Prioritizing Maintenance Spare Parts Based on Supportability Analysis and Neural Network

In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.

C-CORE:Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

Edge devices,due to their limited computational and storage resources,often require the use of compilers for program optimization.Therefore,ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI.One widely used testing method for this purpose is fuzz testing,which detects bugs by inputting random test cases into the target program.However,this process consumes significant time and resources.To improve the efficiency of compiler fuzz testing,it is common practice to utilize test case prioritization techniques.Some researchers use machine learning to predict the code coverage of test cases,aiming to maximize the test capability for the target compiler by increasing the overall predicted coverage of the test cases.Nevertheless,these methods can only forecast the code coverage of the compiler at a specific optimization level,potentially missing many optimization-related bugs.In this paper,we introduce C-CORE(short for Clustering by Code Representation),the first framework to prioritize test cases according to their code representations,which are derived directly from the source codes.This approach avoids being limited to specific compiler states and extends to a broader range of compiler bugs.Specifically,we first train a scaled pre-trained programming language model to capture as many common features as possible from the test cases generated by a fuzzer.Using this pre-trained model,we then train two downstream models:one for predicting the likelihood of triggering a bug and another for identifying code representations associated with bugs.Subsequently,we cluster the test cases according to their code representations and select the highest-scoring test case from each cluster as the high-quality test case.This reduction in redundant testing cases leads to time savings.Comprehensive evaluation results reveal that code representations are better at distinguishing test capabilities,and C-CORE significantly enhances testing efficiency.Across four datasets,C-CORE increases the average of the percentage of faults detected(APFD)value by 0.16 to 0.31 and reduces test time by over 50% in 46% of cases.When compared to the best results from approaches using predicted code coverage,C-CORE improves the APFD value by 1.1% to 12.3% and achieves an overall time-saving of 159.1%.

Prioritizing Innovation

China’s employment landscape looks sparse right now as many private enterprises that absorb much of the country’s workforce are succumbing to economic stagnation. While the Western world is caught in a tailspin, numerous private enterprises, and exporters in particular, have no place to hide. Their orders are declining to a trickle, and the supply chains of many also have broken down because of a lack of cash. In such a time of gloom, how can they weather the storm through innovation and industrial upgrading? Beijing Review reporter Hu Yue interviewed several economists and entrepreneurs about this issue at the 2008 Dialogue Between Chinese Private Enterprises and Global Fortune 500 held last December in Wenzhou, Zhejiang Province.

Prioritizing Test Cases for Memory Leaks in Android Applications

Mobile applications usually can only access limited amount of memory. Improper use of the memory can cause memory leaks, which may lead to performance slowdowns or even cause applications to be unexpectedly killed. Although a large body of research has been devoted into the memory leak diagnosing techniques after leaks have been discovered, it is still challenging to find out the memory leak phenomena at first. Testing is the most widely used technique for failure discovery. However, traditional testing techniques are not directed for the discovery of memory leaks. They may spend lots of time on testing unlikely leaking executions and therefore can be inefficient. To address the problem, we propose a novel approach to prioritize test cases according to their likelihood to cause memory leaks in a given test suite. It firstly builds a prediction model to determine whether each test can potentially lead to memory leaks based on machine learning on selected code features. Then, for each input test case, we partly run it to get its code features and predict its likelihood to cause leaks. The most suspicious test cases will be suggested to run at first in order to reveal memory leak faults as soon as possible. Experimental evaluation on several Android applications shows that our approach is effective.

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using Genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.

Selecting proper sites for underground dam construction using Multi-Attribute Utility Theory in arid and semi-arid regions

Although the construction of underground dams is one of the best methods to conserve water resources in arid and semi-arid regions,applying efficient methods for the selection of suitable sites for subsurface dam construction remains a challenge.Due to the costly and time-consuming methods of site selection for underground dam construction,this study aimed to present a new method using geographic information systems techniques and decision-making processes.The exclusionary criteria including fault,slope,hypsometry,land use,soil,stream,geology,and chemical properties of groundwater were selected for site selection of dam construction and inappropriate regions were omitted by integration and scoring layers in ArcGIS based on the Boolean logic.Finally,appropriate sites were prioritized using the Multi-Attribute Utility Theory.According to the results of the utility coefficient,seven sites were selected as the region for underground dam construction based on all criteria and experts’opinions.The site of Nazarabad dam was the best location for underground dam construction with a utility coefficient of 0.7137 followed by sites of Akhavan with a utility coefficient of 0.4633 and Mirshamsi with a utility coefficient of 0.4083.This study proposed a new approach for the construction of the subsurface dam at the proper site and help managers and decision-makers achieve sustainable water resources with limited facilities and capital and avoid wasting national capital.

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements.

Fermatean Hesitant Fuzzy Prioritized Heronian Mean Operator and Its Application in Multi-Attribute Decision Making

In real life,incomplete information,inaccurate data,and the preferences of decision-makers during qualitative judgment would impact the process of decision-making.As a technical instrument that can successfully handle uncertain information,Fermatean fuzzy sets have recently been used to solve the multi-attribute decision-making(MADM)problems.This paper proposes a Fermatean hesitant fuzzy information aggregation method to address the problem of fusion where the membership,non-membership,and priority are considered simultaneously.Combining the Fermatean hesitant fuzzy sets with Heronian Mean operators,this paper proposes the Fermatean hesitant fuzzy Heronian mean(FHFHM)operator and the Fermatean hesitant fuzzyweighted Heronian mean(FHFWHM)operator.Then,considering the priority relationship between attributes is often easier to obtain than the weight of attributes,this paper defines a new Fermatean hesitant fuzzy prioritized Heronian mean operator(FHFPHM),and discusses its elegant properties such as idempotency,boundedness and monotonicity in detail.Later,for problems with unknown weights and the Fermatean hesitant fuzzy information,aMADM approach based on prioritized attributes is proposed,which can effectively depict the correlation between attributes and avoid the influence of subjective factors on the results.Finally,a numerical example of multi-sensor electronic surveillance is applied to verify the feasibility and validity of the method proposed in this paper.

Test Case Prioritization in Unit and Integration Testing:A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.

Bug Prioritization Using Average One Dependence Estimator

Automation software need to be continuously updated by addressing software bugs contained in their repositories.However,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and importance.Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical bugs.Therefore,bug report prioritization is vital.This study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of attributes.The more the number of attributes,the more the severity.The proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation coefficient.Results of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)models.Eclipse and Mozilla datasetswere used as the sources of bug reports.The proposed model improved the bug repository management and out-performed the SVM and NB models.Additionally,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost.

Simulating long-term effect of Hyrcanian forest loss on phosphorus loading at the sub-watershed level

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus（P） nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler（LCM） combining with the Soil Water Assessment Tool（SWAT） model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm^2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm^2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.

A Survey about Algorithms Utilized by Focused Web Crawler

Abstract—Focused crawlers （also known as subjectoriented crawlers）, as the core part of vertical search engine, collect topic-specific web pages as many as they can to form a subject-oriented corpus for the latter data analyzing or user querying. This paper demonstrates that the popular algorithms utilized at the process of focused web crawling, basically refer to webpage analyzing algorithms and crawling strategies （prioritize the uniform resource locator （URLs） in the queue）. Advantages and disadvantages of three crawling strategies are shown in the first experiment, which indicates that the best-first search with an appropriate heuristics is a smart choice for topic-oriented crawlingwhile the depth-first search is helpless in focused crawling. Besides, another experiment on comparison of improved ones （with a webpage analyzing algorithm added） is carried out to verify that crawling strategies alone are not quite efficient for focused crawling and in most cases their mutual efforts are taken into consideration. In light of the experiment results and recent researches, some points on the research tendency of focused crawler algorithms are suggested.

Imaging diagnosis of bronchogenic carcinoma(the forgotten disease)during times of COVID-19 pandemic:Current and future perspectives

Patients with bronchogenic carcinoma comprise a high-risk group for coronavirus disease 2019(COVID-19),pneumonia and related complications.Symptoms of COVID-19 related pulmonary syndrome may be similar to deteriorating symptoms encountered during bronchogenic carcinoma progression.These resemblances add further complexity for imaging assessment of bronchogenic carcinoma.Similarities between clinical and imaging findings can pose a major challenge to clinicians in distinguishing COVID-19 super-infection from evolving bronchogenic carcinoma,as the above-mentioned entities require very different therapeutic approaches.However,the goal of bronchogenic carcinoma management during the pandemic is to minimize the risk of exposing patients to COVID-19,whilst still managing all life-threatening events related to bronchogenic carcinoma.The current pandemic has forced all healthcare stakeholders to prioritize per value resources and reorganize therapeutic strategies for timely management of patients with COVID-19 related pulmonary syndrome.Processing of radiographic and computed tomography images by means of artificial intelligence techniques can facilitate triage of patients.Modified and newer therapeutic strategies for patients with bronchogenic carcinoma have been adopted by oncologists around the world for providing uncompromised care within the accepted standards and new guidelines.

Do the Right Patients use the Ambulance Service in South-Eastern Finland?-A Prospective Descriptive Study of Ambulance Dispatching in Relation to the Ambulance Staff’s Assessment of Patients’Needs in a Subset in the South East of Finland

Background: Several Emergency Medical Systems use a criteria-based prioritization system for ambulance response. The emergency medical priority dispatching of ambulances was introduced in the 1980s. In a system of this kind, the operators at the medical emergency dispatch centers have to assess the patients’ symptoms and the need for ambulance response. The prioritization of the ambulance response is based on the seriousness of the patient’s symptoms, his/her current condition and, in the case of trauma, the trauma mechanism. The priority system is supposed to optimize the use of the ambulance service and to match and meet the patients’ needs with an adequate response from the ambulances. The aim of this study was to describe the dispatching and utilization of the ambulance service in a part of Finland. Results: There was a substantial divergence between the initial priority assigned and the patients’ medical status at the scene. The ambulance staff confirmed the need for ambulance transport for 65% of all the patients who were assigned an ambulance by the dispatch center. Conclusions: Using a criteria-based dispatch protocol, the dispatch operator works with a wider safety margin in the priority assessments for ambulance response than was actually confirmed by the ambulance personnel at the scene. In this sample, there may be some overuse of the ambulance service. According to the assessments made by the ambulance staff, 35% of the patients did not require ambulance transport. The emergency system has to accept and work with safety margins. At the same time, there must be a balance between a safety margin and a waste of limited resources.

Selection of patients with hepatocellular carcinoma for livertransplantation:Past and future

The aim of liver transplantation(LT) for hepatocellular carcinoma(HCC) is to ensure a rate of disease-free survival similar to that of patients transplanted due to benign disease. Therefore, we are forced to adopt strict criteria when selecting candidates for LT and prioritizing patients on the waiting list(WL), to have clarified indications for bridging therapy for groups at risk for progression or recurrence, and to establish certain limits for downstaging therapies. Although the Milan criteria(MC) remain the standard and most employed criteria for indication of HCC patients for LT by far, in the coming years, criteria will be consolidated that take into account not only data regarding the size/volume and number of tumors but also their biology. This criteria will mainly include the alpha fetoprotein(AFP) values and, in view of their wide variability, any of the published logarithmic models for the selection of candidates for LT. Bridging therapy is necessary for HCC patients on the WL who meet the MC and have the possibility of experiencing a delay for LT greater than 6 mo or any of the known risk factors for recurrence. It is difficult to define single AFP values that would indicate bridging therapy(200, 300 or 400 ng/m L); therefore, it is preferable to rely on the criteria of a French AFP model score > 2. Other single indications for bridging therapy include a tumor diameter greater than 3 cm, more than one tumor, and having an AFP slope greater than 15 ng/m L per month or > 50 ng/m L for three months during strict monitoring while on the WL. When considering the inclusion of patients on the WL who do not meet the MC, it is mandatory to determine their eligibility for downstaging therapy prior to inclusion. The upper limit for this therapy could be one lesion up to 8 cm, 2-3 lesions with a total tumor diameter up to 8 cm, or a total tumor volume of 115 cm^3. Lastly, liver allocation and the prioritization of patients with HCC onthe WL should take into account the recently described HCC model for end-stage liver disease, which considers hepatic function, HCC size and the number and the log of AFP values. This formula has been calibrated with the survival data of non-HCC patients and produces a dynamic and more accurate assessment model.

Emerging Organic Contaminants in Chinese Surface Water:Identification of Priority Pollutants

The occurrence and impacts of emerging organic contaminants(EOCs)in the aquatic environment have gained widespread attention over the past two decades.Due to large number of potential contaminants,monitoring campaigns,treatment plants,and proposed regulations should preferentially focus on specific pollutants with the highest potential for ecological and human health effects.In the present study,a multi-criteria screening approach based on hazard and exposure potentials was developed for prioritization of 405 unregulated EOCs already present in Chinese surface water.Hazard potential,exposure potential,and risk quotients for ecological and human health effects were quantitatively analyzed and used to screen contaminants.The hazard potential was defined by contaminant persistence,bioaccumulation,ecotoxicity,and human health effects;similarly,the exposure potential was a function of contaminant concentration and detection frequency.In total,123 compounds passed the preselection process,which involved a priority index equal to the normalized hazard potential multiplied by the normalized exposure potential.Based on the prioritization scheme,11 compounds were identified as top-priority,and 37 chemicals were defined as high-priority.The results obtained by the priority index were compared with four other prioritization schemes based on exposure potential,hazard potential,or risk quotients for ecological effects or human health.The priority index effectively captured and integrated the results from the more simplistic prioritization schemes.Based on identified data gaps,four uncertainty categories were classified to recommend:①regular monitoring,derivation of environmental quality standards,and development of control strategies;②increased monitoring;③fortified hazard assessment;and④increased efforts to collect occurrence and toxicity data.Overall,20 pollutants were recommended as priority EOCs.The prioritized list of contaminants provides the necessary information for authoritative regulations to monitor,control,evaluate,and manage the risks of environmentally-relevant EOCs in Chinese surface water.

Prioritization of Semi-Arid Agricultural Watershed Using Morphometric and Principal Component Analysis, Remote Sensing, and GIS Techniques, the Zerqa River Watershed, Northern Jordan

Remote sensing and GIS techniques were employed for prioritization of the Zerqa River watershed. Forty-three 4th order sub-watersheds were prioritized based on morphometric and Principal Component Analysis (PCA), in order to examine the effectiveness of morphometric parameters in watershed prioritization. A comparison has been carried out between the results achieved through applying the two methods of analysis (morphometric and PCA). Afterwards, suitable measures are proposed for soil and water conservation. Topo sheets and ASTER DEM have been employed to demarcate the 43 sub-watersheds, to extract the drainage networks, and to compile the required thematic maps such as slope categories and elevation. LANDSAT 8 image (April-2015) is employed to generate land use/cover maps using ENVI (v 5.1) software. The soil map of the watershed has been digitized using Arc GIS software. Prioritization of the 43 sub-watersheds was performed using ten linear and shape parameters, and three parameters which are highly correlated with components 1 and 2. Subsequently, different sub-watersheds were prioritized by ascribing ranks based on the calculated compound parameters (Cp) using the two approaches. Comparison of the results revealed that prioritization of watersheds based on morphometric analysis is more consistent and serves for better decision making in conservation planning as compared with the PCA approach. The recommended soil conservation measures are prescribed in accordance with the specified priority, in order to avoid undesirable effects on land and environment. Sub-watersheds classified under high priority class are subjected to high erosion risk, thus, creating an urgent need for applying soil and water conservation measures. It is expected that decision makers will pay sufficient attention to the present results/information, activate programs encouraging soil conservation, integrated watershed management, and will continue working on the afforestation of the government-owned sloping lands. Such a viable approach can be applied at different parts of the rainfed highland areas to minimize soil erosion loss, and to increase infiltration and soil moisture in the soil profile, thus, reducing the impact of recurrent droughts and the possibility of flooding hazards.

Attitude Analysis in Process Conflict for C919 Aircraft Manufacturing

Based on the option prioritization in graph model for conflict resolution of two decision makers(DMs),new logical and matrix representations of four stability concepts for DMs′attitude are proposed.The logical representation of attitude is defined,and converted to the matrix form in order to develop a decision support system(DSS)efficiently.Compared with existing definitions of DMs′attitude based on states,the proposed definitions of attitude based on options are convenient and more effective to generate preferences since that of states can be significantly larger than that of options in a large conflict.In addition,it is easier to obtain the information of the prioritization of option statements than to obtain preference of states for users.The proposed representations are applied to the process conflict during aircraft manufacturing to demonstrate the efficiency of the new approach.