Search Results (355)

Slab–column structures are susceptible to sudden punching shear failure at connections due to the absence of traditional beam support, prompting the need for effective damage monitoring. This study employs an acoustic emission (AE) technique to investigate the failure process of reinforced concrete slab–column specimens, analyzing basic AE parameters (hits, amplitude, energy), improved b-value (Ib-value), and RA–AF correlation, while introducing a Gaussian Mixture Model (GMM) to establish a unified index integrating crack type identification and energy information. Experimental results show that AE parameters can effectively track different stages of crack development, with Ib-value reflecting the transition from micro-crack to macro-crack growth. The correlation between AE energy and structural strain energy enables quantitative damage assessment, while RA–AF analysis and GMM clustering reveal the shift from bending-dominated to shear-dominated failure modes. This study provides a comprehensive framework for real-time damage evaluation and failure mode prediction in slab–column structures, demonstrating that AE-based multi-parameter analysis and data-driven clustering methods can characterize damage evolution and improve the reliability of structural health monitoring. Full article

The linear rolling guide (LRG) is widely used in the computer numerical control machine tool industry and other industries. To accurately evaluate the performance of LRGs, a multi-parameter test bench was developed to measure motion accuracy, preload drag force (PDF), vibration, temperature rise, and fatigue life. The mechanical structure and measurement and control system of the test bench were designed based on established principles and methods. ANSYS 19.0 software was used for static analysis of the gantry, modal analysis of the upper bed, and simulation of the impact of loading block thickness on load distribution uniformity. At the same time, we used an impact hammer modal test to verify the correctness of the finite element analysis of the upper bed. The analysis results validated the structural design. To verify the test bench’s repeatability, comparative experiments were conducted with the Hilectro LGD35-type LRGs, focusing on motion accuracy, PDF, and fatigue life. The experimental results confirmed the test bench’s high repeatability and validated the derived equations for measuring motion accuracy. Full article

Helicopters often operate at altitudes where cloud activity is prevalent, making them susceptible to icing hazards. Accurate and rapid prediction of ice accretion on rotors is crucial for expanding helicopter flight capabilities and enhancing flight safety. In this paper, we first introduce an improved 3-D ice accretion simulation method that accurately models runback water characteristics by considering factors such as control volume size, runback water speed, and direction. This method precisely calculates the ice accretion mass and runback water distribution. Building upon this foundation, we then present a rapid ice accretion prediction model, designed to overcome the time-consuming nature of traditional CFD frameworks. In the experimental section, our simulation methodology is applied to a hovering UH-1H rotor. A comparative analysis with experimental results reveals that the maximum absolute ice thickness error remains below 3 mm, demonstrating satisfactory computational accuracy of the proposed approach. Moreover, we demonstrate the model’s rapid prediction capabilities (achieving within a computational time of 2.66 s and a maximum ice thickness error of 7.2 mm) and implement multi-parameter predictions. Full article

Traditional landslide monitoring systems struggle to capture the spatiotemporal dynamics of rainfall-induced hydro-mechanical processes, with a significant risk of signal loss during critical “unsaturated-saturated” state transitions. To address this issue, we propose an integrated framework that utilizes FLAC3D numerical simulation to dynamically optimize multi-sensor deployments. Through coupled seepage-stress analysis under different rainfall scenarios in China’s Tianshan Mountains, this study achieved the following objectives: (1) risk-based sensor deployment by precisely identifying shallow shear strain concentration zones (5–15 m) through FLAC3D simulation (with FBG density of 0.5 m/point in the core sliding belt and GNSS spacing ≤ 50 m); (2) establishment of a multi-parameter cooperative early warning system (displacement > 50 mm/h, pore water pressure > 0.4 MPa, strain > 6400 με), where red alerts are triggered when at least two parameters exceed thresholds, reducing false alarm rates; and (3) development of an adaptive sampling framework based on three rainfall intensity scenarios, which increases measurement frequency during heavy rainfall to capture transient critical points (GNSS sampling rate enhanced to 10 Hz). This approach significantly enhances the capture capability of critical hydro-mechanical transition processes while reducing the monitoring redundancy. The framework provides a scientifically robust and reliable solution for slope disaster-risk prevention and management. Full article

Background/Objectives. Recent evidence underscores the prognostic and classificatory relevance of somatic mutations in myelodysplastic neoplasms (MDSs) and acute myeloid leukemia (AML). Methods. This prospective study assessed gene mutation dynamics via next-generation sequencing (NGS) in 84 MDS/AML patients treated with intensive chemotherapy or hypomethylating agents plus venetoclax. Results. At diagnosis, 95% had somatic mutations detected by NGS, while only 29% had a measurable residual disease (MRD) marker with qPCRs. NGS at complete remission (CR) was performed in 56/71 patients who achieved CR; 59% had persisting mutations, mostly in DNMT3A, TET2, and ASXL1 (DTA mutations). Mutations’ persistence in CR was linked to a shorter relapse-free survival (RFS; median 8 months vs. not reached, HR 4.41, 95% CI 1.69–11.49; p = 0.002) and overall survival (OS; 2-year OS: 51.5% vs. 88%, HR 4.02, 95% CI 1.39–11.65; p = 0.001). Combining NGS and multiparameter flow cytometry (MFC) for MRD detection, we divided patients into three groups with distinct RFS (NGS−/MFC−, NGS−/MFC+, or NGS+/MFC− and NGS+/MFC+), with double-negative patients displaying the best RFS (p < 0.001). In the multivariate analysis, NGS and MFC MRD+ were independent predictors of RFS. Conclusions. This real-world study confirms the added prognostic role of NGS in MRD detection on RFS, particularly when combined with MFC. This approach may improve risk stratification and guide treatment decisions. Full article

Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential therapeutic target for drug development. Six QSAR models were established to predict the inhibitory activity (expressed as IC₅₀ values) of candidate compounds against CatL. These models were developed using statistical method heuristic methods (HMs), the evolutionary algorithm gene expression programming (GEP), and the ensemble method random forest (RF), along with the kernel-based machine learning algorithm support vector regression (SVR) configured with various kernels: radial basis function (RBF), linear-RBF hybrid (LMIX2-SVR), and linear-RBF-polynomial hybrid (LMIX3-SVR). The particle swarm optimization algorithm was applied to optimize multi-parameter SVM models, ensuring low complexity and fast convergence. The properties of novel CatL inhibitors were explored through molecular docking analysis. The LMIX3-SVR model exhibited the best performance, with an $R^2$ of 0.9676 and 0.9632 for the training set and test set and RMSE values of 0.0834 and 0.0322. Five-fold cross-validation $R_{5-fold}^2$ = 0.9043 and leave-one-out cross-validation $R_{loo}^2$ = 0.9525 demonstrated the strong prediction ability and robustness of the model, which fully proved the correctness of the five selected descriptors. Based on these results, the IC₅₀ values of 578 newly designed compounds were predicted using the HM model, and the top five candidate compounds with the best physicochemical properties were further verified by Property Explorer Applet (PEA). The LMIX3-SVR model significantly advances QSAR modeling for drug discovery, providing a robust tool for designing and screening new drug molecules. This study contributes to the identification of novel CatL inhibitors, which aids in the development of effective therapeutics for SARS-CoV-2. Full article

In the context of the energy crisis and the urgency of passive design in contemporary architecture, this study focuses on the Tujia-style Sanheyuan in southeastern Chongqing, China, which is highly adaptable to local climatic conditions. Using field surveys, architectural mapping, computational fluid dynamics numerical simulations, and multi-parameter comparative analysis, this study systematically explores the relationship between the geometric form of the Sanheyuan and its courtyard ventilation performance. Based on the Tujia construction scale modulus, this study summarizes the basic prototype of the Sanheyuan, analyzes the selection paths of its three sets of construction parameters, and constructs 48 typical courtyard models for wind environment simulation. By introducing five evaluation indicators—wind speed uniformity coefficient, proportion of strong wind zone area, proportion of calm wind zone area, and unit area wind rate—this study comprehensively assesses the impact of Sanheyuan design parameters on courtyard wind environment adaptability. This study concludes that specific spatial design parameters of the Tujia-style Sanheyuan significantly influence wind environment adaptability, offering quantitative guidance for climate-responsive and culturally informed architectural design. This study found that the optimal side room width-to-depth ratio is [1.00, 0.86, 0.83]; the optimal ridge height-to-stilt height ratio is [4.29, 8.00, 2.96]; and the optimal building footprint-to-side room area ratio is [3.01, 5.06, 4.75]. Full article

Background: Postbiotics with anti-adipogenic properties can significantly modify adipocyte metabolism by influencing key cellular pathways involved in lipid accumulation. In preliminary in vitro studies, it is essential to monitor various cellular and subcellular variables, including gene expression and protein synthesis potential, through RT-qPCR analysis. It is also crucial to select internal controls carefully and evaluate their stability for effective normalization and accurate interpretation of the results. Methods: In this study, we assessed the stability of six commonly used housekeeping genes: GAPDH, Actb, HPRT, HMBS, 18S, and 36B4. We analyzed their variability in mature 3T3-L1 adipocytes treated with supernatants from newly isolated Lacticaseibacillus paracasei strains. Our analysis combined classical statistical methods, a ∆Ct analysis, and software algorithms such as geNorm, NormFinder, BestKeeper, and RefFinder. Results: Our stepwise, multiparameter strategy for selecting reference genes led to the exclusion of Actb and 18S as the most variable reference genes. We identified HPRT as the most stable internal control. Additionally, HPRT and HMBS emerged as a stable pair, while the recommended triplet of genes for reliable normalization consists of HPRT, 36B4, and HMBS. Conclusions: The widely used putative genes in similar studies—GAPDH and Actb—did not confirm their presumed stability, which once again emphasizes the need for experimental validation of internal controls to increase the accuracy and reliability of gene expression. Combining a unique biological model—postbiotic-treated adipocytes—with multiple algorithms integrated into a single workflow allows us to provide a methodological template applicable to similar nutritional and metabolic research settings. Full article

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

The 7075-T7451 aluminum alloy, widely used in aerospace, aviation, and automotive fields for critical load-bearing components due to its excellent mechanical properties, suffers from residual stresses induced by thermo-mechanical coupling during milling, which deteriorate workpiece performance. This study explores how key milling parameters—spindle speed *n_c*, feed per tooth *f_z*, cutting depth *a_p*, and cutting width *a_e*—affect surface residual stress and cutting force via orthogonal experiments and finite element analysis (FEA). Results show *a_e* is critical for X-direction residual stresses, while *f_z* dominates Y-direction ones. Cutting force increases with *f_z*, *a_p*, and *a_e* but decreases with higher *n_c*. Multivariate regression-based prediction models for residual stress and cutting force were established, which effectively characterize parameter–response relationships with maximum prediction errors of 18.69% (residual stress) and 12.27% (cutting force), showing good engineering applicability. The findings provide theoretical and experimental foundations for multi-parameter optimization in aluminum alloy milling and residual stress/cutting force control, with satisfactory practical effectiveness. Full article

Monoterpenoids are a structurally diverse class of natural products with a long-standing history of therapeutic use. Despite their promising bioactivities, their clinical development has been limited by dose-dependent toxicities, poor pharmacokinetics, and suboptimal drug-like properties. In this work, a comprehensive in silico pipeline was employed to evaluate 1175 monoterpenoid compounds retrieved from ChEBI, aiming to identify structurally diverse candidates that possess favorable drug-like characteristics. A total of 54 molecular parameters were calculated using thirteen computational tools, covering physicochemical parameters, ADMET profiles, and toxicological risk assessments. Stepwise filtering was employed to retain only compounds meeting stringent thresholds across multiple domains, followed by chemoinformatic analysis. Structure–activity relationship mapping and target prediction were subsequently conducted to explore mechanistic plausibility. This workflow led to the identification of seven top-performing monoterpenoids that exhibited ideal physicochemical profiles, high gastrointestinal absorption, low predicted toxicity, and full compliance with medicinal chemistry rules. Notably, target prediction revealed a convergence on GPCRs, enzymatic and nuclear receptors, highlighting potential anti-inflammatory and neuromodulatory effects. The identification of conserved pharmacophores across selected scaffolds further reinforces their translational potential. Our results highlight the value of multi-parameter computational triage in natural product drug discovery and reveal a subset of overlooked monoterpenoids with promising preclinical applications. Full article

Amidst the background of accelerated global energy transition, the safety risk of lithium-ion battery energy storage systems, especially the fire hazard, has become a key bottleneck hindering their large-scale application, and there is an urgent need to build a systematic prevention and control program. This paper focuses on the fire characteristics and thermal runaway mechanism of lithium-ion battery energy storage power stations, analyzing the current situation of their risk prevention and control technology across the dimensions of monitoring and early warning technology, thermal management technology, and fire protection technology, and comparing and analyzing the characteristics of each technology from multiple angles. Building on this analysis, this paper summarizes the limitations of the existing technologies and puts forward prospective development paths, including the development of multi-parameter coupled monitoring and warning technology, integrated and intelligent thermal management technology, clean and efficient extinguishing agents, and dynamic fire suppression strategies, aiming to provide solid theoretical support and technical guidance for the precise risk prevention and control of lithium-ion battery storage power stations. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article