I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

The Relationship between Self-Esteem and Social Phobia among College Students:The Mediating Effect of Fear of Negative Evaluation and the Moderating Effect of Perfectionism

Objectives:To explore the relationship between college students’self-esteem(SE)and their social phobia(SP),as well as the mediating role of fear of negative evaluation(FNE)and the moderating effect of perfectionism.Methods:A convenience sampling survey was carried out for 1020 college students from Shandong Province of China,utilizing measures of college students’self-esteem,fear of negative evaluation,perfectionism,and social phobia.Data analysis was performed using the SPSS PROCESS macro.Results:(1)college students’self-esteem significantly and negatively predicts their social phobia(β=−0.31,t=−10.10,p<0.001);(2)fear of negative evaluation partially mediates the relation between self-esteem and social phobia among college students,with the mediating effect accounting for 48.97%of the total effect(TE);(3)the mediating role of fear of negative evaluation is moderated by perfectionism(β=0.18,t=7.75,p<0.001),where higher levels of perfectionism strengthen the mediating effect of fear of negative evaluation.Conclusions:Perfectionism moderates the mediating effect that fear of negative evaluation plays,establishing a moderated mediating model.

Establishment of acquired tracheoesophageal fistula using a modified magnetic compression technique in rabbits and its postmodeling evaluation

BACKGROUND Previous studies have validated the efficacy of both magnetic compression and surgical techniques in creating rabbit tracheoesophageal fistula(TEF)models.Magnetic compression achieves a 100%success rate but requires more time,while surgery,though less frequently successful,offers rapid model establishment and technical maturity in larger animal models.AIM To determine the optimal approach for rabbit disease modeling and refine the process.METHODS TEF models were created in 12 rabbits using both the modified magnetic compression technique and surgery.Comparisons of the time to model establishment,success rate,food and water intake,weight changes,activity levels,bronchoscopy findings,white blood cell counts,and biopsies were performed.In response to the failures encountered during modified magnetic compression modeling,we increased the sample size to 15 rabbit models and assessed the repeatability and stability of the models,comparing them with the original magnetic compression technique.RESULTS The modified magnetic compression technique achieved a 66.7%success rate,whereas the success rate of the surgery technique was 33.3%.Surviving surgical rabbits might not meet subsequent experimental requirements due to TEF-related inflammation.In the modified magnetic compression group,one rabbit died,possibly due to magnet corrosion,and another died from tracheal magnet obstruction.Similar events occurred during the second round of modified magnetic compression modeling,with one rabbit possibly succumbing to aggravated lung infection.The operation time of the first round of modified magnetic compression was 3.2±0.6 min,which was significantly reduced to 2.1±0.4 min in the second round,compared to both the first round and that of the original technique.CONCLUSION The modified magnetic compression technique exhibits lower stress responses,a simple procedure,a high success rate,and lower modeling costs,making it a more appropriate choice for constructing TEF models in rabbits.

Similarity evaluation model for the internal defect detection of strip steel

An internal defect meter is an instrument to detect the internal inclusion defects of cold-rolled strip steel.The detection accuracy of the equipment can be evaluated based on the similarity of the multiple detection data obtained for the same steel coil.Based on the cosine similarity model and eigenvalue matrix model,a comprehensive evaluation method to calculate the weighted average of similarity is proposed.Results show that the new method is consistent with and can even replace artificial evaluation to realize the automatic evaluation of strip defect detection results.

Research on the prognostic value of adjusting intraperitoneal threedimensional quality evaluation mode in laparoscopic cholecystectomy patients

BACKGROUND Benign gallbladder diseases have become a high-prevalence condition not only in China but also worldwide.The main types of benign gallbladder diseases include gallbladder polyps,acute and chronic cholecystitis,and gallstones,with gallstones being the most common,accounting for over 70%of cases.Although the mortality rate of benign gallbladder diseases is low,they carry obvious potential risks.Studies have shown that an increased incidence of benign gallbladder diseases can increase the risk of cardiovascular diseases and gallbladder cancer,resulting in a substantial disease burden on patients and their families.AIM To assess the medical utility of the Configuration-Procedure-Consequence(CPC)three-dimensional quality evaluation model in modulating the prognosis of laparoscopic cholecystectomy patients.METHODS A total of 98 patients who underwent laparoscopic cholecystectomy in our hospital from February 2020 to January 2022 were selected as the subjects.According to the random number table method,they were divided into a study group and a control group,with 49 patients in each group.The control group received routine perioperative care,while the study group had the addition of the CPC three-dimensional quality evaluation.The postoperative recovery-related indicators(time to first flatus,time to oral intake,time to ambulation,hospital stay),stress indicators(cortisol and adrenaline levels),distinctions in anxiety and RESULTS The time to first flatus,time to oral intake,time to ambulation,and hospital stay of the study group patients were obviously lower than those of the control group patients,with statistical significance(P<0.05).On the 1st day after admission,there were no obvious distinctions in cortisol and adrenaline levels in blood samples,as well as in the Self-Rating Anxiety Scale(SAS)and Self-Rating Depression Scale(SDS)scores between the study group and the control group(P>0.05).However,on the 3rd day after surgery,the cortisol and adrenaline levels,as well as SAS and SDS scores of the study group patients,were obviously lower than those of the control group patients(P<0.05).The study group had 2 cases of incisional infection and 1 case of pulmonary infection,with a total incidence of complications of 6.12%(3/49),which was obviously lower than the 20.41%(10/49)in the control group(P<0.05).CONCLUSION Implementing the CPC three-dimensional quality evaluation model for patients undergoing laparoscopic cholecystectomy can help accelerate their perioperative recovery process,alleviate perioperative stress symptoms,mitigate anxiety,depression,and other adverse emotions,and to some extent,reduce the incidence of perioperative complications.

Exploration of the Talent Cultivation Model for the Integration of Industry and Education in University Asset Evaluation

During the 14th Five Year Plan period,the main task of talent team construction in China’s asset appraisal industry was to develop innovative talent training models.Therefore,this article focuses on the talent cultivation model of integrating industry and education in asset evaluation in universities,systematically summarizes the theoretical and practical significance of research on asset evaluation talent cultivation models in universities,and explores the construction measures of asset evaluation talent cultivation models based on the integration of industry and education that meet social needs and the development of the times[1].At the same time,the strategy of constructing a deep integration talent training system was explored,guided by the integration of industry and education,to cultivate asset evaluation composite talents with strong practical skills.The aim is to provide a reference for improving the quality of asset evaluation professionals in China and promoting the development of asset evaluation talent training models.

Exploration of the Evaluation Method for the Soothing Effect of UV Induced Human Skin Damage Model

To explore the method of evaluating the soothing effect of human skin damage,a human skin damage model was established using UV light induction.Four test areas were set up,namely blank control area,UV damage prevention and soothing area,immediate soothing area after UV damage and soothing area after UV damage.Five skin parameters,including skin melanin,red pigment value,skin pigmentation value,a*value,and skin redness value,were used to characterize skin pigmentation before and after using the sample Changes in properties such as skin erythema and skin pigment.The results showed that the method showed significant changes in the skin condition of volunteers before and after using the sample,and could achieve a soothing effect,which has certain reference significance.

Course Design and Evaluation System Construction Under the STEM Education Mode

With the rapid development of information technology,Science,Technology,Engineering,and Mathematics(STEM)education,as a crucial model for nurturing innovative talents,is gaining increasing attention.However,challenges such as insufficient resources and a lack of diversity in teaching methods exist in its implementation.Against this backdrop,this article conducts an in-depth analysis of course design and evaluation system construction under the STEM education model.The aim is to explore effective teaching strategies and diversified evaluation methods,with the ultimate goal of enhancing the quality of education and cultivating students’comprehensive problem-solving skills.

Can We Estimate the Impacts of Fixed Links Using Current Transport Models?Experiences from Concept Evaluations of Fixed Links on E39

Transport analysis and impact evaluations are important input for decisions about infrastructure projects.The impacts on transport from fjord crossing tunnels or bridges are the foundation for the cost benefit analysis,and also the basis for estimating the income from toll collection.Based on experiences from concept evaluations of several fixed link projects on E39,and an ongoing overall analysis,we question the results from transport analysis made by the official tools for such analysis:the RTM(regional transport model)which estimates the demand for trips below 10 km,the NTM(national transport model),for trips of 10 km or more,and the freight transport model.Both the NTM and the freight transport model are integrated in the RTM in the net assignment stage.We will demonstrate strengths and weaknesses in the transport models by showing contra intuitive or questionable results using the model as it is.The following questions arose as the initial results from the transport model were presented:Are the transport models able to capture immediate as well as long-term impacts?How would different assumptions about the monetary costs on these projects affect the forecasted demand and the cost benefit analysis?Are there other and wider ranges of impacts,if the analysis covers the total coastal highway as a whole,compared to evaluating impacts of each fixed link project individually?Do we have enough data to include transport effects of wider impacts of the fixed link projects?We had to deal with these questions in the concept evaluations carried out for the various fixed links project and in the current overall evaluation.We would like to suggest improvements in the analysis tools and emphasize requirements for knowledge about impacts of fixed links projects.

Study on the Construction and Evaluation System of Clinical Midwifery Teaching Faculty under the New Nursing Model

Objective:To explore the impact of the construction of a clinical midwifery teaching faculty and the development of an evaluation system under the new nursing model on the current teaching quality.Methods:From July 2022 to March 2023,10 clinical teaching teachers and 20 midwifery interns from Beijing Anzhen Hospital affiliated with Capital Medical University were selected as the subjects of this study.The clinical teaching teachers and midwifery interns were divided into an observation group and a control group,with each group including 5 clinical teaching teachers and 10 midwifery interns.The observation group received daily management and evaluation under the new nursing model,while the control group received management and evaluation under the traditional nursing model.The teaching quality evaluation of clinical midwifery teaching teachers by midwifery interns,the exit exam scores of midwifery interns,and the scores of clinical teaching teachers’internship lectures and teaching rounds were compared between the two groups.Results:In the observation group,the scores for teaching attitude,teaching skills,and teaching management in the teaching quality evaluation of clinical midwifery teaching teachers were higher than those in the control group.The professional theory scores(91.28±3.64)and overall nursing comprehensive scores(92.56±4.38)of midwifery interns in the observation group were higher than those of midwifery interns in the control group(81.58±2.27 and 80.29±3.33,respectively).The scores for internship lectures(89.32±4.15)and teaching rounds(90.64±5.52)in the observation group were also significantly higher than those in the control group(80.46±3.28 and 81.24±4.38,respectively),and the differences were statistically significant(P<0.05).Conclusion:The management of the clinical midwifery teaching faculty under the new nursing model effectively improved the quality of clinical teaching.It significantly enhanced the teaching effectiveness of clinical teaching teachers and the proficiency of midwifery interns in clinical operations,making it worthy of promotion and use.

Comprehensive Benefit Evaluation of SZ Distributed Photovoltaic Power Generation Project Based on AHP-Matter-Element Extension Model

With the introduction of the“dual carbon goals,”there has been a robust development of distributed photovoltaic power generation projects in the promotion of their construction.As part of this initiative,a comprehensive and systematic analysis has been conducted to study the overall benefits of photovoltaic power generation projects.The evaluation process encompasses economic,technical,environmental,and social aspects,providing corresponding analysis methods and data references.Furthermore,targeted countermeasures and suggestions are proposed,signifying the research’s importance for the construction and development of subsequent distributed photovoltaic power generation projects.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat

In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.

Comparative evaluation of commercial Douchi by different molds:biogenic amines,non-volatile and volatile compounds

To provide new insights into the development and utilization of Douchi artificial starters,three common strains(Aspergillus oryzae,Mucor racemosus,and Rhizopus oligosporus)were used to study their influence on the fermentation of Douchi.The results showed that the biogenic amine contents of the three types of Douchi were all within the safe range and far lower than those of traditional fermented Douchi.Aspergillus-type Douchi produced more free amino acids than the other two types of Douchi,and its umami taste was more prominent in sensory evaluation(P<0.01),while Mucor-type and Rhizopus-type Douchi produced more esters and pyrazines,making the aroma,sauce,and Douchi flavor more abundant.According to the Pearson and PLS analyses results,sweetness was significantly negatively correlated with phenylalanine,cysteine,and acetic acid(P<0.05),bitterness was significantly negatively correlated with malic acid(P<0.05),the sour taste was significantly positively correlated with citric acid and most free amino acids(P<0.05),while astringency was significantly negatively correlated with glucose(P<0.001).Thirteen volatile compounds such as furfuryl alcohol,phenethyl alcohol,and benzaldehyde caused the flavor difference of three types of Douchi.This study provides theoretical basis for the selection of starting strains for commercial Douchi production.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

PARE:Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.

Evaluation of a Rapid Diagnostic Test, Boson Biotech SARS CoV-2 Ag, for the Detection of SARS-CoV-2 in Gabon

1) Background: Rapid and acurate diagnostic testing for case identification, quarantine, and contact tracing is essential for managing the COVID 19 pandemic. Rapid antigen detection tests are available, however, it is important to evaluate their performances before use. We tested a rapid antigen detection of SARS-CoV-2, based on the immunochromatography (Boson Biotech SARS-CoV-2 Ag Test (Xiamen Boson Biotech Co., Ltd., China)) and the results were compared with the real time reverse transcriptase-Polymerase chain reaction (RT-PCR) (Gold standard) results;2) Methods: From November 2021 to December 2021, samples were collected from symptomatic patients and asymptomatic individuals referred for testing in a hospital during the second pandemic wave in Gabon. All these participants attending “CTA Angondjé”, a field hospital set up as part of the management of COVID-19 in Gabon. Two nasopharyngeal swabs were collected in all the patients, one for Ag test and the other for RT-PCR;3) Results: A total of 300 samples were collected from 189 symptomatic and 111 asymptomatic individuals. The sensitivity and specificity of the antigen test were 82.5% [95%CI 73.8 - 89.3] and 97.9 % [95%CI 92.2 - 98.2] respectively, and the diagnostic accuracy was 84.4% (95% CI: 79.8 - 88.3%). The antigen test was more likely to be positive for samples with RT-PCR Ct values ≤ 32, with a sensitivity of 89.8%;4) Conclusions: The Boson Biotech SARS-CoV-2 Ag Test has good sensitivity and can detect SARS-CoV-2 infection, especially among symptomatic individuals with low viral load. This test could be incorporated into efficient testing algorithms as an alternative to PCR to decrease diagnostic delays and curb viral transmission.

Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation

Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical energy storage.However,the performance of MIBs is significantly influenced by numerous variables,resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement.Machine learning(ML),with its capability to solve intricate tasks and perform robust data processing,is now catalyzing a revolutionary transformation in the development of MIB materials and devices.In this review,we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years.We present an extensive overview of existing algorithms,elucidating their details,advantages,and limitations in various applications,which encompass electrode screening,material property prediction,electrolyte formulation design,electrode material characterization,manufacturing parameter optimization,and real-time battery status monitoring.Finally,we propose potential solutions and future directions for the application of ML in advancing MIB development.