Analysis of the integrated test and evaluation methods of tidal current energy generating devices in the offshore testing site

Actual sea condition testing and inspection and evaluation method research are carried out for tidal energy devices to provide scientific and effective technical support for the ocean high-tech achievement transformation and marine renewable energy development. By analyzing three core indicators, including the power output characteristics of the tidal current device, the generating capacity, energy conversion efficiency, proposed the test contents and evaluation methods of indicators are proposed in this paper; and based on the research of wind farms, power quality testing and assessment methods of offshore tidal energy device are proposed; given the security access to the test contents of tidal current energy device, tidal current energy device running conditions in the testing ground are comprehensively assessed.

Towards Integrated Testing Approach: An Application of Cognitive Science and Deep Learning Principle

The use of multiple-choice(MC)question types has been one of the most contentious issues in language testing.Much has been said and written about the use of MC over the years.However,no attempt has ever been made to introduce any innovation in test item types.The researchers proposed a jumbled words test item(JW)based on cognitive science and deep learning principles,and addressed the feasibility of replacing the type of multiple-choice(MC)question with JW to meet the ongoing rapid development of language testing practice.Two research questions were proposed ad hoc,focusing on the co-relationship between JW and MC scores.RASCH-GZ was used to perform item analyses(Rasch,1960).The item difficulty parameters thus obtained were used to compare the two different test items.The sample data metric includes 40 Chinese participants.The findings revealed that correlation analysis revealed that the performance of the same group of subjects taking both JW and MC was not relevant(Pearson Corr=0).This is primarily due to the total elimination of guessing factors inherent in test-takers during JW test performance.Three factors were specified for the design of the JW test:compute program,test difficulty,and score acceptability.These all have three dimensions.Data collected through questionnaires were analyzed using EFA in SPSS V.24.0.KMOs(=0.867)were found to be approximately one and significance at 0.000(0.05),indicating that the construct of theuestionnaire thus designed has better validity for factor analysis.Three important conclusions were obtained,the implications of which could provide impetus for our testing counterparts to practice more precisely and correctly,potentially reshaping our overall language testing practice.Limitations and recommendations for future research were also discussed.

Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

Test system of the front-end readout for an application-specific integrated circuit for the water Cherenkov detector array at the large high-altitude air shower observatory

The water Cherenkov detector array(WCDA) is an important part of the large high-altitude air shower observatory(LHAASO),which is in a research and development phase.The central scientific goal of LHAASO is to explore the origin of high-energy cosmic rays of the universe and to push forward the frontier of new physics.To simplify the WCDA's readout electronics,a prototype of a front-end readout for an application-specific integrated circuit(ASIC) is designed based on the timeover-threshold method to achieve charge-to-time conversion.High-precision time measurement and charge measurement are necessary over a full dynamic range[1-4000photoelectrons(P.E.)].To evaluate the performance of this ASIC,a test system is designed that includes the front-end ASIC test module,digitization module,and test software.The first module needs to be customized for different ASIC versions,whereas the digitization module and test software are tested for general-purpose use.In the digitization module,a field programmable gate array-based time-todigital converter is designed with a bin size of 333 ps,which also integrates an inter-integrated circuit to configure the ASIC test module,and a universal serial bus interface is designed to transfer data to the remote computer.Test results indicate that the time resolution is better than 0.5 ns,and the charge resolution is better than 30%root mean square(RMS) at 1 P.E.and 3%RMS at 4000 P.E.,which are beyond the application requirements.

Integrated Design and Accuracy Analysis of Star Sensor and Gyro on the Same Benchmark for Satellite Attitude Determination System

As an important sensor in the navigation systems,star sensors and the gyro play important roles in spacecraft attitude determination system.Complex environmental factors are the main sources of error in attitude determination.The error influence of different benchmarks and the disintegration mode between the star sensor and the gyro is analyzed in theory.The integrated design of the star sensor and the gyro on the same benchmark can effectively avoid the error influence and improves the spacecraft attitude determination accuracy.Simulation results indicate that when the stars sensor optical axis vectors overlap the reference coordinate axis of the gyro in the same benchmark,the attitude determination accuracy improves.

An innovative classification system for ranking the biological effects of marine aromatic hydrocarbons based on fish embryotoxicity

Petroleum hydrocarbon pollution is a global concern,particularly in coastal environments.Polycyclic aromatic hydrocarbons(PAHs) are regarded as the most toxic components of petroleum hydrocarbons.In this study,the biomonitoring and ranking effects of petroleum hydrocarbons and PAHs on the marine fish model Oryzias melastigma embryos were determined in the Jiulong River Estuary(JRE) and its adjacent waters in China.The results showed that the levels of petroleum hydrocarbons from almost all sites met the primary standard for marine seawater quality,and the concentrations of the 16 priority PAHs in the surface seawater were lower compared with those in other coastal areas worldwide.A new fish expert system based on the embryotoxicity of O.melastigma(OME-FES) was developed and applied in the field to evaluate the biological effects of petroleum hydrocarbons and PAHs.The selected physiological index and molecular indicators in OME-FES were appropriate biomarkers for indicating the harmful effects of petroleum hydrocarbons and PAHs.The outcome of OME-FES revealed that the biological effect levels of the sampling sites ranged from level Ⅰ(no stress) to level Ⅲ(medium stress),which is further corroborated by the findings of nested analysis of variance(ANOVA) models.Our results suggest that the OME-FES is an effective tool for evaluating and ranking the biological effects of marine petroleum hydrocarbons and PAHs.This method may also be applied to evaluate other marine pollutants based on its framework.

A new method to evaluate integrated production system capacity in oil fields

To improve the design and management of an integrated production system(IPS),a set of mathematical models and workflows are developed for evaluating the capacity of an IPS at steady-state conditions.Combining the conservation laws with applicable multiphase fluid and choke models,these mathematical models are solved to characterize the hydraulics of an integrated system of reservoir,wells,chokes,flowlines,and separator at steady state.The controllable variables such as well count,choke size and separator pressure are adjusted to optimize the performance of the IPs at a specific time.It is found that increasing the well count can increase the bulk flow rate of the production network,but too many wells may increase the manifold pressure,leading to decline of single-well production.Increasing the choke size can improve the capacity of the IPs.The production of the IPs is negatively correlated with the separator pressure.With increasing separator pressure and decreasing choke size,the increment of total fluid production(the capacity of IPS)induced by increasing well count decreases.Validation tests with field examples show a maximum absolute deviation is 1.5%,demonstrating the robustness and validity of the proposed mathematical models and workflows.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.

Genetic testing vs microforceps biopsy in pancreatic cysts:Systematic review and meta-analysis

BACKGROUND Carcinoembryonic antigen(CEA)and cytology in pancreatic cystic fluid are suboptimal for evaluation of pancreatic cystic neoplasms.Genetic testing and microforceps biopsy are promising tools for pre-operative diagnostic improvement but comparative performance of both methods is unknown.AIM To compare the accuracy of genetic testing and microforceps biopsy in pancreatic cysts referred for surgery.METHODS We performed a literature search in Medline,Scopus,and Web of Science for studies evaluating genetic testing of cystic fluid and microforceps biopsy of pancreatic cysts,with endoscopic ultrasound with fine-needle aspiration(EUSFNA)prior to surgery and surgical pathology as reference standard for diagnosis.We evaluated the diagnostic accuracy for:1-benign cysts;2-mucinous low-risk cysts;3-high-risk cysts,and the diagnostic yield and rate of correctly identified cysts with microforceps biopsy and molecular analysis.We also assessed publication bias,heterogeneity,and study quality.RESULTS Eight studies,including 1206 patients,of which 203(17%)referred for surgery who met the inclusion criteria were analyzed in the systematic review,and seven studies were included in the meta-analysis.Genetic testing and microforceps biopsies were identical for diagnosis of benign cysts.Molecular analysis was superior for diagnosis of both low and high-risk mucinous cysts,with sensitivities of 0.89(95%CI:0.79-0.95)and 0.57(95%CI:0.42-0.71),specificities of 0.88(95%CI:0.75-0.95)and 0.88(95%CI:0.80-0.93)and AUC of 0.9555 and 0.92,respectively.The diagnostic yield was higher in microforceps biopsies than in genetic analysis(0.73 vs 0.54,respectively)but the rates of correctly identified cysts were identical(0.73 with 95%CI:0.62-0.82 vs 0.71 with 95%CI:0.49-0.86,respectively).CONCLUSION Genetic testing and microforceps biopsies are useful second tests,with identical results in benign pancreatic cysts.Genetic analysis performs better for low-and high-risk cysts but has lower diagnostic yield.

Integrated analysis of comorbidity, pregnant outcomes, and amniotic fluid cytogenetics of fetuses with persistent left superior vena cava

BACKGROUND Persistent left superior vena cava(PLSVC)is the most common venous system variant.The clinical characteristics and amniotic fluid cytogenetics of fetuses with PLSVC remain to be further explored.AIM To develop reliable prenatal diagnostic recommendations through integrated analysis of the clinical characteristics of fetuses with PLSVC.METHODS Cases of PLSVC diagnosed using prenatal ultrasonography between September 2019 and November 2022 were retrospectively studied.The clinical characteristics of the pregnant women,ultrasonic imaging information,gestational age at diagnosis,pregnancy outcomes,and amniocentesis results were summarized and analyzed using categorical statistics and the chi-square test or Fisher’s exact test.RESULTS Of the 97 cases diagnosed by prenatal ultrasound,49(50.5%)had isolated PLSVC and 48(49.5%)had other structural abnormalities.The differences in pregnancy outcomes and amniocentesis conditions between the two groups were statistically significant(P<0.05).No significant differences were identified between the two groups in terms of advanced maternal age and gestational age(P>0.05).According to the results of the classification statistics,the most common intrac-ardiac abnormality was a ventricular septal defect and the most common extrac-ardiac abnormality was a single umbilical artery.In the subgroup analysis,the concurrent combination of intra-and extracardiac structural abnormalities was a risk factor for adverse pregnancy outcomes(odds ratio>1,P<0.05).Additional-ly,all abnormal cytogenetic findings on amniocentesis were observed in the comorbidity group.One case was diagnosed with 21-trisomy and six cases was diagnosed with chromosome segment duplication.CONCLUSION Examination for other structural abnormalities is strongly recommended when PLSVC is diagnosed.Poorer pregnancy outcomes and increased amniocentesis were observed in PLSVC cases with other structural abnor-malities.Amniotic fluid cytogenetics of fetuses is recommended for PLSVC with other structural abnormalities.

Analysis of Toxicological Test Results of 580 Detergents

To evaluate the safety of detergents,the toxicological test results of 580 collected detergents were analyzed.The results showed that,in the acute oral toxicity test,94.4%and 5.6%of the samples were classified as non-toxic and toxic respectively.In skin irritation test,95.1%of the samples were mild irritation or below,and 4.9%were moderate irritation or above.In the vaginal mucosa irritation test,79.0%of the samples were very mild irritation or below,while 21.0%were mild irritation or above.In the acute inhalation toxicity test,the proportion of the samples classified as actually non-toxic and toxic were 87.9%and 12.1%respectively.In the eye irritation test,95.7%of the samples belonged to mild irritation or below,and 4.3%of the samples belonged to moderate irritation or above.The products at risk in category A,B and C products account for 6.3%,7.7%and 14.2%respectively.Of the 580 tested samples,90.9%were relatively safe,while 9.1%were at risk.These data showed that most of the tested samples meet the safety requirements for use.

Quantitative Analysis of Seeing with Height and Time at Muztagh-Ata Site Based on ERA5 Database

Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.

An Empirical Application of User-Guided Program Analysis

Although static program analysis methods are frequently employed to enhance software quality,their efficiency in commercial settings is limited by their high false positive rate.The EUGENE tool can effectively lower the false positive rate.However,in continuous integration(CI)environments,the code is always changing,and user feedback from one version of the software cannot be applied to a subsequent version.Additionally,people find it difficult to distinguish between true positives and false positives in the analytical output.In this study,we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true positives.On the three projects ethereum,go-cloud,and kubernetes,we assessed our methodologies.According to the trial findings,EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of reports.We paired our techniques with GoInsight~1 and discovered a vulnerability.We also offered a patch to the community.

昆明烟区土壤线虫数量与环境因子的Mantel Test分析

为进一步明确昆明烟区影响线虫的环境因素,于2022年采集云南昆明烟区8个县(市、区)64个乡镇的320个植烟土壤样品,调查与测定样品采集地的海拔、土壤线虫数量以及土壤理化性质(pH值及有机质、水解性氮、速效钾和有效磷含量),并探讨土壤线虫数量与植烟土壤环境因子之间的潜在关系。结果表明,昆明烟区土壤线虫数量一定程度上受到海拔及土壤理化性质的影响,海拔与有机质、水解性氮和速效钾呈显著正相关,pH值与有效磷呈极显著负相关,相关系数为-0.850;土壤线虫数量与土壤有效磷呈正相关,与海拔、pH值、有机质、水解性氮、速效钾呈负相关,其中与海拔、有机质和水解性氮的相关系数较大,分别为-0.751、-0.651和-0.568。通过Mantel test分析确定海拔、有机质和水解性氮含量是影响昆明烟区土壤线虫数量的重要因子。本结果可为有效防治土壤线虫提供科学依据。

CAUCHY TYPE INTEGRALS AND A BOUNDARY VALUE PROBLEM IN A COMPLEX CLIFFORD ANALYSIS

Clifford analysis is an important branch of modern analysis;it has a very important theoretical significance and application value,and its conclusions can be applied to the Maxwell equation,Yang-Mill field theory,quantum mechanics and value problems.In this paper,we first give the definition of a quasi-Cauchy type integral in complex Clifford analysis,and get the Plemelj formula for it.Second,we discuss the H?lder continuity for the Cauchy-type integral operators with values in a complex Clifford algebra.Finally,we prove the existence of solutions for a class of linear boundary value problems and give the integral representation for the solution.

Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.

Scheme of Railway 5G Integrated Innovation Test Platform

An inevitable trend has already taken shape for the application of the 5th Generation Mobile Communication Technology(5G)in the railway sector.The application scenarios and business types of the railway sector are complex and diverse,so it is indispensable to test and verify the railway 5G before actual deployment.The design and creation of the railway 5G integrated innovation test platform provides engineering design,test and verification conditions for the networking,coverage and business development of 5G public networks and 5G-R in railway scenarios.This paper introduces the design of the overall architecture for the integrated railway 5G innovation test platform according to the railway network requirements,application scenarios and intelligent development trend;respectively elaborates on the design of the 5G-R core network,bearer network and wireless access along loop tracks,in combination with the characteristics of railway scenarios and the requirements of railway dispatching,operation and safety;raises further solutions on the network deployment and coverage schemes of 5G public networks so as to meet the application requirements of 5G public networks.The study results show that the integrated railway 5G innovation test platform scheme contains co-existence of 5G public and private networks,combines the indoor and outdoor scenarios,as well as takes into account of the dynamic and static tests so as to meet the environmental requirements for the integrated railway 5G test and application of all network functions,for which it can provide comprehensive technical support for railway 5G technology research and development,standard formulation,testing,etc.

Isogeometric Analysis of Hyperelastic Material Characteristics for Calcified Aortic Valve

This study explores the implementation of computed tomography(CT)reconstruction and simulation techniques for patient-specific valves,aiming to dissect the mechanical attributes of calcified valves within transcatheter heart valve replacement(TAVR)procedures.In order to facilitate this exploration,it derives pertinent formulas for 3D multi-material isogeometric hyperelastic analysis based on Hounsfield unit(HU)values,thereby unlocking foundational capabilities for isogeometric analysis in calcified aortic valves.A series of uniaxial and biaxial tensile tests is executed to obtain an accurate constitutive model for calcified active valves.To mitigate discretization errors,methodologies for reconstructing volumetric parametric models,integrating both geometric and material attributes,are introduced.Applying these analytical formulas,constitutive models,and precise analytical models to isogeometric analyses of calcified valves,the research ascertains their close alignment with experimental results through the close fit in displacement-stress curves,compellingly validating the accuracy and reliability of the method.This study presents a step-by-step approach to analyzing themechanical characteristics of patient-specific valves obtained fromCT images,holding significant clinical implications and assisting in the selection of treatment strategies and surgical intervention approaches in TAVR procedures.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.