Verification Of Model Research Articles

Quantitative Evaluation of Pre-Drilling Safety by Combining Analytic Hierarchy Process with Alternating Condition Expectation

In order to avoid potential personnel and financial losses, the evaluation of pre-drilling safety is of great importance in oil and gas exploration and development. This paper presents a method of evaluating pre-drilling safety through combining the Analytic Hierarchy Process (AHP) with the Alternating Condition Expectation (ACE) method. An indicator system with a 9-3-1 structure was established, incorporating various unrestricted variables to describe the technical factor. Additionally, nine membership functions and weights were determined in order to build the AHP model by connecting the independent variables in the basic layer to dependent variables in the middle layer. Four transformed functions were also formulated to construct the ACE model by linking the middle variables to the pre-drilling safety value in the final layer. A total of 28 sets of on-site drilling data from three oilfields were collected for the establishment and verification of the AHP-ACE model. Average absolute error (AAE) and average absolute relative error (AARE) of the model to predict the training data are 0.03 and 4.29%, respectively, whereas the AAE and AARE for verification samples are 0.03 and 4.51%, respectively. The sensitivity ranking of the three potential variables is as follows: human factor exhibits the highest degree of sensitivity, followed by natural factor and technical factor, in descending order. The AHP-ACE model for pre-drilling safety assessment faces limitations in universal applicability and scope, particularly in real-time drilling activities. However, its potential for improvement lies in integrating insights from past operations and expanding the dataset to enhance accuracy and broaden safety assessment coverage. This method is not limited by blocks, which is of great significance to ensure drilling safety.

Effect of magnetic field and key parameters values on natural convection and entropy generation of hybrid nanofluid in a corrugated enclosure

This work enlightens the magnetohydrodynamics (MHD) natural convective hydrothermal scenario and entropy generation analysis in a corrugated enclosure filled with MoS2–SiO2–water hybrid nanofluid. Three discrete uniform heaters are kept fixed to the enclosure bottom wall. The enclosure top wall and the remaining parts of its bottom wall are isothermally cooled. The enclosure has adiabatic vertical walls. A higher-order compact (HOC) finite difference technique is used to determine the solutions to the dimensionless governing equations. To demonstrate the accuracy of the mathematical model, experimental and numerical verifications are accomplished. The numerical investigation is accomplished for Rayleigh number ( Ra = 10 4 − 10 6 ), aspect ratios of heater length ( AR = 0.2 , 0.4 , 0.6 , 0.8 ), nanoparticle volume percentage ( ϕ = 0 − 4 % ), and Hartmann number ( Ha = 0 − 120 ). The numerical outcomes are presented and studied by means of isotherms, streamlines, and the average Nusselt number. Furthermore, local entropy generation resulting from heat transmission, fluidic friction, and magnetic field, along with overall entropy formation and Bejan number are analyzed. This study reveals that the aspect ratio AR = 0.2 enhances the convective flow process at Ra = 10 6 , even when a magnetic field is present. The heat transfer rate increases as a function of the Rayleigh number, while it exhibits a decreasing trend with the aspect ratio of heater length and Hartmann number. Total entropy generation reduces as the Hartmann number and aspect ratio of heater length increase. Bejan number graphs show a reverse pattern as compared to total entropy generation. It has been noted that an increase in the Hartmann number from 0 to 120 decreases the average Nusselt number and total entropy generation by 19.77 and 46.63%, respectively, for AR = 0.2 , Ra = 10 6 , and ϕ = 4 % .

Advanced intelligent monitoring technologies for animals: A survey

Effective animal intelligent monitoring is of great value in terms of ecological protection and endangered specie conservation. At present, computer vision technologies have shed light on animal intelligent monitoring. Especially, numerous deep learning-based models and methods have been developed to address various challenges, and have made substantial strides in this field. However, there are still several problems to be solved and related areas to be mined, such as exploring new strategies to enhance the robustness and generalization ability of models, designing novel models for complex environments, and establishing large-scale publicly available animal datasets for performance verification of models. Therefore, we comprehensively elaborated and analyzed existing works on animal intelligent monitoring based on advanced information science technologies, so as to provide useful information assistance for relevant researchers. In this paper, we focus on three primary task fields: precise animal localization, tracking and individual identification. Specifically, we elucidate the definition and significance of each monitoring task, and summarize the baseline models for addressing different problems. We provide a specific analysis of strategies and prototypes of the models and methods employed in each tasks following by the technical progression from traditional machine learning to deep learning. In addition, we make a comparison and analysis of the relevant methods, summarize their similarities and differences between them, and point out the advantages and disadvantages of these methods. Finally, we present several unresolved challenges and problems in animal intelligent monitoring and provide potential research directions in the future. We expect that our review can serve as reference and guidance for related research fields.

Antecedents and outcomes of cyberbullying among Chinese university students: verification of a behavioral pathway model.

Cyberbullying is a commonly-seen and hotly-debated social topic around the globe. This negative behavior is the source of many disastrous events, and so leading government bodies, organizations, schools and social communities attach great importance to addressing this topic. However, there is still much work to do in order to be clear about the causes of cyberbullying. The previous research cases were mostly viewed from the victims' perspectives; however, there is no comprehensive understanding of the perpetrators' viewpoints. Therefore, based on Social Cognitive Theory (SCT) and analysis of discussion in the literature, the following six variables were chosen as the focus of this study: overconfidence, excessive moral sense, cyberbullying, perceived value, happiness, and continued cyberbullying intention. This study established a research model of continued cyberbullying intention, which was verified by Structural Equation Modeling. In order to achieve the aims of the study, Chinese university students with an average age of 20.29 (SD = 1.43) were recruited as participants, from whom 1,048 valid questionnaires were collected. The research results are as follows: 1. Overconfidence and excessive moral sense positively predicted cyberbullying behaviors; 2. Overconfidence positively predicted excessive moral sense; 3. Cyberbullying positively predicted perceived value and sense of happiness; and 4. Perceived value and sense of happiness positively predicted continued cyberbullying intentions. Students' biased self-perception significantly predicts their cyberbullying behaviors and continued cyberbullying intention. What is more, it is interesting to learn that perpetrators will continue to exhibit cyberbullying behaviors when they believe that what they do (cyberbullying) is valuable or allows them to experience positive feelings; this requires our attention.

Verification of a Monte Carlo binary collision model for simulating elastic and inelastic collisions in particle-in-cell simulations

We present the development and verification of a Monte Carlo binary collision model for simulating elastic and inelastic collisions in particle-in-cell simulations. We apply the corrected binary collision model originally developed for charged-particles collisions to all considered scattering channels, including Coulomb collisions, elastic neutral–neutral and charged–neutral collisions, ionization, excitation, and fusion. The model's implementation is described and verified through a series of simulations, including charged- and neutral-particle thermal equilibration, slowing of electrons in warm solid-density aluminum, collisional damping of a Langmuir wave, helium gas breakdown in an applied electric field, and thermonuclear and beam–target fusion. Then, we demonstrate the model within simulations of hydrogen plasma formation in the Princeton Field-Reversed Configuration as well as of the burning of aneutronic fusion fuel p-11B. The latter includes measurement of the fusion power density in a low-density plasma and fusion production due to the stopping of a proton ignitor beam in a compressed boron target.

A three-dimensional numerical well-test model for pressure transient analysis in fractured horizontal wells with secondary fractures

During oil and gas reservoir development, multi-stage horizontal wells (MFHWs) and hydraulic fracturing techniques can effectively increase estimated ultimate recovery. However, there still lacks an understanding of the three-dimensional (3D) pressure transient behaviors of multi-stage fractured horizontal wells with secondary fractures. To narrow this gap, a three-dimensional numerical well-test model based on a discrete fracture model and unstructured tetrahedral grids is developed to study the pressure transient behaviors of MFHWs with secondary fractures. The pressure transient solutions of MFHWs with secondary fractures have been demonstrated by model verifications. The results show that the proposed model can accurately capture the complex transient flow around fractures, including early radial flow that is not easily captured by two-dimensional numerical well test models. The proposed model classifies the flow regimes of a MFHW as: wellbore storage and skin effects, early radial flow, bilinear flow, linear flow, elliptical flow, pseudo radial flow, and pseudo-boundary dominated flow. It is found that the fracture geometry has a relatively large effect on the shape of the pressure derivative curve in this work. The hydraulic fracture half-length has the greatest impact on the pressure transient behaviors of the MFHW, followed by fracture height and secondary fracture half-length, as found in this study. Additionally, fracture parameters are evaluated, and actual well testing data are interpreted, taking into account the fracture height. This work is meaningful to understand the three-dimensional pressure transient behaviors of MFHWs with secondary fractures.

Establishment and verification of a prognostic model based on coagulation and fibrinolysis-related genes in hepatocellular carcinoma.

Studies have shown that coagulation and fibrinolysis (CFR) are correlated with Hepatocellular carcinoma (HCC) progression and prognosis. We aim to build a model based on CFR-correlated genes for risk assessment and prediction of HCC patient. HCC samples were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. The Molecular Signatures Database (MSigDB) was used to select the CFR genes. RiskScore model were established by single sample gene set enrichment analysis (ssGSEA), weighted correlation network analysis (WGCNA), multivariate Cox regression analysis, LASSO regression analysis. PCDH17, PGF, PDE2A, FAM110D, FSCN1, FBLN5 were selected as the key genes and designed a RiskScore model. Those key genes were Differential expressions in HCC cell and patients. Overexpression PDE2A inhibited HCC cell migration and invasion. The higher the RiskScore, the lower the probability of survival. The model has high AUC values in the first, third and fifth year prediction curves, indicating that the model has strong prediction performance. The difference analysis of clinicopathological features found that a great proportion of high clinicopathological grade samples showed higher RiskScore. RiskScore were positively correlated with immune scores and TIDE scores. High levels of immune checkpoints and immunomodulators were observed in high RiskScore group. High RiskScore groups may benefit greatly from taking traditional chemotherapy drugs. We screened CFR related genes to design a RiskScore model, which could accurately evaluate the prognosis and survival status of HCC patients, providing certain value for optimizing the clinical treatment of cancer in the future.

Optimization of traditional methods for determining the similarity of project names and purchases using large language models

The subject of the study is the analysis and improvement of methods for determining the relevance of project names to the information content of purchases using large language models. The object of the study is a database containing the names of projects and purchases in the field of electric power industry, collected from open sources. The author examines in detail such aspects of the topic as the use of TF-IDF and cosine similarity metrics for primary data filtering, and also describes in detail the integration and evaluation of the effectiveness of large language models such as GigaChat, GPT-3.5, and GPT-4 in text data matching tasks. Special attention is paid to the methods of clarifying the similarity of names based on reflection introduced into the prompta of large language models, which makes it possible to increase the accuracy of data comparison. The study uses TF-IDF and cosine similarity methods for primary data analysis, as well as large GigaChat, GPT-3.5 and GPT-4 language models for detailed verification of the relevance of project names and purchases, including reflection in model prompta to improve the accuracy of results. The novelty of the research lies in the development of a combined approach to determining the relevance of project names and purchases, combining traditional methods of processing text information (TF-IDF, cosine similarity) with the capabilities of large language models. A special contribution of the author to the research of the topic is the proposed methodology for improving the accuracy of data comparison by clarifying the results of primary selection using GPT-3.5 and GPT-4 models with optimized prompta, including reflection. The main conclusions of the study are confirmation of the prospects of using the developed approach in the tasks of information support for procurement processes and project implementation, as well as the possibility of using the results obtained for the development of text data mining systems in various sectors of the economy. The study showed that the use of language models makes it possible to improve the value of the F2 measure to 0.65, which indicates a significant improvement in the quality of data comparison compared with basic methods.

Modeling and Experimental Verification of a Continuous Curvature-Based Soft Growing Manipulator

Modeling and Experimental Verification of a Continuous Curvature-Based Soft Growing Manipulator

Numerical Study of GFRP-reinforced Concrete Beams with Straight and Hooked-end Bar Lap Splices

Glass fibre-reinforced polymer (GFRP) has been increasingly used as the main reinforcement in concrete structures due to its durability and resistance to corrosion. However, there is a lack of data regarding the bond behaviour of GFRP bars with 180-degree hooked ends used in flexure concrete elements. To investigate the effects of the hooked ends of GFRP bars on the bond strength with concrete, beams reinforced with GFRP bars with straight and hooked-end lap splices at the midspan were modelled using ABAQUS/Standard software. The finite element models were validated using the experimental results of four full-size concrete beams. Two beam specimens had straight-end bars, while the other two had hooked-end bars. Sensitivity analysis was performed to find the proper values for model verification, such as dilation angle and mesh density. According to the outcomes of the parametric study conducted in this research, changing the length of the bar lap splices in the range of 15 to 40 times the bar diameter had a negligible effect on the beam’s overall behaviour. Increasing the number of reinforcing bars from 3 to 5 increased the beam flexural strength by about 33%, whereas increasing the diameter of the bars from 10 to 20 mm doubled the beam strength.

Model Exploration and Application of Near-Infrared Spectroscopy for Species Separation and Quantification during Mixed Litter Decomposition in Subtropical Forests of China

Litter of different species coexists in the natural ecosystem and may induce non-additive effects during decomposition. Identifying and quantifying the origins of species in litter mixtures is essential for evaluating the responses of each component species when mixed with co-occurring species and then unraveling the underlying mechanism of the mixing effects of litter decomposition. Here, we used near-infrared spectroscopy (NIRS) to predict the species composition and proportions of four-tree species foliage mixtures in association with litter crude ash and litter decomposition time. To simulate the whole mixed litter decomposition process in situ, a controlled mixture of four tree species litter leaves consisting of 15 tree species combinations and 193 artificial mixed-species samples were created for model development and verification using undecomposed pure tree species and decomposed litter of single tree species over one year. Two series of NIRS models were developed with the original mass and ash-free weight as reference values. The results showed that these NIRS models could provide an accurate prediction for the percentage of the component species from in the litter leaf mixture’s composition. The predictive ability of the near-infrared spectroscopy model declined marginally with the prolonged litter decomposition time. Furthermore, the model with ash-free litter mass as a reference exhibited a higher coefficient of determination (R2) and a lower standard error of prediction (RMSECV). Thus, our results demonstrate that NIRS presents great potential for not only predicting the organic composition and proportion in multi-species mixed samples in static conditions, but also for samples in dynamic conditions (i.e., during the litter decomposition process), which could facilitate evaluation of the species-specific responses and impacts on the interspecific interactions of co-occurring species in high-biodiversity communities.

Temperature regime of the cold period in the Arctic zone of Yakutia for 1960–2022

Changes in the Arctic have global consequences. An increase in air temperature and a change in the cold period can affect vulnerable ecosystems with a permafrost lithogenic base in the Yakutia Arctic region. In this study, we specified the dates of the beginning and end of the cold period in spring and autumn for two multi-year periods (1961–1990 and 1991–2022) of the northwestern and northeastern parts of the Arctic zone in Yakutia. We calculated the temperature difference and rate of change (C°/10 years) for 1960–2022. We used meteorological series according to the “RIHMI-WDC” data: st. Olenek (Oleneksky ulus), st. Zhigansk (Zhigansky) st. Chokurdakh (Allaikhovsky) and st. Srednekolymsk (Srednekolymsky). For the period 1991–2022 in the northwestern part of the Arctic zone in Yakutia, a reduction in the cold period was observed due to an earlier onset in spring (May: 4 days at Olenek station and 6 days at Zhigansk station) and in the northeastern part-later autumn (September: 3 days at Chokurdakh and Srednekolymsk stations). The results of our study indicate that in April, the difference in the average air temperature between 1961 and 1990 and 1991–2022 at all stations is higher than in October. The only exception is the information obtained according to st. Chokurdakh (October–2.6°C, April–2.1°C). The rate of change in air temperature (°С/10 years) for the period 1960–2022 at the beginning of the cold period (October), it manifested itself to a greater extent in the northeast of Yakutia (Srednekolymsk station – 0.7 °C/10 years, Chokurdakh –°C/10 years). In the middle (January – 1.1 °С/10 years) and the end (April – 1.0 °С/10 years) of the cold period, the maximum rate of air temperature change was noted in the north-west of Yakutia according to the data of st. Olenek.This study is necessary for the verification and selection of representative climate models, the analysis of the spatiotemporal variability of the cold period, and the forecasting and contribution to the overall dispersion of soil temperature.

Study on failure modes and calculation method of the cast steel joint with branches in treelike structure

The treelike structure links members and transfers loads via its solitary cast steel joint with branches. Therefore, the joint’s bearing capacity significantly affects the treelike structure’s stability, security, and economics. This paper utilized experimental verification and numerical modeling to examine the mechanical behavior of cast-steel joints with branches in the treelike structure under various loading conditions. Then, researchers investigated the failure process and mechanism of joints, and the three most common failure modes were outlined. Furthermore, the researchers proposed the bearing capacity calculation formula based on the common failure modes. The results show that the three common failure modes of the cast-steel joints with branches under different loading conditions are the failure in the joint core area under the axial load, the failure in the main pipe compression side under eccentric load, and the failure in the compression side of the single branch pipe root when the single branch pipe is under the uneven load. The suggested empirical calculation method can serve as a reference point for similar engineering practices design.

MODELING OF THE ELECTRONIC AND ELASTIC PROPERTIES OF THE Y<sub>3</sub>Al<sub>5</sub>O<sub>12</sub> CRYSTAL

In this scientific work, we conducted a comprehensive study of yttrium aluminum garnet using computer modeling methods. Our efforts are focused on the precise analysis of elastic properties, the development of equations of state, as well as a detailed study of crystal and band structures. We conducted a detailed study of the mechanical parameters of the material, such as the modulus of elasticity and the Poisson's ratio, using modern computational methods. Virtual modeling methods allow us to study in detail the mechanical characteristics of the material. The main focus is on the creation and verification of computer models that accurately reflect the elastic properties of the crystal. The values obtained using various hybrid functionals were compared with experimental data. Virtual models allowed not only to study in detail the mechanical characteristics of the material, but also provided results that are significantly close to real experimental data. Comparisons were carried out according to the following parameters: cell parameters, band gap energy, effective charges of crystal atoms, equations of state and elastic properties. Also, the error levels relative to the experimental data are determined. We present the results of the analysis of the crystal structure of yttrium aluminum garnet achieved using various exchange-correlation hybrid functionals such as B3PW, B3LYP, HSE06, PBE. This aspect of the study includes the analysis of the crystal lattice parameters, the distribution of atoms in the crystal and the band structure, which is important for a complete understanding of the properties of the material.

Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification

Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm running on a uniform 20 km grid over the continental USA and Alaska. This is the first and only high-resolution lightning forecasting model for North America derived from 29-year-long data records. The algorithm consists of a large set of regional logistic equations parameterized on the long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 principal components from a list of 611 potential predictors. Our analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: the temperature and geopotential heights across vertical pressure levels, the amount of low-level atmospheric moisture, and wind vectors. These physical variables isolate the conditions that are favorable for the development of thunderstorms and impact the vertical separation of electric charges in the lower troposphere during storms, which causes the voltage potential between the ground and the cloud deck to increase to a level that triggers electrical discharges. The results from a forecast verification using independent data showed excellent model performance, thus making this algorithm suitable for incorporation into models designed to forecast the chance of wildfire ignitions.

A nomogram prediction model for hungry bone syndrome in dialysis patients with secondary hyperparathyroidism after total parathyroidectomy

ObjectiveSecondary hyperparathyroidism (SHPT) is a common complication of chronic kidney disease (CKD). Hungry bone syndrome (HBS) after parathyroidectomy (PTX) is a serious complication, which can lead to diarrhea, convulsion, arrhythmia and even death. This study was aimed to determine the risk factors for HBS after PTX in dialysis patients with SHPT and construct a nomogram prediction model to predict the incidence of postoperative complications.MethodsClinical data were collected from 80 maintenance hemodialysis (MHD) patients with SHPT who received total PTX in the Second Hospital of Jilin University from January 2018 to September 2021. In line with the inclusion and exclusion criteria, totally 75 patients were finally enrolled for analysis. Patients were divided into two groups for retrospective analysis according to the severity of postoperative HBS, including HBS group and non-HBS (N-HBS) group. Univariate and multivariate logistic regression analyses were conducted to determine the risk factors for postoperative HBS. Afterwards, the receiver operating characteristic (ROC) curves were plotted based on the statistical analysis results, aiming to compare the prediction effects of different predicting factors. Finally, the nomogram was established to evaluate the occurrence probability of postoperative complications predicted by the risk factors.ResultsAmong the 75 patients, 32 had HBS (HBS group), while 43 did not have HBS (N-HBS group). Univariate analysis results indicated that, the preoperative intact parathyroid hormone (iPTH) and serum alkaline phosphatase (ALP) levels in HBS group were significantly higher than those in N-HBS group, while preoperative hemoglobin and preoperative albumin (Alb) levels were significantly lower than those in N-HBS group. As discovered by multivariate logistic regression analysis, preoperative iPTH (OR = 1.111, P = 0.029) and ALP (OR = 1.010, P < 0.001) were the independent risk factors for postoperative HBS. ROC curve analysis suggested that the area under the curve (AUC) values of these two indicators were 0.873 and 0.926, respectively (P < 0.0001). Subsequently, the nomogram model for predicting HBS was constructed. The model verification results indicated that the predicted values were basically consistent with the measured values, with the C-index of 0.943 (95% CI 0.892–0.994). Besides, the calibration curve was consistent with the ideal curve, demonstrating the favorable accuracy and discrimination of the model.ConclusionsPreoperative iPTH and preoperative ALP are the risk factors for postoperative HBS, which can be used to guide the early clinical intervention.

Errors in average strength estimates of sedimentary rock masses in South Yakutia based on geometric induction sounding data

The probabilistic model developed to describe the engineering and geological conditions for the operation of buildings and infrastructure in the city of Neryungri has been verified. The model is a power-function equation which correlates, by regression relationships, laboratory-determined compressive strength of saturated frozen sandstone samples with attenuation of the high-frequency harmonic field of a vertical magnetic dipole in warm permafrost composed of sandstone. The attenuation values were obtained by geometric electromagnetic induction sounding at a frequency of 1.125 MHz. The model verification was conducted at a fixed frequency on a section of the Amur-Yakutsk Railway located at a significant distance from Neryungri. The railway here is underlain by low-temperature dolomite masses. The statistical analysis indicates that the relative errors of the dolomite saturated strength estimates from the geometric EM induction data with a 73.0% probability do not exceed 20%. With this level of accuracy close to that of laboratory tests according to GOST 25100–2020, it is reasonable to suggest that the probabilistic model can be used region-wide in southern Yakutia to predict saturated strength of foundation materials composed of sandstone and dolomite. It can provide an effective solution for market-based construction and mining industries, as increasing anthropogenic and climatic impacts are anticipated to cause thawing and saturation of sedimentary rock masses.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.

Numerical investigation and dynamics of pollutant dispersion in underground restroom ventilation

Urbanization is expanding in both surface and underground space, where the air quality in underground space restrooms in public transportation hubs is a huge challenge due to variations in passenger’ flow and specific features. The pollution in the underground restrooms mainly comes from the microorganisms and odors of human waste. This paper proposes a back-side air exhaust method for the restroom compartment to enhance pollutants removal efficiency, and an optimal design study is performed to determine the best device position, evacuation pressure, and air exchange rate. In response to research gaps, we introduced the influence of unsteady human breathing behavior and human obstacles coupled with the flow field and the pollutants diffusion in the CFD model. After model verification, we numerically investigated the effect of different ventilation modes. The results show that the top evacuation mode may accelerate the diffusion of pollutants. In contrast, the pollutant concentration around the mouth and the spillage outward from the door cracks are significantly reduced under the new ventilation mode. The standardized and actual concentrations of the pollutant and the normalized exposure index were used to assess the indoor air quality. Finally, we put forward a series of dynamical models to describe the variation of indoor pollutant concentrations over time. The study's results will provide reliable theoretical and numerical support to improve the air quality of public restrooms in urban underground spaces.

Establishment and verification of invasion syndrome prediction model in patients with diabetes complicated with Klebsiella pneumoniae liver abscess

Objective: To analyze the correlative factors of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess, and to construct and verify the online nomographic prediction model. Methods: A case control study. The clinical data of 213 diabetic patients with Klebsiella pneumoniae liver abscess admitted to the Third Affiliated Hospital of Soochow University from January 1, 2015 to December 31, 2021 were retrospectively analyzed. The patients were divided into the training set (149 cases) and the test set (64 cases) by stratified random sampling method at a ratio of 7∶3. Synthetic minority over-sampling technique(SMOTE) was used to process the imbalanced data, then Lasso regression was used to screen out the optimal feature variables in the training set and multivariate logistic regression model was used to construct the prediction model of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess, and verify it in the training set and test set. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model, and the simple and online interactive dynamic web page column graph was constructed. Results: Among the 213 patients, 60 were males and 153 were females, aged of (61.4±12.0) years. A total of 25(11.74%) diabetic patients with Klebsiella pneumoniae liver abscess developed invasion syndrome, which were included in divided into invasive K.pneumoniae liver abscesses syndrome (IKPLAS) group, and the other 188 cases were in without invasive K.pneumoniae liver abscesses syndrome (NIKPLAS) group. SMOTE algorithm was used for oversampling processing, so that the ratio of positive and negative samples was 1∶1. In the oversampling training set, 5 main risk factors were screened based on Lasso regression, namely fasting blood glucose (λ=0.063), hemoglobin (λ=-0.042), blood urea nitrogen (λ=-0.050), abscess size (λ=-0.025) and sequential organ failure assessment (SOFA) score (λ=0.450), respectively. Multivariate logistic regression model showed that fasting blood glucose (OR=1.20, 95%CI: 0.98-1.48, P=0.006), hemoglobin (OR=0.90, 95%CI: 0.86-0.95, P<0.001), blood urea nitrogen (OR=1.22, 95%CI: 1.03-1.43, P=0.017), abscess diameter (OR=0.76, 95%CI: 0.61-0.94, P=0.010), SOFA score (OR=3.08, 95%CI: 2.18-4.36, P<0.001) were associated with invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess. The area under the curve of ROC in the training set was 0.966 (95%CI: 0.943-0.989), the sensitivity was 90.5%, and the specificity was 91.3%. The area under the curve of the validation set ROC was 0.946 (95%CI: 0.902-0.991), with a sensitivity of 79.6% and a specificity of 88.9%. The calibration curves drawn in the training set and the test set fit well with the ideal curve. DCA showed that the neomorph prediction model had a good clinical net benefit when predicting the risk of IKPLAS in patients with diabetes complicated with Klebsiella pneumoniae liver abscess was 0.10-0.40. Conclusions: Fasting blood glucose, hemoglobin, urea nitrogen, abscess size and SOFA score are the related factors for invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess. The constructed column graph can effectively predict the risk of invasion syndrome in patients with diabetes complicated with Klebsiae pneumoniae liver abscess.