Search Results (12)

The piezocone penetration test (CPTU) provides rapid, continuous measurements of in situ geotechnical parameters, making it a valuable tool for soil classification and stratigraphic identification. However, conventional classification methods frequently exhibit poor cross-regional generalizability and remain limited in achieving fine-grained stratigraphic identification. To address these limitations, this study constructs a cross-regional CPTU soil classification dataset by integrating data from three sources: the Premstaller Geotechnik database, the Global-CPT/3/1196 database, and a Chinese engineering project database. The compiled dataset was subsequently partitioned into a training set of 454,184 samples and three independent test sets. Three feature combinations and four machine learning algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost), were evaluated in terms of classification performance and cross-regional robustness. Results indicate that the XGBoost-based model, using Depth, corrected cone resistance (q_t), friction ratio (R_f), pore pressure ratio (B_q), normalized friction ratio (F_r), and pore pressure (u₂) as inputs, achieved the highest performance across the three independent test sets. Misclassifications primarily occurred between adjacent soil types with similar physical characteristics. SHapley Additive exPlanations (SHAP) analysis indicated that F_r and q_t were the dominant contributors to model predictions; R_f played an important role in minority classes; Depth showed relatively balanced importance across classes, while B_q and u₂ made minimal contributions. Applying the best-performing model to unseen CPTU data and comparing the predictions with borehole logs showed that the model not only preserves overall stratigraphic trends but also identifies finer-scale stratigraphic details. Full article

The BN-350 is the first industrial fast neutron reactor in the history of nuclear energy. It is currently undergoing decommissioning. One of the key challenges of decommissioning is managing the solid radioactive waste that has accumulated throughout the reactor’s operational life. At the moment, the accumulated solid radioactive waste is stored in a storage facility within the BN-350 reactor complex. An analysis showed that more than ~7262 tons with 5.17 × 10¹⁴ Bq activity of various types of solid radioactive waste have been accumulated over the reactor operation. They are mainly represented by materials with low activity. At the same time, the main share of activity is comprised of highly active waste with a total mass of ~170 tons and an activity of 4.73 × 10¹⁴ Bq. A solid radioactive waste management strategy has been developed. It includes all stages from collection and classification to transportation and long-term storage. Modern technologies now offer new possibilities. Some radioactive waste can be processed and reused in other economic sectors. In particular, recycling metals and alloys can reduce the volume of solid radioactive waste. It can also return valuable materials to industrial use. Full article

A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. Full article

The emergence of SARS-CoV-2 variants has heightened concerns about vaccine efficacy, posing challenges in controlling the spread of COVID-19. As part of the COVID-19 Vaccine Effectiveness and Variants (COVVAR) study in Uganda, this study aimed to genotype and characterize SARS-CoV-2 variants in patients with COVID-19-like symptoms who tested positive on a real-time PCR. Amplicon deep sequencing was performed on 163 oropharyngeal/nasopharyngeal swabs collected from symptomatic patients. Genome assembly, lineage classification and phylogenetic analysis was performed using the Edge Bioinformatics pipeline version 2.4.0, Pangolin version 4.3.1 and iqtree version 2.3.6 software respectively. Of the 163 deep sequences analyzed between April 2023 and March 2024, the most common were XBB.1 lineages and sublineages (113, 69.3%), followed by JN.1* (12, 7.4%), XBB.2* (11, 6.7%) and FL* (11, 6.7%), EG* (7, 4.3%), others (BQ.1.1, FY.4.1, FY.4.1.2, GY.2.1, HK.27.1) (5, 3.1%) and CM* (4, 2.5%). XBB.1* dominated from April to July 2023; thereafter, other variants, including JN.1* were increasingly detected. There was no statistically significant association between vaccine status and lineage assignment (Fisher’s exact test, p-value = 0.994). Our findings showed that the Omicron variant, specifically the XBB.1* lineage, was the dominant circulating virus. However, the emergence of the JN.1 variant that exhibits a significant spike protein mutation profile could impact COVID-19 transmission in Uganda. Full article

In order to fully explore the development degree and distribution law of the structural plane of a tunnel surrounding rock in three-dimensional space, this paper studies the geometric characteristic parameters of a structural plane in the study area through field investigation, data acquisition and statistical analysis. The structural plane is divided into three dominant groups by using DIPS. v5. 103 software. The probability distribution model of occurrence, trace length, diameter and spacing of the structural plane is established. This paper focuses on the error correction of structural plane occurrence and the estimation of average trace length based on the rectangular window method. The discrete fracture network model is generated by using MATLAB R2021b software, and the discrete fracture network model is verified from three aspects: structural plane occurrence, average trace length and area density. The verification results are compared with the measured data, and the simulation results are in line with the actual situation on site. Based on the discrete fracture network model, the volume joint number of rock mass is calculated. Based on the JSR index, BQ classification method and RQD classification, the development degree of fractures and surrounding rock classification in this area are evaluated. A method of surrounding rock classification based on three evaluation indexes is discussed to comprehensively and accurately classify the quality of rock mass in this area. Full article

The rock mass deformation modulus is one of the most important design parameters in a range of rock engineering applications. Its value is usually obtained directly through in situ testing or estimated indirectly on the basis of a rock mass quality classification system. Because in situ testing is generally costly, time-consuming, and presents operational difficulties, it cannot be carried out extensively, and many researchers have concentrated on developing indirect procedures to obtain information on the modulus of deformation, such as the RMR method, Q method, and GSI method. The purpose of this paper is to present a new system for estimating the rock mass deformation modulus called the BQ method, which is based on the BQ (basic quality) system. In this paper, the BQ system is first briefly reviewed, and then more than 60 in situ measurements from three large hydropower stations in China are used to develop a new relationship between BQ and the deformation modulus, based on a power function relationship. The paper also derives correlations based on the existing estimation formula and the relationship between BQ and other classification schemes, resulting in several recommended formulas for estimating the deformation modulus of a rock mass using the BQ method. Full article

In this study, the K-means algorithm based on particle swarm optimization (K-PSO) and game theory are introduced to establish the quality evaluation model of a rock mass. Five evaluation factors were considered, i.e., uniaxial saturated compressive strength of rock, discontinuity spacing, acoustic velocity, rock quality designation (RQD), and integrity coefficient. The rock mass of an elevation adit at the abutment of Maji hydropower station was taken as a case study. The subjective weight of the evaluation factor was determined by the weighted least squares method, and the objective weight of the evaluation factor was determined by the entropy method. The combined weights of each influencing factor were determined by game theory to be 0.142, 0.179, 0.035, 0.116, and 0.108. The rock mass quality evaluation in the study area was analyzed by K-PSO algorithm. The results indicate that the K-PSO clustering results are almost the same as the evaluation results of the traditional basic quality (BQ) classification method and the widely used extension evaluation method and are consistent with the preliminary judgment of the expert field. The results are consistent with the field observation law. It is considered that the K-PSO clustering theory can reflect the engineering geological characteristics of the rock mass of the hydropower project in the rock mass quality evaluation. Full article

New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the β-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels. Full article

The BQ.1 SARS-CoV-2 variant, also known as Cerberus, is one of the most recent Omicron descendant lineages. Compared to its direct progenitor BA.5, BQ.1 has some additional spike mutations in some key antigenic sites, which confer further immune escape ability over other circulating lineages. In such a context, here, we perform a genome-based survey aimed at obtaining a complete-as-possible nuance of this rapidly evolving Omicron subvariant. Genetic data suggest that BQ.1 represents an evolutionary blind background, lacking the rapid diversification that is typical of a dangerous lineage. Indeed, the evolutionary rate of BQ.1 is very similar to that of BA.5 (7.6 × 10⁻⁴ and 7 × 10⁻⁴ subs/site/year, respectively), which has been circulating for several months. The Bayesian Skyline Plot reconstruction indicates a low level of genetic variability, suggesting that the peak was reached around 3 September 2022. Concerning the affinity for ACE2, structure analyses (also performed by comparing the properties of BQ.1 and BA.5 RBD) indicate that the impact of the BQ.1 mutations may be modest. Likewise, immunoinformatic analyses showed moderate differences between the BQ.1 and BA5 potential B-cell epitopes. In conclusion, genetic and structural analyses on SARS-CoV-2 BQ.1 suggest no evidence of a particularly dangerous or high expansion capability. Genome-based monitoring must continue uninterrupted for a better understanding of its descendants and all other lineages. Full article

The accurate and reliable classification of rock mass is the basis of a reasonable engineering design. In the Xishan mining region of Sanshandao Gold Mine, three conventional rock mass classification methods of Tunneling Quality Index (Q), Rock Mass Rating (RMR) and China National Standard-basic quality (BQ), were compared in the burial depth area above 780 m, and it was discovered that the classification results of different rock mass classification methods had a low coincidence rate in the deep area; Therefore, this paper adopted entropy weight method, TOPSIS method and grey correlation analysis method to calculate the entropy weight and relative closeness of different methods in different middle sections. The study’s findings revealed that in the deep area, the relative closeness between each classification mass was: RMR > Q > BQ; Based on the above results, the IRMR method with modified RMR was selected for comprehensive analysis, and the concept of importance degree of evaluation index was defined; it was found that the importance degree of evaluation index of in-situ stress loss was the highest, while the importance degree of joint direction was the lowest; The “ETG” rock mass classification method based on “site-specific” is established, which provides a reference for the establishment of deep rock mass classification method. Full article

This study assesses the efficiency of the empirically recommended supported design of the underground powerhouse of the Panlong pumped-storage power station in Chongqing, China by using 3D distinct element code (3DEC). Field and laboratory tests were conducted to investigate the geological properties of intact rock and rock mass. The results showed that the stability of the large powerhouse may be controlled by the soft rock (mudstone) layers. The rock mass was classified in terms of the Q classification system, basic quality (BQ) method, and hydropower classification (HC) method, and then the supported system was put forward. The efficiency of the designed supported was checked based on the numerical simulation results of deformation and plastic zone. The results showed that the installed support reduces the radius of the plastic zones and the maximum deformation significantly. Full article

Radon (²²²Rn) and thoron (²²⁰Rn) account for almost two-thirds of the annual average radiation dose received by the Irish population. A detailed study of natural radioactivity levels and radon and thoron exhalation rates was carried out in a legislatively designated “high radon” area, as based on existing indoor radon measurements. Indoor radon concentrations, airborne radiometric data and stream sediment geochemistry were collated, and a set of soil samples were taken from the study area. The exhalation rates of radon (E²²²_Rn) and thoron (E²²⁰_Rn) for collected samples were determined in the laboratory. The resultant data were classified based on geological and soil type parameters. Geological boundaries were found to be robust classifiers for radon exhalation rates and radon-related variables, whilst soil type classification better differentiates thoron exhalation rates and correlated variables. Linear models were developed to predict the radon and thoron exhalation rates of the study area. Distribution maps of radon and thoron exhalation rates (range: E²²²_Rn [0.15–1.84] and E²²⁰_Rn [475–3029] Bq m⁻² h⁻¹) and annual effective dose (with a mean value of 0.84 mSv y⁻¹) are presented. For some parts of the study area, the calculated annual effective dose exceeds the recommended level of 1 mSv y⁻¹, illustrating a significant radiation risk. Airborne radiometric data were found to be a powerful and fast tool for the prediction of geogenic radon and thoron risk. This robust method can be used for other areas where airborne radiometric data are available. Full article