Placement with Integral Action (ASPPIA) method and outperforms the standard Model-Based Predictive Control (MPC) baseline, particularly during critical equilibrium point transitions; and (3) it offers a robust yet computationally simple design that provides superior flexibility for pole placement, accommodating future identification-based models and adaptive tuning. These results validate our algebraic synthesis as a robust, computationally efficient solution for managing highly interactive nonlinear dynamics. Full article

Background: Hepatic ischemia-reperfusion injury (HIRI) is a major complication in liver surgery with limited therapeutic options. Houttuynia cordata polysaccharide (HCP), a key bioactive component of the traditional anti-inflammatory herb, has demonstrated immunomodulatory potential, but its effect on HIRI remains unclear. Methods: A murine model of 70% hepatic ischemia for 60 min followed by reperfusion was established. Mice were administered low-dose (50 mg/kg) or high-dose (100 mg/kg) HCP or the positive control N-acetylcysteine (150 mg/kg). Liver injury was assessed by serum ALT/AST levels, histopathology, oxidative stress markers, and inflammatory cytokines. Macrophage polarization and the TLR4/NF-κB pathway were analyzed using flow cytometry, qPCR, and Western blot. The TLR4 inhibitor TAK-242 was used for reverse validation, and molecular docking was performed to predict HCP binding to the TLR4/MD-2 complex. Results: HCP significantly attenuated HIRI-induced liver injury, as shown by reduced ALT/AST, improved histopathological scores, decreased MDA, increased SOD, and lower TNF-α and IL-6 levels. Mechanistically, HCP promoted a shift from M1 to M2 macrophage polarization, with increased CD206⁺ cells and Arg-1/IL-10 expression and decreased CD86⁺ cells and iNOS/IL-1β expression. HCP also suppressed TLR4/MyD88/NF-κB pathway activation, inhibiting NF-κB p65 phosphorylation and nuclear translocation. These protective effects were largely reversed by TAK-242 in vivo and in vitro. Molecular docking indicated stable binding between HCP and TLR4/MD-2. Conclusions: HCP protects against HIRI by targeting TLR4 to inhibit NF-κB signaling, thereby reprogramming macrophage polarization toward the M2 phenotype and alleviating inflammation and oxidative stress. These findings highlight HCP as a promising natural agent for HIRI intervention. Full article

Natural fractures are critical controls on shale oil storage and migration in the Upper Triassic Chang 7 Member of the Ordos Basin. However, conventional identification techniques—such as mud-invasion correction, R/S rescaled range analysis, and radioactive element analysis—are time-consuming, computationally intensive, and highly dependent on specialized logging data, limiting their large-scale application. To overcome these challenges, this study develops a multi-modal deep neural network that integrates conventional well logs with borehole imaging data. A coupled convolutional neural network (CNN) and deep neural network (DNN) architecture was constructed to predict fracture occurrence, dip angle, and aperture. The model achieves dip-angle prediction accuracies of 98.82% for both training and testing datasets, while aperture prediction accuracies reach 95.97% and 95.91%, respectively. Predicted dip angles are concentrated between 65° and 80°, deviating by less than 0.48° from measured values, whereas apertures fall mainly within 0.5–4.5 cm, with deviations below 0.21 cm except in extreme cases. The CNN branch effectively extracts spatial features from imaging logs, while the DNN branch captures nonlinear relationships in conventional logs. The integrated framework substantially improves fracture characterization accuracy and efficiency. This study provides a scalable and cost-effective approach for rapid fracture identification based on conventional logging data, reducing reliance on specialized imaging logs and supporting integrated geological and engineering evaluations in shale oil reservoirs. Full article

Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO₂ concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO₂) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article