Design and Implementation of a BPEL Profiling System by Aspect-oriented Programming

Business Process Exeention Language （BPEL） is being used in various applications as the standard of workflow. As the requirerments increase to describe application processes in BPEL, BPEL documentt is getting more Complplicated. Therefore it is difficult to describe applications in BPEL with high reliability. In order to write highly reliable BPEL document, it is necessary to check if the document is working correctly in accordance with user＇s intention. But it is difficult to carry out the test since BPEL engine doesn＇t provide a profiling function. In this paper, we suggest a BPEL system with a real-time profiling function. The suggested system consists of an integrated development environment and a BPEL nunning environment. BPEL integrated development environment has a BPEL to Java（B2J） engine, which generates and executes a Java workflow program from a BPEL document, and a converter, which converts Java Weaving XML （JWX） documents to Aspect-oriented programs. The new functions are described in JWX, which is Extensive Markup Lan-guage （XML）-based document. Since aspect-oriented programming technique provides the way to modularize main and supplemental requirements, it guarantees the low degree of coupling between BPEL workflow program and new functions by weaving them. Our approach can be extended to add other functions to provide context-aware services.

A Moored Underwater Energy Conservation System for Profiling Measurement

There is a need to obtain the hydrologic data including ocean current, wave, temperature and so on in the South China Sea. A new profiling instrument which does not suffer from the damage due to nature forces or incidents caused by passing ships, is under development to acquire data from this area. This device is based on a taut single point mid-water mooring system. It incorporates a small, instrumented vertically profiling float attached via an electromechanical cable to a winch integral with the main subsurface flotation. On a pre-set schedule, the instrument float with sensors is winched up to the surface if there is no ship passing by, which is defined by an on-board miniature sonar. And it can be. inunediately winched down to a certain depth if the sonar sensor finds something is coming. Since, because of logistics, the area can only be visited once for a long time and a minimum of 10 times per day profiles are desired, energy demands are severe. To respond to these concerns, the system has been designed to conserve a substantial portion of the potential energy lost during the ascent phase of each profile and subsequently use this energy to pull the instrument down. Compared with the previous single-point layered measuring mode, it is advanced and economical. At last the paper introduces the test in the South China Sea.

A new data acquisition and processing system for profiling sonar

A multi-beam chirp sonar based on IP connections and DSP processing nodes was proposed and designed to provide an expandable system with high-speed processing and mass-storage of real-time signals for multi-beam profiling sonar.The system was designed for seabed petroleum pipeline detection and orientation,and can receive echo signals and process the data in real time,refreshing the display 10 times per second.Every node of the chirp sonar connects with data processing nodes through TCP/IP. Merely by adding nodes,the system’s processing ability can be increased proportionately without changing the software.System debugging and experimental testing proved the system to be practical and stable.This design provides a new method for high speed active sonar.

Bacterial Protein Profiling——Comparison of Three Mass Spectrometry Methodologies

Profiling the protein composition of bacteria is essential for understanding their biology,physiology and interaction with environment.Mass spectrometry has become a pivotal tool for protein analysis,facilitating the examination of expression levels,molecular masses and structural modifications.In this study,we compared the performance of three widely-used mass spectrometry methods,i.e.,matrix-assisted laser desorption/ionization(MALDI)protein fingerprinting,top-down proteomics and bottom-up proteomics,in the profiling of bacterial protein composition.It was revealed that bottom-up proteomics provided the highest protein coverage and exhibited the greatest protein profile overlap between bacterial species.In contrast,MALDI protein fingerprinting demonstrated superior detection reproducibility and effectiveness in distinguishing between bacterial species.Although top-down proteomics identified fewer proteins than bottom-up approach,it complemented MALDI fingerprinting in the discovery of bacterial protein markers,both favoring abundant,stable,and hydrophilic bacterial ribosomal proteins.This study represents the most systematic and comprehensive comparison of mass spectrometry-based protein profiling methodologies to date.It provides valuable guidelines for the selection of appropriate profiling strategies for specific analytical purposes.This will facilitate studies across various fields,including infection diagnosis,antimicrobial resistance detection and pharmaceutical target discovery.

A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.

Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,etc.However, the research on RomanUrdu is not up to the mark.Hence, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

Genomic profiling of colorectal cancer in large-scale Chinese patients:amplification and somatic mutations in ERBB2

Objectives:Human epidermal growth factor receptor 2(HER2)-targeted therapies have demonstrated potential benefits for metastatic colorectal cancer(mCRC)patients with HER2 amplification,but are not satisfactory in cases of HER2 mutant CRCs.Methods:Consequently,further elucidation of amplifications and somatic mutations in erythroblastic oncogene B-2(ERBB2)is imperative.Comprehensive genomic profiling was conducted on 2454 Chinese CRC cases to evaluate genomic alterations in 733 cancer-related genes,tumor mutational burden,microsatellite instability,and programmed death ligand 1(PD-L1)expression.Results:Among 2454 CRC patients,85 cases(3.46%)exhibited ERBB2 amplification,and 55 cases(2.24%)carried ERBB2 mutation.p.R678Q(28%),p.V8421(24%),and p.S310F/Y(12%)were the most prevalent of the 16 detected mutation sites.In comparison to the ERBB2 altered(alt)group,KRAS/BRAF mutations were more prevalent in ERBB2 wild-type(wt)samples(ERBB2wt vs.ERBB2alt,KRAS:50.9%vs.25.6%,p<0.05;BRAF:8.5%vs.2.3%,p<0.05).32.7%(18/55)of CRCs with ERBB2 mutation exhibited microsatellite instability high(MSI-H),while no cases with HER2 amplification displayed MSI-H.Mutant genes varied between ERBB2 copy number variation(CNV)and ERBB2 single nucleotide variant(SNV);TP53 alterations tended to co-occur with ERBB2 amplification(92.3%)as opposed to ERBB2 mutation(58.3%).KRAS and PIK3CA alterations were more prevalent in ERBB2 SNV cases(KRAS/PIK3CA:45.8%/31.2%)compared to ERBB2 amplification cases(KRAS/PIK3CA:14.1%/7.7%).Conclusion:Our study delineates the landscape of HER2 alterations in a large-scale cohort of CRC patients from China.These findings enhance our understanding of the molecular features of Chinese CRC patients and offer valuable implications for further investigation.

Effect of resistance training volume on body adiposity,metabolic risk,and inflammation in postmenopausal and older females:Systematic review and meta-analysis of randomized controlled trials

Purpose:This meta-analytical study aimed to explore the effects of resistance training(RT) volume on body adiposity,metabolic risk,and inflammation in postmenopausal and older females.Methods:A systematic search was performed for randomized controlled trials in PubMed,Scopus,Web of Science,and SciELO.Randomized controlled trials with postmenopausal and older females that compared RT effects on body adiposity,metabolic risk,and inflammation with a control group(CG) were included.Independent reviewers selected the studies,extracted the data,and performed the risk of bias and certainty of the evidence(Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)) evaluations.Total body and abdominal adiposity,blood lipids,glucose,and C-reactive protein were included for meta-analysis.A random-effects model,standardized mean difference(Hedges’ g),and 95% confidence interval(95%CI) were used for meta-analysis.Results:Twenty randomized controlled trials(overall risk of bias:some concerns;GRADE:low to very low) with overweight/obese postmenopausal and older females were included.RT groups were divided into low-volume RT(LVRT,~44 sets/week) and high-volume RT(HVRT,~77 sets/week).Both RT groups presented improved body adiposity,metabolic risk,and inflammation when compared to CG.However,HVRT demonstrated higher effect sizes than LVRT for glucose(HVRT=-1.19;95%CI:-1.63 to-0.74;LVRT=-0.78;95%CI:-1.15 to-0.41) and C-reactive protein(HVRT=-1.00;95%CI:-1.32 to-0.67;LVRT=-0.34;95%CI,-0.63 to-0.04)) when compared to CG.Conclusion:Compared to CG,HVRT protocols elicit greater improvements in metabolic risk and inflammation outcomes than LVRT in overweight/obese postmenopausal and older females.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Assessment of spermatogenesis in camels via seminiferous tubule staging and testosterone profiling

Objective:To investigate the successive morphological stages of spermatogenesis,hormonal regulation,and testosterone profile in dromedary camel reproduction.Methods:Testicular tissue samples were obtained from 12 dromedary bull camels aged 7 to 8 at a local abattoir.The histological assessment involved tissue processing,hematoxylin and eosin(H&E)staining,and examination under a microscope.Stereological analysis,germ cell identification,and assessment of seminiferous tubules and maturation were conducted.Testosterone assay was performed by radioimmunoassay using blood samples collected at regular intervals.Results:The study revealed 12 phases of the dromedary camel's seminiferous epithelium cycle,highlighting distinct morphological characteristics and cellular processes.Acrosomal migration,maturation,cap formation,and the Golgi-mediated synthesis of proacrosomal vesicles were also explained in dimension,as were the steps of acrosome biogenesis.Spermatids and mature sperm cells were present when spermatogenesis phases were examined.An analysis of the dimensions of seminiferous tubules revealed specific measures for diameter,area,and epithelial height about luminal characteristics.Moreover,there were noticeable variations in the serum testosterone concentrations during the study period,indicating temporal dynamics.Conclusions:This study outlines the spermatogenesis process in dromedary camels across 12 stages,emphasizing cellular dynamics and acrosomal biogenesis.It also provides seminiferous tubule measurements and observes seasonal testosterone fluctuations,offering insights into reproductive regulation and potential strategies for camel breeding conservation.

SmartMicro Grid Energy System Management Based on Optimum Running Cost for Rural Communities in Rwanda

The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in remote areas.So far,Solar Home Systems(SHS)have mostly been applied to increase electricity access in rural areas.SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities.The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study.The renewable energy resources available in Remera are the key sources of electricity in that village.The generation capacity is estimated based on the load profile.The microgrid configurations are simulated with HOMER,and the genetic algorithm is used to analyze the optimum cost.By analyzing the impact of operation and maintenance costs,the results show that the absence of subsidies increases the levelized cost of electricity(COE)five times greater than the electricity price from the public utility.The microgrid made up of PV,diesel generator,and batteries proved to be the most viable solution and ensured continuous power supply to customers.By considering the subsidies,COE reaches 0.186$/kWh,a competitive price with electricity from public utilities in Rwanda.

Influence of the Channel Design on the Heat Exchange Characteristics of Pulsating Flows in the Supply System of an Engine

Heat engines based on reciprocating machines remain in demand as energy converters in a variety of industries around the world.The aim of the study was to evaluate the gas-dynamic,consumable and heat exchange characteristics of non-stationary air flows in a supply system with transverse profiling of valve channels based on experimental studies.Valve channels with cross sections in the form of a circle,square and triangle were used to control the consumable and heat exchange characteristics of the flows in the supply system of the reciprocatingengine model.The article presents data on changes in local velocity,volumetric airflow and instantaneous heat transfer coefficient of non-stationary airflow in supply systems with different valve channel designs.A spectral analysis of the pulsations of the local heat transfer coefficient was also performed.The Nusselt number was calculated for the studied supply systems.The figured valve channels lead to an increase in the volumetric airflow through the supply systemupto32%comparedwiththe basic configuration.The useof a square valve channel leads to suppression of heat transfer(drop is about 15%)compared to the basic supply system,and the use of a triangular valve channel causes an intensification of heat transfer(growth is about 17.5%).The obtained data can be useful for refining mathematical models,adjusting machine learning algorithms,and improving design methods for supply systems of reciprocating machines to improve their technical,economic,and environmental characteristics.

ABMRF:An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

This study explores the area of Author Profiling(AP)and its importance in several industries,including forensics,security,marketing,and education.A key component of AP is the extraction of useful information from text,with an emphasis on the writers’ages and genders.To improve the accuracy of AP tasks,the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1(ABM1)and Random Forest(RF).The work uses an extensive technique that involves textmessage dataset pretreatment,model training,and assessment.To evaluate the effectiveness of several machine learning(ML)algorithms in classifying age and gender,including Composite Hypercube on Random Projection(CHIRP),Decision Trees(J48),Na飗e Bayes(NB),K Nearest Neighbor,AdaboostM1,NB-Updatable,RF,andABMRF,they are compared.The findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,respectively.Additional metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling tasks.This study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and education.The results emphasize the effectiveness of ensemble approaches in enhancing author profiling task accuracy,particularly when it comes to age and gender identification.

Study and Application of a New Association Polymer System for Profile Control

A new polymer system, referred to simply as the AP-P4 polymer system, aims at solving the problems of high temperature, high salinity and the poor shearing resistance, all of which are encountered by conventional polymers (such as polyacrylamide) used in profile control, profile performance improvement and EOR operations in the Zhongyuan Oilfield, Sinopec. This system has been developed on the basis of the specific molecular structure and the better properties of high temperature resistance, high salinity resistance and strong shearing resistance of the new type of AP-P4 association polymer. Acidity modifying agents and cross-linking agents (MZ-YL, MZ-BE, MZ-XS), compatible with the new polymer system, are selected. Results of performance tests have shown that the new polymer system has excellent thickening, high temperature, high salinity and shearing resistance and anti-dehydrating properties. In 2003, it underwent its first pilot test in 26 wells in China, with remarkable effects in increasing oil production and decreasing water production. The newly developed polymer system and its application technology described in this paper may play a guiding role in polymer profile control operations in the oil reservoirs of high temperature and high salinity.

基于OPT Cache替换Profiling的Cache提示生成

提出了一个基于最优Cache替换(OPT)Profiling的静态Cache提示生成方法,并通过模拟SPEC2000Int测试程序,对该方法与LRUCache替换策略进行了性能比较。

Peripheral blood RNA biomarkers can predict lesion severity in degenerative cervical myelopathy

Degenerative cervical myelopathy is a common cause of spinal cord injury,with longer symptom duration and higher myelopathy severity indicating a worse prognosis.While numerous studies have investigated serological biomarkers for acute spinal cord injury,few studies have explored such biomarkers for diagnosing degenerative cervical myelopathy.This study involved 30 patients with degenerative cervical myelopathy(51.3±7.3 years old,12 women and 18 men),seven healthy controls(25.7±1.7 years old,one woman and six men),and nine patients with cervical spondylotic radiculopathy(51.9±8.6 years old,three women and six men).Analysis of blood samples from the three groups showed clear differences in transcriptomic characteristics.Enrichment analysis identified 128 differentially expressed genes that were enriched in patients with neurological disabilities.Using least absolute shrinkage and selection operator analysis,we constructed a five-gene model(TBCD,TPM2,PNKD,EIF4G2,and AP5Z1)to diagnose degenerative cervical myelopathy with an accuracy of 93.5%.One-gene models(TCAP and SDHA)identified mild and severe degenerative cervical myelopathy with accuracies of 83.3%and 76.7%,respectively.Signatures of two immune cell types(memory B cells and memory-activated CD4^(+)T cells)predicted levels of lesions in degenerative cervical myelopathy with 80%accuracy.Our results suggest that peripheral blood RNA biomarkers could be used to predict lesion severity in degenerative cervical myelopathy.

Relationship between longitudinal changes in lipid composition and ischemic stroke among hypertensive patients

BACKGROUND Dyslipidemia was strongly linked to stroke,however the relationship between dyslipidemia and its components and ischemic stroke remained unexplained.AIM To investigate the link between longitudinal changes in lipid profiles and dyslipidemia and ischemic stroke in a hypertensive population.METHODS Between 2013 and 2014,6094 hypertension individuals were included in this,and ischemic stroke cases were documented to the end of 2018.Longitudinal changes of lipid were stratified into four groups:(1)Normal was transformed into normal group;(2)Abnormal was transformed into normal group;(3)Normal was transformed into abnormal group;and(4)Abnormal was transformed into abnormal group.To examine the link between longitudinal changes in dyslipidemia along with its components and the risk of ischemic stroke,we utilized multivariate Cox proportional hazards models with hazard ratio(HR)and 95%CI.RESULTS The average age of the participants was 62.32 years±13.00 years,with 329 women making up 54.0%of the sample.Over the course of a mean follow-up of 4.8 years,143 ischemic strokes happened.When normal was transformed into normal group was used as a reference,after full adjustments,the HR for dyslipidemia and ischemic stroke among abnormal was transformed into normal group,normal was transformed into abnormal group and abnormal was transformed into abnormal Wei CC et al.Dyslipidemia changed and ischemic stroke WJCC https://www.wjgnet.com 2 February 6,2025 Volume 13 Issue 4 group were 1.089(95%CI:0.598-1.982;P=0.779),2.369(95%CI:1.424-3.941;P<0.001)and 1.448(95%CI:1.002-2.298;P=0.047)(P for trend was 0.233),respectively.CONCLUSION In individuals with hypertension,longitudinal shifts from normal to abnormal in dyslipidemia-particularly in total and low-density lipoprotein cholesterol-were significantly associated with the risk of ischemic stroke.

指导cache静态划分的程序性能profiling优化技术

对于共享cache的多核处理器,如何管理好各个核对cache的利用,对于充分发挥多核处理器性能是很关键的问题.目前采用的cache替换方法程序间会出现性能干扰,cache静态划分技术则是通过为同时运行的程序分配不同的空间来解决性能干扰问题.为了给程序分配合适大小的cache空间,需要对程序进行性能profiling,即事先多遍运行收集程序在各种cache容量下的性能数据,这种性能profiling方法开销巨大,影响实用.为了解决性能profiling需要多遍运行程序的问题,提出了只需单遍运行的程序性能profiling优化技术.该技术利用在线的phase分析技术识别程序的运行阶段,避免对相同阶段的重复profiling;同时分析程序各phase的性能同cache容量变化的关系趋势,对于性能不敏感的容量变化则不进行profiling,降低开销.在程序运行结束后通过程序各phase在cache各种容量下的性能来估计程序在各容量下的整体性能,以指导cache静态划分.实验表明,该技术的开销仅为7%,而该方法指导的cache划分比未划分时有8%的性能改进,同多遍运行的程序性能profiling指导的cache划分性能相比仅有1%的下降.

Cache Profiling技术

如何减少和隐藏cache失效的延迟,是人们关注的热点。编译器为了得到cache访问命中的情况,往往使用模拟器去跑一遍来得到结果,这样的速度很慢。为了克服以上缺点,提出了在编译器中作cache profiling来获取cache访问的信息。类似于value profiling和strideprofiling,cache profiling对访存指令作插装,可以有效地提高速度,并且只需要编译器的支持即可。Cache profiling获得的信息可以用来改进指令调度、软件预取、生成cache hint和辅助线程等。

基于动态profiling技术的流水粒度调优

结点间流水是解决数据分布和计算分割不一致时的一种重要的并行发掘技术.结点间流水通过计算与通信的重叠获得并行度.精确的流水粒度是获得良好的流水性能的关键.流水分块取决于很多因素,如程序规模、程序的访问模式、结点规模、结点的计算能力和存储体系、通信系统的性能、通信库开销等等.提出了动态profiling方式并实现在流水粒度的推导中,运行时信息收集部分典型分块,结合代价模型推导流水粒度,该模型考虑局部性优化;探索如何减少插桩执行的开销的同时保证代价模型的精度.实验证明,这种方式有更好的适应性,能获得较好的流水并行.