Multifactor Algorithm for Test Case Selection and Ordering

Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls short. The current research is motivated by this concept and proposes a multifactor algorithm incorporated with genetic operators and powerful features. A factor-based prioritizer is introduced for proper handling of tied test cases that emerged while implementing re-ordering. Besides this, a Cost-based Fine Tuner (CFT) is embedded in the study to reveal the stable test cases for processing. The effectiveness of the outcome procured through the proposed minimization approach is anatomized and compared with a specific heuristic method (rule-based) and standard genetic methodology. Intra-validation for the result achieved from the reduction procedure is performed graphically. This study contrasts randomly generated sequences with procured re-ordered test sequence for over '10' benchmark codes for the proposed prioritization scheme. Experimental analysis divulged that the proposed system significantly managed to achieve a reduction of 35-40% in testing effort by identifying and executing stable and coverage efficacious test cases at an earlier phase

Effect of Temperature and Strain Rate Variation on Tensile Properties of a Defective Nanocrystalline Copper-Tantalum Alloy

358-366Nanocrystalline alloys of immiscible in nature are emerging topics of interest for researchers due to better mechanical stability at high temperatures. Nanocrystalline Copper-Tantalum alloy is of particular interest for research exploration due to its high strength, limited solubility and high-temperature stability. In the present work, the mechanical properties of nanocrystalline 90/10 copper-tantalum (9Cu-Ta) alloy have been investigated using the molecular dynamics approach. Embedded atom method (EAM) of potential has been used to analyze the mechanical properties at high temperatures due to the high stability of EAM in molecular dynamic simulation. At high-temperature defects plays a very important role therefore a specific 9Cu-Ta nanostructure having 3% vacancies has been selected to explore its performance under a particular type of point defect. This study has been conducted under uniaxial tensile loading. The tensile properties of this defective nanocrystalline alloy have been compared at specific temperatures i.e. 300 K, 600 K, 800 K, 1000 K and 1200 K. The study revealed that the variation in temperature from 300 K to 1200 K results in the shifting of the stress-strain graph to lower stress values. It has also been noticed that the variation in ultimate tensile strength is the least in comparison to yield strength and elastic constant for the same variation in temperature. These results indicate the importance of avoiding thermal agitations during the synthesis and surface modification of nanocrystalline copper-tantalum alloy

Dry Beneficiation of Coking Coal Fines using an Advanced Air Cyclone

An attempt is made on coking coal fines dry beneficiation of below 2 mm size, with VSK separator consisting of advanced air cyclone. The results of these investigations reveal that simple size with 250 micron can give a product with 18% ash at 17.4% yield, whereas by using VSK separator the product obtained contains 17.8% ash with 48.1% yield from a feed sample containing 23.3% ash. Since the preliminary investigations has shown encouraging results on reduction of ash, large scale continuous tests can provide better yield for various samples

Comminution Characters of Fault Zone Rocks and Secure Outcomes in the Blockchain Record-Keeping System for Industrial Applications

This paper is an attempt to find the energy required for the comminution of fault zone rocks and also to determine the energy required to grind ore from infinite size to the desired particle size in non-traditional approach, for various value additions. The results in the present investigations also confirm about the brittleness test and friability tests, whose values depend on the drop weight and its height for different types of fault zone rock. Also the results of its brittleness tests determine the grindability of fault zone rocks. All the outcome results are then secured with the help of decentralized and immutable record-keeping system using Blockchain technology. The Blockchain network in the present investigations not only allows any users to enhance the performance but also it will secure the experimental outcomes in immutable distributed ledgers through smart contracts to increase transparency between users in a trusted manner

Trusted and Transparent Blockchain-enabled E-waste Optimization to Recover Precious Metals with Microwave Heating

Blockchain technology facilitates trust and transparency in the decision-making process and enables the transaction's verifiability by reading immutable distributed ledgers. It has been innovatively applied this technology in the E-waste optimization for the recovery of precious metals using microwave heat treatment. This present paper presents the maximum recovery of precious and base metals from E-waste with a numerical technique called surface response methodology, and was compared with the actual experimental results. The main goal of this paper is to recover the precious metals like copper and gold with its adjacent metals from unwanted and discarded printed circuit boards, integrated circuits, and standards connectors, with the input variables of microwave power, maximum temperature, and aqua leaching ratio. The obtained empirical information of recovered metals was recorded in immutable distributed ledgers so that every member of the blockchain network can be read and verified through the stored records. These records were also utilized to minimize the error and maximize the precious metal outcomes. The result with blockchain network also shows that identical resemblance between the experimental and statistical predicted data obtained with surface methodology. Further, Smart Contracts has been created and deployed to store and retrieve empirical records in the Hyperledger Fabric Blockchain Platform and then measured the performance using Hyperledger Caliper Benchmar

Trusted and Transparent Blockchain-enabled E-waste Optimization to Recover Precious Metals with Microwave Heating

623-629Blockchain technology facilitates trust and transparency in the decision-making process and enables the transaction's verifiability by reading immutable distributed ledgers. It has been innovatively applied this technology in the E-waste optimization for the recovery of precious metals using microwave heat treatment. This present paper presents the maximum recovery of precious and base metals from E-waste with a numerical technique called surface response methodology, and was compared with the actual experimental results. The main goal of this paper is to recover the precious metals like copper and gold with its adjacent metals from unwanted and discarded printed circuit boards, integrated circuits, and standards connectors, with the input variables of microwave power, maximum temperature, and aqua leaching ratio. The obtained empirical information of recovered metals was recorded in immutable distributed ledgers so that every member of the blockchain network can be read and verified through the stored records. These records were also utilized to minimize the error and maximize the precious metal outcomes. The result with blockchain network also shows that identical resemblance between the experimental and statistical predicted data obtained with surface methodology. Further, Smart Contracts has been created and deployed to store and retrieve empirical records in the Hyperledger Fabric Blockchain Platform and then measured the performance using Hyperledger Caliper Benchmark

Population Health Metrics Research Consortium gold standard verbal autopsy validation study: design, implementation, and development of analysis datasets

Background: Verbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment.Methods: Data collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths.Results: Over 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions.Conclusions: This unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems