Seismic performance evaluation of water supply pipes installed in a full-scale RC frame structure based on a shaking table test

As an important part of nonstructural components,the seismic response of indoor water supply pipes deserves much attention.This paper presents shaking table test research on water supply pipes installed in a full-scale reinforced concrete(RC)frame structure.Different material pipes and different methods for penetrating the reinforced concrete floors are combined to evaluate the difference in seismic performance.Floor response spectra and pipe acceleration amplification factors based on test data are discussed and compared with code provisions.A seismic fragility study of displacement demand is conducted based on numerical simulation.The acceleration response and displacement response of different combinations are compared.The results show that the combination of different pipe materials and different passing-through methods can cause obvious differences in the seismic response of indoor riser pipes.

Evaluation Test Research of DR Inspection System for Circumferential Welds in Long Distance Pipeline

Digital radiographic(DR)testing equipment has been widely promoted and applied in the inspection of circumferential welds in oil and gas pipelines.In order to establish a comprehensive quality control system for digital radiographic testing and fully evaluate the integrated system inspection ability of equipment,personnel,and processes,a scientific and standardized evaluation method to the system is very necessary.Here investigates the precedents of relevant non-destructive testing evaluation methods at home and abroad,considers the testing characteristics of DR equipment,develops a complete set of DR testing system evaluation procedures.It deeply studies the adaptability methods of program processes from defect production to slicing processing and data statistical calculation for digital radiographic testing evaluation.To check the repeatability and reliability of the detectable system,five process welds with 200 real metallographic defects were fabricated in the laboratory.From the detected results,the DR system has good repeatability in image quality,and the detectable defect size reaches 0.85 mm under achieving 90%detection probability at a confidence level of 95%,the error of detected defect length is±2 mm,and the error of detected defect localization is±5 mm.The qualitative and quantitative detection of defects are accurate and reliable.The test further confirmed the reliable detection ability of the DR detection system,and fully validated the scientific and practical evaluation method designed.The research on the evaluation test method can serve as an important link in the quality control system for the on-site application of digital ray equipment in long-distance pipelines.The designed program,test,and evaluation content can serve as an important basis for the formulation of relevant specifications or standards.

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.

Evaluation of urea breath test as a diagnostic tool for Helicobacter pylori infection in adult dyspeptic patients

In this editorial,we discuss the article in the World Journal of Gastroenterology.The article conducts a meta-analysis of the diagnostic accuracy of the urea breath test(UBT),a non-invasive method for detecting Helicobacter pylori(H.pylori)infection in humans.It is based on radionuclide-labeled urea.Various methods,both invasive and non-invasive,are available for diagnosing H.pylori infection,inclu-ding endoscopy with biopsy,serology for immunoglobulin titers,stool antigen analysis,and UBT.Several guidelines recommend UBTs as the primary choice for diagnosing H.pylori infection and for reexamining after eradication therapy.It is used to be the first choice non-invasive test due to their high accuracy,specificity,rapid results,and simplicity.Moreover,its performance remains unaffected by the distribution of H.pylori in the stomach,allowing a high flow of patients to be tested.Despite its widespread use,the performance characteristics of UBT have been inconsistently described and remain incompletely defined.There are two UBTs available with Food and Drug Administration approval:The 13C and 14C tests.Both tests are affordable and can provide real-time results.Physicians may prefer the 13C test because it is non-radioactive,compared to 14C which uses a radioactive isotope,especially in young children and pregnant women.Although there was heterogeneity among the studies regarding the diagnostic accuracy of both UBTs,13C-UBT consistently outperforms the 14C-UBT.This makes the 13C-UBT the preferred diagnostic approach.Furthermore,the provided findings of the meta-analysis emphasize the significance of precise considerations when choosing urea dosage,assessment timing,and measurement techniques for both the 13C-UBT and 14C-UBT,to enhance diagnostic precision.

Analytical evaluation of steady-state solute distribution in through- diffusion and membrane behavior test under non-perfectly flushing boundary conditions

The through-diffusion and membrane behavior testing procedure using a closed-system apparatus has been widely used for concurrent measurement of diffusion and membrane efficiency coefficients of low-permeability clay-based barrier materials.However,the common assumption of perfectly flushing conditions at the specimen boundaries could induce errors in analyses of the diffusion coefficients and membrane efficiencies.In this study,an innovative pseudo three-dimensional(3D)analytical method was proposed to evaluate solute distribution along the boundary surfaces of the soil-porous disks system,considering the non-perfectly flushing conditions.The results were consistent with numerical models under two scenarios considering different inflow/outflow positions.The proposed model has been demonstrated to be an accurate and reliable method to estimate solute distributions along the bound-aries.The calculated membrane efficiency coefficient and diffusion coefficient based on the proposed analytical method are more accurate,resulting in up to 50%less relative error than the traditional approach that adopts the arithmetic mean value of the influent and effluent concentrations.The retar-dation factor of the clay specimen also can be calculated with a revised cumulative mass approach.Finally,the simulated transient solute transport matched with experimental data from a multi-stage through-diffusion and membrane behavior test,validating the accuracy of the proposed method.

Evaluation of Interferon-Gamma Release Assay Testing and Tuberculin Skin Test for Early Diagnosis of Tuberculosis in Children and Adolescents

Background: This study aimed to evaluate the diagnostic value of interferon-γ release assay (IGRA), a sensitive microbiological diagnostic method, in children and adolescents with suspected tuberculosis in a country with a high burden of tuberculosis. Method: This study included 581 children and adolescents aged 4 - 19 years who were suspected of having tuberculosis, were latently infected with Mycobacterium tuberculosis, and had received at least one dose of BCG vaccine between April 17, 2019, and February 24, 2021. The study evaluated the TST results of 106 patients who had a positive Quantiferon test and were suspected of having tuberculosis. Results: The study included 581 patients aged between 4 and 19 years. Of these, 106 patients tested positive for the Quantiferon test, while 19 were indeterminate and 456 were negative. The Quantiferon test positivity rate was 18.24%. Among the 106 QFT-Plus-positive cases, 23 patients also tested positive for TST. The difference in distribution was found to be statistically significant. Conclusion: The QFT-Plus test is considered an alternative to TST and other microbiological diagnostic methods for early tuberculosis diagnosis, particularly in children and adolescents.

Evaluation of the effects of EPS composite soil replacement on the dynamic performance of caisson structure using shaking table tests

The seismic performance of a caisson structure under two types of models with a saturated sandy foundation(CSS)and an expanded polystyrene(EPS)composite soil foundation(CES)are studied using shaking table tests.The macro phenomena of the two different foundation models are described and analyzed.The effects of the replacement of EPS composite soil on seismic-induced liquefaction of backfill and the dynamic performance of a caisson structure are evaluated in detail.The results show that the excess pore water pressure generation in the CES is significantly slower than that in the CSS during the shaking.The dynamic earth pressure acting on the caisson has a triangular shape.The response of horizontal acceleration,displacement,settlement,and rotation angle of the caisson in the CES is smaller than that in the CSS,which means the caisson in the CES has a better seismic performance.Furthermore,the out-of-phase phenomenon between dynamic earth thrust and inertial force in the CES is more obvious than that in the CSS,which is beneficial to reduce the lateral force and improve the stability of the caisson structure.

Performance and Safety Improvement of Induction Motors based on Testing and Evaluation Standards

The induction motor,which converts electrical energy into mechanical energy,has been recognized as the cornerstone of industrialization.The rotor of an induction motor can be either a squirrel cage rotor or a wound-type rotor,both existing as magnetless topologies.Three-phase squirrel cage induction motors are frequently utilized in industrial drives because they are dependable,have high starting torque,are selfstarting and affordable.Single-phase induction motors,on the other hand,are commonly used for small loads such as domestic appliances in form of modest fans,pumps and electric power tools.In South Africa,there have been reports of fires and explosions resulting in live and property loss because of induction motors that have not been thoroughly tested or are incorrectly labelled in terms of ratings,electrical safety and performance.The goal of this study is targeted at preventing end-user injuries and failures caused by non-compliant induction motors,by evaluating locally manufactured/imported induction motors based on tests and evaluation from standards(IEC and SANS).The study is conducted using experimental procedures at the Explosion Prevention Technology and Rotating Machines(EPT and RM)laboratory,South African Bureau of Standards(SABS),South Africa.The main finding from the study shows differences in the nameplate characteristics of various induction motors which could have detrimental effects such as production and operational downtime in their end-use industries,at later stages.

Flexible pavement longitudinal joint quality evaluation using non-destructive testing

Longitudinal joint construction quality is critical to the life of flexible pavements.Maintaining deteriorated longitudinal joints has become a challenge for many highway agencies.Improving the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less maintenance.Current quality control(QC)and quality assurance(QA)plans provide limited coverage.Consequently,the risk of missing areas with poor joint compaction is significant.A density profiling system(DPS)is a non-destructive alternative to conventional destructive evaluation methods.It can provide quick and continuous real-time coverage of the compaction during construction in dielectrics.The paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPS to evaluate the joint's compaction quality.The paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal joint.The paper proposes a dielectric-based longitudinal joint quality index(LJQI)to evaluate the relative compaction of the joint during construction.It also shows that adopting DPS for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints,identify locations of poor quality during construction,and achieve better-performing flexible pavements.

Evaluation of Digits-in-Noise Test and Hearing Handicap Inventory for Adults Screening in Patients with Occupational Noise-Induced Hearing Loss

Objective:To explore the clinical evaluation role of the Digits-in-Noise(DIN)test and Hearing Handicap Inventory for Adults Screening(HHIA-S)for patients with occupational noise-induced hearing loss and to observe and analyze their application values.Methods:Fifty patients with suspected occupational noise-induced hearing loss were randomly selected from the Department of Otolaryngology at the hospital as the research target.The collection period for the research cases spanned from January 2022 to November 2023,and all patients had a history of noise exposure.The DIN test and HHIA-S were used for hearing examinations,with clinical,comprehensive diagnosis serving as the gold standard to study their diagnostic performance.Results:The compliance rate of the DIN test was 88.00%,the HHIA-S’s compliance rate was 80.00%,and the combined compliance rate was 94.00%.The compliance rate of the DIN test and the combined compliance rates of the patients were statistically significant compared to the clinical gold standard data(P<0.05),while there was no difference between the compliance rate of the HHIA-S and the gold standard(P>0.05).The data shows that the sensitivity of the combined diagnosis is significantly higher than the sensitivity data of the DIN test and HHIA-S examination alone(P<0.05).Its specificity is 100.00%,and the accuracy data of the joint diagnosis in the degree were higher than those of the DIN test alone(P>0.05)and the HHIA-S alone(P<0.05).Conclusion:For patients with occupational noise-induced hearing loss,the joint evaluation of the DIN test and HHIA-S can significantly improve their diagnostic value with high sensitivity and accuracy.

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.

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.

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.

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.

Identification and evaluation of shale oil micromigration and its petroleum geological significance

Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale.

Comprehensive Evaluation of Flower Border Application Value of New and Superior Plants in Hefei Area

An analytic hierarchy process(AHP)was employed to assess the applicability of 18 new and superior varieties of flowers in Hefei City flower border applications.A total of 12 indicators were selected from three distinct aspects of adaptability,ornamental characteristics and use traits,in order to establish a comprehensive evaluation model.The results demonstrate that grade I(J≥2.685)exhibits excellent application value,encompassing six species of plants,such asHydrangeamacrophylla‘Endless Summer’;grade II(2.684≤J≤2.420)is also of notable application value,encompassing five species of plants,such asCallistemonrigidus;grade III(2.419≤J≤2.615)is of average application value,including five species of plants,such asCrocosmiacrocosmiflora;grade IV(J≤2.16)is of relatively poor application value.The evaluation results may be utilized as a theoretical reference for the promotion of new and superior varieties in the flower border of Hefei.

Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank

In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.

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.

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.