Accurate Evaluation Research Articles

The functional subclasses of AT1 receptor autoantibody in patients with coronary heart disease

Recently, the identification of autoantibodies (AT1-AA) targeting the second extracellular loop of angiotensin II type 1 receptor (AT1R-ECII) in patients with coronary heart disease (CHD) offers a novel perspective on the interplay between immunity and cardiovascular disease. However, much remains unknown regarding the functional diversity of AT1-AA. In this study, we measured the levels of AT1-AA in the sera of 306 CHD patients and purified AT1-AA from patient’s sera (n = 127). The subclasses of AT1-AA were categorized based on their impact on intracellular calcium ([Ca2+]i) levels in mouse arterial smooth muscle cells (MASMCs). Our findings revealed 4 distinct [Ca2+]i response patterns indicating the existence of 4 functional subclasses named H1-, H2-, H3-, and H4-AT1-AA. The correlation analysis demonstrated a positive association between H1-AT1-AA and endogenous coagulation, as well as between H2-AT1-AA and exogenous coagulation; no significant correlation was observed between H3-AT1-AA and the indicators we analyzed. Conversely, H4-AT1-AA exhibited a negative correlation with both leukocyte number and bile acid levels. Logistic regression analysis showed that H2-AT1-AA possessed predictive value for severe CHD. Furthermore, in vitro experiments indicated that both H1- and H2-AT1-AA exerted cytotoxic effects on MASMCs, while H4-AT1-AA increased cell viability. Additionally, an AT1-AA-positive rat model was established by subcutaneously injecting with AT1R-ECII peptide, which produced four similar functional subclasses of rat AT1-AA upon active immunization. This study suggested that classifying different functional subclasses of AT1-AAs can facilitate more accurate evaluation of the condition and prognosis in patients with CHD, thereby providing a novel basis for clinical diagnosis and treatment.

Retraction Note: Accuracy evaluation of sports training actions based on grating ruler displacement sensor and joint recognition algorithm

Retraction Note: Accuracy evaluation of sports training actions based on grating ruler displacement sensor and joint recognition algorithm

Accuracy Evaluation of Fetal Foot Length and Correlation with Biparietal Diameter for Ultrasonographic Fetal Age Estimation in Uncomplicated Pregnant Women of Chattogram, Bangladesh

Background: Prediction and visual representation of developmental milestones of a growing fetus is most accurately done by ultrasonography. The present study was aimed to evaluate the correlation and accuracy of fetal foot length with fetal biparietal diameter to estimate fetal gestational age in Bangladeshi uncomplicated pregnant women of Chattogram district. Materials and methods: This cross-sectional observational study was conducted on 200 uncomplicated pregnant women of 15th to 28th weeks of gestation during the period from October 2021 to September 2022 in the Department of Anatomy, Chittagong Medical College, Chattogram. Fetal biometric parameters such as fetal foot length and biparietal diameter were measured by ultrasonography. For statistical analysis, Pearson’s correlation test and correlation-regression analysis was done and p-value was considered significant if it was <0.05 at 95% level of confidence. Results: Pearson’s correlation test showed that gestational age had strong positive and highly significant (p=0.000) correlation with both fetal foot length and biparietal diameter. The relation between gestational age and fetal foot length(r=0.986) is less strong than the relation between gestational age and biparietal diameter(r=0.988). Conclusion: This study revealed relationship between fetal foot length and gestational age regarding accuracy. Ultrasonographic measurement of fetal foot length can be practiced alongside traditional biometric parameters for pregnancy dating in modern medical science. IAHS Medical Journal Vol 6(2), December 2023; 35-39

Accuracy Evaluation of LiDAR-SLAM Based 2-Dimensional Modelling for Indoor Environment: A Case Study

The rapid development of sensor technologies has led to smaller sensor sizes and lower costs. Today, the easy purchase of sensors such as cameras, Light Detection and Ranging (LiDAR), Radio Detection and Ranging (RADAR), Inertial Measurement Unit (IMU), and Global Navigation Satellite Systems (GNSS) receivers has caused significant developments in many applications such as robotics and unmanned vehicles. Raw data is transformed into information and products thanks to the methods in which the sensor data are used alone or with sensor integration. Simultaneous Localization and Mapping (SLAM) is one of the critical methods in which the vehicle's location is determined and the environment is modeled. This method can realize applications using cameras, LiDAR, or RADAR sensors as detection sensors. This study aimed to model an indoor area with a two-dimensional (2D) LiDAR sensor placed on an Unmanned Ground Vehicle (UGV) and analyzed the accuracy of the produced model. Normal Distribution Transform (NDT) - Particle Swarm Optimization (PSO) algorithm was used to generate the 2D model from the collected LiDAR data. The NDT-PSO algorithm was run on the Robot Operating System (ROS) installed on the Jetson Nano Developer Kit, and a real-time 2D model of the working area was processed. The reference lengths of the 75 facades in the 232 m2 indoor space were measured using a total station and calculated with CAD software. Percent error values were evaluated by comparing the reference and model lengths of the facades.

Assessing microvascular invasion in HBV-related hepatocellular carcinoma: an online interactive nomogram integrating inflammatory markers, radiomics, and convolutional neural networks.

The early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC. 173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves. Inflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively. NLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.

Evaluation of Postoperative Anastomotic Patency in Lymphaticovenular Anastomosis Using Photoacoustic Imaging.

Lymphaticovenular anastomosis (LVA) is a surgical technique used to alleviate lymphedema by bypassing the lymphatic and venous vessels and facilitating lymphatic fluid drainage. Accurate evaluation of anastomotic patency is crucial for assessing LVA outcomes. Traditional near-infrared fluorescence lymphography has limitations, including fluorescence diffusion in subcutaneous fat and difficulty evaluating areas beneath the dermal backflow. Photoacoustic imaging (PAI) is a potential alternative for high-resolution visualization of lymphatic and blood vessels. We aimed to evaluate the utility of PAI for assessing LVA patency. Using the LUB0 PAI system, we examined patients who underwent LVA. Imaging was conducted using subcutaneously injected indocyanine green (ICG) to visualize lymphatic vessels. Results showed clear patency in some cases, inability to evaluate it in others, and confirmed occlusion in certain instances. While PAI provides valuable insights, challenges remain, including the potential for ambiguous results from the intermittent nature of lymphatic flow and difficulty visualizing low-ICG-concentration lymphatic vessels. Nonetheless, PAI offers a promising method for detailed 3D evaluation of anastomoses. It may improve surgical outcomes and contribute to future evidence in the field. Further advancements, including real-time video assessment, may enhance the accuracy and reliability of LVA patency evaluation.

An Adaptive Transfer Learning Framework for Functional Classification

In this paper, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial benefits under certain conditions than another dataset with an identical distribution to the target dataset. This observation renders the commonly-used debiasing step in the parameter-based transfer learning algorithm unnecessary under some circumstances to the classification problem. In particular, we propose two adaptive transfer learning algorithms based on the functional Distance Weighted Discrimination (DWD) classifier for scenarios with and without prior knowledge regarding informative sources. Furthermore, we establish the upper bound on the excess risk of the proposed classifiers, providing the statistical gain via transfer learning mathematically provable. Simulation studies are conducted to thoroughly examine the finite-sample performance of the proposed algorithms. Finally, we implement the proposed method to Beijing air-quality data, and significantly improve the prediction of the PM 2.5 level of a target station by effectively incorporating information from source datasets.

Evaluation of dimensional accuracy and surface roughness of lingual bracket slot –An in vitro study

The paradigm shift with the increasing number of adults and teens seeking aesthetic options for orthodontic treatment led to the increased demand for lingual orthodontics. When it comes to size and slot dimensions, lingual brackets are very different from labial brackets.With the rise of lingual orthodontics in our everyday practice, it's more critical than ever for practitioners to understand these potential bracket size variations. The resistance to sliding mechanics can occur if the contact angle between the archwire and bracket increases, this creates the need for precise bracket slot dimension. The amount of friction varies proportionally to the accuracy of the dimensions and the roughness of the bracket slot.To evaluate the precision of commercially available orthodontic lingual bracket slots in inch dimensions with manufacturers’ published dimensions using a stereomicroscope and to compare the surface roughness of commercially available orthodontic lingual bracket slots using an atomic force microscope.Lingual brackets from four different manufacturers were taken for evaluation of slot dimensions. Twenty brackets of each manufacturer were randomly selected. Trinocular Stemi 2000 Stereo Zoom Microscope with Digital Camera (Carl Zeiss, Germany) was used for measurement of bracket slot dimensions. An atomic Force Microscope (AFM) (Nanoscope® IV Di digital instrument, California, USA) was used to evaluate the surface roughness of lingual bracket slots. Comparison of dimensions between mesial processes and comparison of dimensions between distal processes showed that the difference was only marginal with no significant statistic value. Statistically significant results proved that slot dimensions were not precise as per the manufacturer’s standards for given lingual brackets and were oversized for all bracket systems. Statistically insignificant results showed that the bracket systems were similar concerning the surface roughness of the bracket.The analyzed series of lingual bracket systems exhibited significant differences with manufacturers’ standards in slot dimension, which will clinically result in torque play. Lack of standardization of slot dimensions during the manufacturing process may be clinically associated with undesirable tooth positioning and movement; inferring that the bracket systems were similar concerning the surface roughness of the bracket slot.

EXTENDED MONITORING AS SUPPORT IN NUMERICAL MODELLING OF COMPLEX HISTORICAL TIMBER STRUCTURE

To support the conservation efforts regarding a wooden church in Domachowo, extensive research and design work was required to strengthen its weakened structure. A variety of data and analyses are necessary to make an accurate assessment, including obtaining a computational model, monitoring the behavior of the structure and its response to external forces and also performing strength analyses and verifying them with measurement data. For accurate geometric evaluation, static and dynamic measurements were required. A mathematical model and a flowchart of the necessary tasks were developed, along with the selection and installation of measuring devices. For this particular structure, static measurements were made using an automatic total station and dynamic measurements using tilt sensors. The purpose of the analysis was to correlate the inertia measurements with the absolute tachymetric observations related to reference points fixed outside the object in order to accurately assess the behavior of the object. Another important issue was to model the column element under study in such a way that its horizontal displacements could be determined from the measured inclinations. The obtained results indicated the need to strengthen the joints of the two main parts of the structure in order to minimize the impact of dynamic weather conditions. The paper describes the measurement process, the method of calculating displacements and the correlation of both types of data. Selected results confirming the conclusions are also presented.

229 Awardee Talk: The quest for an ideal in-field embryo evaluation tool in cattle: Opportunities and challenges

Abstract Accurately evaluating bovine embryos to select the most viable ones for transfer and successful pregnancy has been a challenge since embryo transfer began in cattle over six decades ago. Despite more than 3,100 peer-reviewed studies on bovine embryo evaluation published in PubMed from 1964 to 2023, there is still no consensus or gold standard method for assessing their developmental potential. The evaluation “problem” in assisted reproduction extends beyond embryos to gametes as well. Numerous microscopic techniques (e.g., differential interference contrast, electron, fluorescent, time-lapse, and artificial intelligence-based microscopy) and non-microscopic methodologies (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, and nuclear magnetic resonance) have been tested to find an embryo evaluation technique superior to morphological evaluation. While many of these research tools can accurately determine embryo quality and viability, most are invasive, expensive, labor-intensive, technically complex, and time-consuming, making them impractical for in-field embryo evaluation. Employing complex methods like RNA sequencing, SNP chips, mass spectrometry, and multiphoton microscopy at thousands of embryo production and collection facilities is unrealistic, no matter how accurate they may be. Therefore, future research should focus on innovating field-friendly, simple benchtop tests using existing findings, particularly from omics-based research methodologies. Time-lapse monitoring and artificial intelligence-based automated image analysis also potentially provide accurate embryo evaluation. However, further research is necessary to develop economically feasible options for in-field applications. Our recent work has examined possible applications of nuclear magnetic resonance (NMR) imaging, label-free microscopy, particularly holographic microscopy techniques like gradient light interference microscopy (GLIM), spatial light interference microscopy (SLIM), and multiphoton holographic microscopy in IVF labs. The ideal embryo evaluation tool should be accurate, objective, non-invasive, affordable, and simple enough for widespread adoption by embryo production and collection facilities worldwide. Under current circumstances, it is doubtful that transcriptomics, proteomics, metabolomics, GLIM, SLIM, NMR, multiphoton holographic microscopy, and similar techniques can be directly used for in-field embryo evaluation. However, biomarkers identified by these methodologies could be used for in-field embryo evaluation by devising simple, affordable benchtop techniques. Further investigation on time-lapse microscopy (TLM) and AI-based automated image processing may make these methods more affordable and potentially adoptable by the masses. There is great potential for these methodologies to become widely used, provided they can be made more economically viable. In conclusion, the next generation of microscopy capable of providing objective markers for gamete and embryo quality is on the horizon.

A novel approach to the analysis of Overall Survival (OS) as response with Progression-Free Interval (PFI) as condition based on the RNA-seq expression data in The Cancer Genome Atlas (TCGA)

BackgroundOverall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS.ResultsBased on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI.ConclusionsBased on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

Asymmetric fiscal policies and digital economy development: An empirical analysis based on the global digital value chain perspective

The indicator system for evaluating the development level of the digital economy is multidimensional and dynamic. Accurate and scientific evaluation of the digital economy's development level is crucial. This study, based on the characteristics of the digital economy's development stages, evaluates the value creation dimension of the digital economy from the perspective of the global digital value chain. Additionally, it considers the access and usage dimensions to construct a comprehensive indicator system for evaluating the global digital economy development level. The results reveal significant disparities in digital economy development between countries, but also show that some developing countries have experienced rapid improvements in their digital economy development levels. In theoretical and empirical research, this paper utilizes the Barro Rule and the Cobb-Douglas production function, employing mathematical derivations combined with empirical regression analysis to explore the relationship and mechanisms between asymmetric fiscal policies and digital economy development during economic downturns. It finds that in the new era of the digital economy, governments often implement proactive asymmetric fiscal expenditure policies during downturns. Continuously promoting the development levels of national digital economies is an important mechanism for forming these characteristics of fiscal expenditures. Finally, this study proposes relevant policy recommendations, enriching academic research and policy discussions in the fields of financial fiscal policy and digital economy development.

An improved Chinese load code method for the evaluation of wind‐induced base shear force on base‐isolated buildings

AbstractThe isolator units in a seismically isolated structure shall have a surplus yield strength under wind loads. The isolation interface must have enough strength to state at an elastic stage under wind loads. This necessitates an accurate evaluation of wind‐induced base shear force. Initially, to calculate the equivalent static wind load (ESWL) and the base shear force of a base‐isolated building, the validity of the inertial wind load (IWL) method and Chinese load code (CLC) method, the simplification based on the IWL method, are examined via comparison with response‐history analysis results. Comparative analysis reveals that the IWL method was more accurate in evaluating the ESWL and the base shear force of base‐isolated building, while the CLC method underestimated them due to the following reasons: inaccurate fundamental modal shape, a reduced peak factor, the omission of the ESWL contribution from the isolation interface, and flawed assumptions of uniform mass distribution. Subsequently, an improved CLC method that combined the exponential modal shape and first modal generalized mass modification coefficient is proposed and verified by a case study. Compared with the CLC method, the fluctuating base shear force calculated by the improved CLC method increased by 10% in the case study. Finally, the effect of non‐uniformly distributed mass is further considered in the proposed method by devising a mass conversion coefficient, and the case study has also validated this method. Without considering the effect of the non‐uniform mass, the fluctuating base shear force will be underestimated by 7.8% in the case study.

Comparative Analysis of Machine Learning Models for Enhanced Software Fault Detection: Performance and Accuracy Evaluation

Software defect prediction expectation is a critical portion of program designing that tries to discover and anticipate bugs or imperfections in program frameworks some time recently they happen. The objective is to come up with strategies and models that offer assistance program advancement groups choose which testing assignments are most imperative and how to best utilize their assets. A few machine learning (ML) strategies are utilized to do this. To create forecasts, these frameworks utilize a parcel of distinctive information, like code measures, past imperfection information, and data approximately the coder. The reason of this study is to use different machine learning strategies to make a determining demonstrate for finding computer program bugs. The proposed demonstrate is utilized in tests on the KC2 dataset from NASA's Guarantee library as portion of the study. It was attempted and compared how well diverse machine learning strategies worked. This consider utilized the KC2 dataset and found that the Decision Tree strategy got 73.28% accuracy, Naïve Bayes got 85.96%, K-Nearest Neighbour (KNN) got 82.57%, Support Vector Machine (SVM) got 86.74%, and Random Forest got 84.20%. The study about appeared that these different models can precisely anticipate program bugs when the Guarantee dataset KC2 is utilized. These comes about appear that machine learning models can offer assistance anticipate bugs superior, which can offer assistance groups speed up their tests and make way better utilize of their assets. The study appears that employing a run of machine learning models can enormously move forward the precision of foreseeing computer program abandons. This gives us valuable data almost regions of program frameworks that are more likely to have bugs, which helps keep software quality high.

Focal cortical dysplasia lesion segmentation using multiscale transformer

ObjectivesAccurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images.MethodsThe core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics.ResultsExperimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods.ConclusionIntegration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET.Critical relevance statementThis multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images.Key PointsThe first transformer-based model was built to explore focal cortical dysplasia lesion segmentation.Integration of global and local features enhances the segmentation performance of lesions.A valuable benchmark for model development and comparative analyses was provided.Graphical

NDSRIs Crisis in Pharmaceuticals; Insights on Formation Pathways, Root Causes, Risk Management, and Novel Analytical Techniques.

The discovery of a new class of nitrosamine impurities called N-nitroso drug substance related impurities (NDSRIs) in pharmaceuticals has emerged as a significant challenge for the pharmaceutical sector due to their significant genotoxic and mutagenic effects. Regulatory bodies globally in active collaboration with all the concerned stake holders, are taking effective measures to prevent and control NDSRIs. This comprehensive review on NDSRIs discusses formation pathways, root cause analysis, acceptable intake limits, case studies, control strategies and regulatory responses pertaining to recent NDSRI incidents. This review discusses the novel liquid chromatographic techniques (LC- MS/MS, GC-MS/MS) used to identify and quantify of NDSRIs. This review would aid pharmaceutical professionals, R&D analytical and formulation scientists, and regulatory bodies in gaining deeper insights into the NDSRIs crisis, facilitating formulation of NDSRI-free drug products, and ensuring their sensitive detection with accurate risk evaluation.

A calcification subtraction method for postmortem coronary computed tomography angiography.

Although coronary computed tomography (CT) angiography is a useful tool for evaluating coronary artery lesions both ante- and postmortem, accurate evaluation of the lumen is difficult when highly calcified lesions are present, owing to overestimation of stenosis caused by blooming and partial volume artifacts. In clinical practice, to overcome this diagnostic problem, a subtraction method has been devised to remove calcification by subtracting the precontrast image from the contrast image. In this report, we describe a calcification subtraction method using image analysis software for postmortem coronary CT angiography. This method was devised based on preliminary experimental results showing that the most accurate subtraction was achieved using images reconstructed with a narrower field of view and bone kernel, resulting in higher spatial resolution. This subtraction method allowed evaluation of lumen patency and the degree of stenosis on contrast-enhanced images in a verification using actual specimens where evaluation of the lumen had been difficult because of high calcification. The results were morphologically similar to the macroscopic findings. This method allows more rapid and reliable lesion retrieval and is expected to be useful for postmortem coronary angiography in forensic practice.

Human thermal comfort model and evaluation on building thermal environment

A healthy and comfortable thermal environment has become the core focus on building environment evaluation. The key is to realize room temperature regulation based on the demand for human thermal comfort. The study aims to build the core of building thermal environment regulation, building thermal environment evaluation system, to achieve accurate evaluation and prediction of thermal comfort temperature for different type buildings and occupants. Considering the differences between traditional and smart buildings in building intelligence, whose thermal environment evaluation frameworks are proposed respectively. Taking the traditional buildings in a typical city from the severe cold zone as an example, the thermal comfort models of different type buildings, heating periods, and occupants are established. Furthermore, the corresponding correction factors are proposed for age, gender, BMI and metabolic rate. This study provides theoretical support for the evaluation of building thermal environments.

Exploring the influences of salinity on CO2 plume migration and storage capacity in sloping formations: A numerical investigation

Accurate evaluation of CO2 plume migration, potential leakage, and storage capacity are the main challenges for CO2 geological storage (CGS). This study develops a suite of numerical models, focusing on the Shiqianfeng Formation of the Shenhua-Carbon Capture and Storage (CCS) demonstration project in the Ordos Basin, China, aiming to numerically evaluate the influence of combining salinity, dip angle, and faulting factors on CO2 plume migration, storage amount, conversion of gas and dissolved phase states. Simulation results show that lower salinity and larger dip angles result in earlier CO2 leakage. Increasing salinity from 0.00 to S in a 10° sloping formation causes later CO2 leakage (205–260 years). Lower salinity enhances CO2 plume migration, injection amount, and dissolved-phase CO2. As the salinity decreased from S to 0.00 in a 3° sloping formation over 120 years of CO2 migration, the dissolved CO2 plume migration increased by 9.1 %, the total CO2 amount increased by 70.0 %, and the proportion of dissolved CO2 increased by 13.07 %. Compared to formation dip angle, salinity has a more significant impact on the plume migration of dissolved CO2. However, CO2 storage safety decreases with increasing dip angle. The conversion proportion (1.88 %–9.75 %) of gas-phase to dissolved CO2 increased with an increasing dip angle and decreasing salinity for the first 100 years after injection ceased. Thus, we conclude that salinity, dip angle and faulting are important factors during CGS, and this study may provide a practical reference for further studies for evaluating CO2 storage potential.

Estimation of rock mass permeability using relaxation time and P‐wave velocity

AbstractDue to the inherent unpredictability of geological conditions, tunnelling operations are often at risk of encountering water inrushes. Such incidents can lead to construction delays, impose financial strains and pose significant safety threats to the workers involved. Water‐bearing geological formations are the main triggers for such incidents, with factors such as the positioning, water quantity and permeability distribution of these formations being key to predicting the occurrence and severity of water inrush disasters. By leveraging the complex interplay among relaxation time, P‐wave velocity and permeability within the rock's physical properties, a series of indoor tests were conducted on 40 artificial reef limestone cores to extract the necessary parameters. Through the analysis of the data, the comprehensive permeability prediction model was established, and the correlation coefficient was 0.9420 between the model's predictions and actual measurements. At the same time, through theoretical and mechanism analysis, the relationship between permeability and relaxation time and the relationship between permeability and P‐wave velocity were analysed. Finally, 10 natural reef limestone samples were used to verify the accuracy of the model. The prediction model enables an accurate evaluation of tunnel permeability, thus providing a scientific basis for the mitigation of tunnel water inrush hazards.