Search Results (12,921)

Rock mass characterization is crucial for evaluating slope stability and recommending effective prevention mechanisms. This study presents a comparative analysis of three approaches for discontinuity analysis: (1) conventional field survey, (2) digital manual measurement on 3D models generated with UAV-based photogrammetry, and (3) semi-automatic analysis based on clustering algorithms (K-NN) for point cloud segmentation. All three methods were applied to the same slope, allowing their performance to be evaluated in terms of accuracy, efficiency, and replicability. The results showed that the semi-automatic method achieved the highest coverage (81%) and identified 586 discontinuities, with RMSE values of 2.58° for orientation, 0.087 m for spacing, and 2.05 m for persistence, using the conventional method as a reference. The digital manual method, with 19% coverage, yielded very low error (RMSE of 3.27° for orientation, 0.012 m for spacing, and 0.063 m for persistence), validating it as a complementary and reliable alternative. In contrast, the conventional method required the longest execution time (10 h) and achieved only 19% coverage, being the least replicable due to its dependence on expert judgment. Overall, the comparison highlights the advantages of digital methods, especially the semi-automatic approach, in improving efficiency, safety, and replicability, while providing robust information to recommend prevention strategies for rock slope stability. Full article

Understanding the impact of hydric stress on medicinal plants in the context of climate change is becoming increasingly important. This study aimed to assess the quality of a seed lot of Agastache mexicana subsp. mexicana (Amm) through a novel calculation of the Vigour Index on time basis (${VI}_T$). The evaluation was based on relationships among plant height, leaf number, survival time, and plant density across six irrigation regimes, referred to as stages, which differed in the timing and quantity of water, designed to impose water stress from seedling emergence until plant death. To maximise growth and survival time, we utilised two input factors: Artificial Shade Levels (ASLs) of 38%, 87%, and 94%, as well as Silicon Dioxide Levels (SDLs) of 0.0%, 0.2%, 0.4%, and 0.8%. The effects of these treatments were measured using the Survival Index (SI) and the ${VI}_T$. The plants achieved their highest SI and ${VI}_T$ values influenced by minimum mortality and maximum height and leaf number in stage three. This behaviour aligned with the field capacity of the substrate, supporting the evaluation of stages one and two as waterlogging stress, while the remaining stages were classified as drought stress. The ${VI}_T$ results showed statistically significant effects from ASL, particularly at 94%. However, the ${VI}_T$ in relation to SDL was not statistically significant. The ${VI}_T$ measurements were visualised using spline interpolation, a method that provides an effective approach to quantify adverse conditions affecting Amm’s development and that it can support to identify the hydric stresses type. Full article

Regenerative medicine is a rapidly evolving field of contemporary biomedical research that offers new therapeutic strategies for conditions previously considered untreatable. Cell therapy shows particular potential in this domain. However, rigorous biosafety measures are required in its development and clinical application. This review proposes a practice-oriented biosafety framework for cell therapy, translating key risks into operational principles: toxicity, oncogenicity/tumorigenicity/teratogenicity, immunogenicity, biodistribution; and cell product quality. For each principle, preclinical approaches and regulatory expectations are summarized. Criteria for immunological safety are addressed, including activation of innate immunity (complement, T- and NK-cell responses) and the need for HLA typing. Biodistribution assessment involves the use of quantitative PCR and imaging techniques (PET, MRI) to monitor cell fate over time. The risks of oncogenicity, tumorigenicity, and teratogenicity can be analyzed using a combination of in vitro methods and in vivo models in immunocompromised animals. Product quality assessment includes sterility, identity, potency, viability, and genetic stability, with alignment of procedures to regulatory requirements and an emphasis on quality-by-design principles to ensure safe and reproducible clinical use. Integrating toxicity and safety pharmacology data supports a balanced risk–benefit assessment and clinical trial planning. Full article

Cofferdams provide dry, stable working conditions for construction in marine environments. However, conventional methods often require significant time and cost for installation and removal, and are prone to leakage. This study proposes a novel method for the rapid and efficient construction of a large-diameter circular cofferdam using suction-driven installation and extraction. As opposed to conventional suction bucket foundations, the upper part of the cofferdam remains exposed above the water surface, and several prefabricated segments are assembled to form a single suction unit. A full-scale field test was conducted in Jebudo, Republic of Korea, using a 20 m-diameter, 13 m-high circular steel cofferdam. The test program included the design and fabrication of a suction cover and an optimized piping system. The key measurements during installation included the suction pressure variation with the penetration depth, leakage at the segmental joints, structural deformations, and inclination. The cofferdam successfully penetrated to a target embedment depth of 5 m at an average rate of 1.83 m/h and was safely removed using reverse suction. Although suction technology has been widely applied to offshore foundations and anchors, this study is the first to demonstrate its feasibility for large cofferdams. These results provide a foundation for future offshore applications of suction-driven cofferdam installations. Full article

Background/Objectives: Transferrin is a multi-task protein commonly known for binding iron; however, it is involved in multiple crucial processes, including antimicrobial activity, the growth of different cell types, differentiation, chemotaxis, the cell cycle, and cytoprotection. Vascular cell adhesion molecule 1 (VCAM-1) is a cell surface glycoprotein which participates in inflammation and the trans-endothelial movement of leukocytes. Neither transferrin nor VCAM-1 has been studied in the context of progressive supranuclear palsy (PSP) or corticobasal syndrome (CBS). This study aimed to evaluate the utility of transferrin and VCAM-1 assessment for the in vivo examination of tauopathic atypical Parkinsonian syndromes. Methods: This study included 10 patients with clinically probable PSP-RS, 10 with clinically probable PSP-P, and 8 with probable CBS. Patients’ blood and urine were collected and analyzed. Twenty-four serum samples (from twelve males and twelve females) were obtained from age-matched healthy volunteers. Peripheral blood inflammatory ratios, including the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, the neutrophil-to-monocyte ratio, the neutrophil-to-high-density lipoprotein ratio, and the monocyte-to-high-density lipoprotein ratio, were calculated. VCAM-1 and transferrin concentrations were measured in the serum and urine. The urinary biomarker results are not included in the main analysis due to the absence of a control group. Results: The highest concentrations of transferrin in the serum were observed in patients with PSP-P, followed by PSP-RS and CBS. Statistically significant differences were found between PSP-P and healthy controls (p < 0.0001) and PSP-RS and healthy controls (p < 0.0001). The highest levels of serum VCAM-1 were observed in the PSP-P group. Significant differences were found between PSP-P and healthy controls (p < 0.0001), PSP-P and CBS (p < 0.001), and PSP-RS and healthy controls (p < 0.001). Serum VCAM-1 levels were negatively correlated with the NLR in CBS patients (p < 0.03; r = −0.74). Serum transferrin levels were negatively correlated with the NHR in CBS patients (p < 0.04; r = −0.64). ROC curve analyses were conducted to evaluate the diagnostic utility of serum transferrin and VCAM-1 in distinguishing tauopathic APS patients from controls. Transferrin showed excellent diagnostic performance, with an AUC of 0.975 (95% CI: 0.888–0.999; p < 0.0001), a sensitivity of 96.4%, and a specificity of 95.8% at the optimal cut-off (>503.0). VCAM-1 demonstrated good accuracy, with an AUC of 0.839 (95% CI: 0.711–0.926; p < 0.0001), a sensitivity of 75.0%, and a specificity of 91.7% at the optimal cut-off (>463.9). Conclusions: The obtained results indicate the potential role of transferrin and VCAM-1 in the pathogenesis of tauopathic APSs and highlight the need for further exploration in this field. Full article

The sustainable use and management of groundwater resources is a challenging issue due to population growth and climate change. Accurate quantification of groundwater recharge is a basic requirement for effective groundwater resource management, yet it is still lacking in many areas around the world. The study was designed to estimate recharge to groundwater from natural rainfall in the Gilgel Gibe and Dhidhessa catchments in southwestern Ethiopia, employing the water table fluctuation (WTF) and chloride mass balance (CMB) techniques. These methods are being applied for the first time in the study area and have not previously been used in these catchments. Given the region’s data scarcity, a community-based data collection program was implemented and supplemented with additional field measurements and secondary data sources. Groundwater level, spring discharge, and rainfall were monitored over the 2022/2023 hydrological year. Groundwater level fluctuations were found to be influenced by topography and rainfall patterns, reaching 8.2 m in amplitude in the upstream part of the catchments. Chloride concentrations were determined in groundwater samples collected from hand-dug wells and springs, and rainwater was also collected. Rainwater exhibited a mean chloride concentration of 2.46 mg/L, while groundwater chloride concentrations ranged from 3 mg/L to 36.99 mg/L. The estimated recharge rates varied spatially, ranging from 170 to 850 mm/year using the CMB method (11% to 55% of annual rainfall, mean recharge rate of 454 mm/year) and from 76 to 796 mm/year using the WTF method (4% to 43% of annual rainfall, mean recharge rate of 439 mm/year). Notably, recharge estimates were lowest downstream in the lowland areas and highest upstream in the highland regions. Rainfall amount, local lithology, and topography were identified as major influences on groundwater recharge across the study area. Both CMB and WTF methods were deemed applicable in the volcanic aquifers, provided that all the respective assumptions are followed. This study significantly contributes to the groundwater dataset for the region, in addition to recharge estimation and the research conclusions, emphasizing the importance of long-term monitoring and time series analysis of chloride data to reduce uncertainties. The work serves as a valuable reference for researchers, policymakers, and regional water resource managers. Full article

Stray currents leaking from electrified DC rail systems cause the greatest corrosion risk to underground metal gas pipelines and can lead to pipeline wall perforation in a very short time. Leakage and gas explosion, and other direct and indirect effects, can even disrupt the stability of the energy system. Maintaining the reliability of gas pipelines, therefore, requires protecting them against corrosion caused by stray currents. It is therefore necessary to conduct field studies to identify sections of gas pipelines at risk and where protective installations should be installed. The paper discusses the most important field methods for assessing the risk of stray currents to gas pipelines: the potential of rail traction relative to ground, electric field gradients in the ground associated with stray current flow, correlation of gas pipeline potential and voltage of pipeline vs. the rail, and time-frequency analysis of the pipeline and rail potentials. A typical application case for each method is indicated, and the advantages and disadvantages of each research technique are identified. The criterion for selecting methods for this review was a short measurement duration (tens of minutes), after which it is possible to determine the level of the hazard to the gas pipeline caused by stray currents in the examined location. This is why these methods have an advantage over other research techniques that require long-term monitoring or exposure of probes or sensors. The review will be useful for cathodic protection personnel involved in the operation of gas pipelines and may be helpful in developing new methods for assessing the impact of stray currents. Full article

Background: Research on the impact of 5G mobile phone electromagnetic exposure on corticospinal excitability and intracortical mechanisms is still poorly understood. Objective: This randomized controlled pilot study explored the effects of 5G mobile phone exposure at 3.6 GHz (power density: 0.0030 W/m²) on corticospinal excitability and intracortical mechanisms in healthy adults. Methods: Nineteen healthy participants (mean age: 36.5 years) were exposed to 5G mobile phone exposure for 5 and 20 min, approximating the typical duration of a phone call. Corticospinal excitability, intracortical facilitation, short intracortical inhibition, and long intracortical inhibition using single- and paired-pulse transcranial magnetic stimulation assessed before and immediately after exposure were performed. Results: A two-way repeated-measures ANOVA revealed no significant interactions between exposure condition (5 min, 20 min, sham) and time (pre vs. post) for CSE, ICF, SICI, or LICI (all p > 0.15). Bayesian analyses yielded Bayes factors close to 1, indicating inconclusive evidence for both the null and alternative hypotheses. Conclusion: Short-term exposure to 5G mobile phone electromagnetic fields did not produce detectable changes in corticospinal or intracortical excitability. Bayesian evidence was similarly inconclusive (Bayes factors ≈ 1), suggesting that the data provide limited support for either the presence or absence of a detectable effect. Any potential influence of 5G exposure on neural function is therefore likely to be subtle with the present methods. As a pilot study, these findings should be interpreted cautiously and underscore the need for further research using more sensitive outcome measures, extended exposure durations, and vulnerable populations. Full article

Understanding water and heat transport through soils is vital for managing soil and groundwater resources, agricultural irrigation, and ecosystem protection. This paper aims to explore the potential application of deep learning methods in simulating water and heat transport processes within soils. It also examines the interactions between soil hydrological processes and environmental factors, including meteorological conditions and groundwater levels. To achieve these, we develop a hybrid model Informer–LSTM by combining two powerful architectures: Informer, a Transformer-based model essentially designed for long-sequence time-series forecasting, and Long Short-Term Memory (LSTM), a neural network that is great at learning short-term patterns in sequential data. The model is applied to field measurements from Henan Township in Ordos, Inner Mongolia, China, for training and testing, to simulate three key variables: soil water content, temperature, and heat flux at different depths in two soil columns with different groundwater levels. Our results confirm that Informer–LSTM is highly effective at simulating the soil water and heat transport. Simultaneously, we evaluate its performance by incorporating various combinations of input data including meteorological data, soil hydrothermal dynamics, and groundwater level. This reveals the relationship between soil hydrothermal processes and meteorological data, as well as coupled processes of soil water and heat transport. Moreover, employing SHapley Additive exPlanations (SHAP) analysis, we identify the most influential factors for predicting heat flux in shallow soils. This research demonstrates that deep learning models are a viable and valuable tool for simulating soil hydrothermal processes in arid and semi-arid regions. Full article

For the fault issues in the workshop environmental control system sensor network, a fault diagnosis and recovery method based on medium-to-long-term predictions is proposed. Firstly, a temperature observer based on the Informer model is established. Then, the predicted data temporarily replaces the missing real data, and the model predicts the state of the sensor system within the step size. Secondly, the predicted data is combined with the measured temperature series, and residuals are utilized for real-time detection of sensor faults. Finally, the predicted data at the time of the fault replaces the real data, enabling the recovery of fault data; experiments are conducted to verify the effectiveness of the proposed method. The results indicate that when the prediction horizon is 1, 5, 10, 20, and 50, the average fault diagnosis rates under four fault levels are 94.40%, 95.28%, 94.79%, 92.52%, and 93.35%, respectively. The average coefficients of determination for data recovery are 0.999, 0.997, 0.995, 0.985, and 0.915, respectively. This achieves medium-to-long-term predictions in the field of sensor fault diagnosis. Full article

We present the ML-CALMO framework, which integrates machine learning with queueing theory for last-mile delivery optimization under dynamic conditions. The system combines Long Short-Term Memory (LSTM) demand forecasting, Convolutional Neural Network (CNN) traffic prediction, and Deep Q-Network (DQN)-based routing with theoretical stability guarantees. Evaluation on modern benchmarks, including the 2022 Multi-Depot Dynamic VRP with Stochastic Road Capacity (MDDVRPSRC) dataset and real-world compatible data from OSMnx-based spatial extraction, demonstrates measurable improvements: 18.5% reduction in delivery time and +8.9 pp (≈12.2% relative) gain in service efficiency compared to current state-of-the-art methods, with statistical significance (p < 0.01). Critical limitations include (1) computational requirements that necessitate mid-range GPU hardware, (2) performance degradation under rapid parameter changes (drift rate > 0.5/min), and (3) validation limited to simulation environments. The framework provides a foundation for integrating predictive machine learning with operational guarantees, though field deployment requires addressing identified scalability and robustness constraints. All code, data, and experimental configurations are publicly available for reproducibility. Full article

Forest managers need regular accurate assessments of forest conditions to make informed decisions associated with harvest schedules, growth projections, merchandising, investment, and overall management planning. Traditionally, this is achieved through field-based sampling (i.e., timber cruising) a subset of the trees within a desired area (e.g., 1%–2%) through stratification of the landscape to group similar vegetation structures and apply a grid within each stratum where fixed- or variable-radius sample locations (i.e., plots) are installed to gather information used to estimate trees throughout the unmeasured remainder of the area. These traditional approaches are often limited in their assessment of uncertainty until trees are harvested and processed. However, the increasing availability of airborne laser scanning datasets in commercial forestry processed into Digital Inventories^® enables the ability to non-destructively assess the accuracy of these field-based surveys, which are commonly referred to as cruises. In this study, we assess the uncertainty of common field sampling-based estimation methods by comparing them to a population of individual trees developed using established and validated methods and in operational use on the University of Idaho Experimental Forest (UIEF) and a commercial conifer plantation in Louisiana, USA (PLLP). A series of repeated sampling experiments, representing over 90 million simulations, were conducted under industry-standard cruise specifications, and the resulting estimates are compared against the population values. The analysis reveals key limitations in current sampling approaches, highlighting biases and inefficiencies inherent in certain specifications. Specifically, methods applied to handle edge plots (i.e., measurements conducted on or near the boundary of a sampling stratum), and stratum delineation contributes most significantly to systematic bias in estimates of the mean and variance around the mean. The study also shows that conventional estimators, designed for perfectly randomized experiments, are highly sensitive to plot location strategies in field settings, leading to potential inaccurate estimations of BAA and TPA. Overall, the study highlights the challenges and limitations of traditional forest sampling and impacts specific sampling design decisions can have on the reliability of key statistical estimates. Full article

A mathematical model was established to describe the sublimation and diffusion of water molecules and their adsorption onto cold traps. This model was used to analyze the combined influence mechanisms of sublimation temperature and ambient pressure on the vapor deposition process of water ice. Tunable Diode Laser Absorption Spectroscopy (TDLAS) was employed to provide real-time feedback on water vapor concentration within the experimental apparatus. Based on this feedback, the sublimation temperature was dynamically adjusted to maintain the concentration dynamically stabilized around the target value. A dedicated apparatus for generating controlled water vapor flow fields and detecting concentration was constructed. The accuracy of both the mathematical model and Finite Element Analysis (FEA) simulations was verified through comparative experiments. Laser triangulation was utilized as a method to detect the thickness of the adsorbed ice film on the sample surface. Leveraging this technique, a water vapor deposition and adsorption verification system was developed. This system was used to test the differences in water adsorption performance across various materials and to measure the correlation between the thickness of the adsorbed/deposited ice film on the samples and both deposition time and sublimation temperature. Full article

Underground power pipelines are often encased in box culverts and buried in soil. When foundation pit excavation involves such existing pipelines, the buried box culverts can become partially suspended, risking excessive vertical deformation and requiring effective in situ protection. This study proposed analytical methods to calculate the vertical deformation of large-span box culverts under both unprotected and protected conditions. A case study of the 112 m suspended power box culverts at Yunnan Road Station on Nanjing Metro Line 5 is presented, where the methods are applied to determine the maximum allowable unsupported span and to formulate specific support and suspension protection schemes, which include a number of protection points and their spacing. Validation through ABAQUS modeling shows strong agreement among theoretical predictions, numerical simulations, and field measurements. Parametric analysis further demonstrated that the height, width, and modulus of the reinforced soil around the buried section all have a significant influence on the deformation control effectiveness. This study provides a combined theoretical framework and practical design guidelines for deformation control of large-span suspended box culverts in engineering applications. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article