Testing Processes Research Articles

Approbation of the test stand for piston hydraulic cylinders

Test of the end production in the mechanic engineering requires extensive energy consumption which is often lost irrevocably during testing processes and inflict damage on environment. Thus, issues of energy conservation during testing processes become actual. The most productive way of energy conservation during the process of machine testing is recuperation of energy. The objective of the present article is to confirm of the proposed theory about the possibility of recuperating part of the energy while testing piston hydraulic cylinders. Piston hydraulic cylinders are actively used in machines’ hydraulic drive and various stationary production equipment. Operating time to failure of the hydraulic cylinder is usually regulated by a manufacturing plant and commonly includes number of working cycles or general piston’s motion distance. To achieve the stated objective, the structure of the test stand for a trial run of piston hydraulic cylinders. The principal diagram is also developed. On the basis of the theory of volume stiffness, the simulation of test stand’s operation is made. The software program is developed using the software package SimInTech. The preliminary calculations of test stand’s functioning are made. Its operating factors are obtained.

“I shall not poison my child with your human experiment”: Investigating predictors of parents’ hesitancy about vaccinating younger children (

BackgroundSince the approval of SARS-CoV-2 vaccines for younger children (those under the age of 12), uptake has been low. Despite widespread vaccination among older children and adults, these trends may undermine public health efforts to manage future waves of SARS-CoV-2 or spill over into other childhood vaccines. The objectives of this study were to understand parents’ intentions to vaccinate their children (under age 12) against SARS-CoV-2, and to explore reasons for and against SARS-CoV-2 vaccination. MethodsA representative sample of parents of school-aged children (ages 3–11 years) from Canada’s four largest provinces were invited in June 2021 to complete a survey on the impact of COVID-19 on schooling. The survey included specific questions on parents’ intentions to vaccinate their child(ren) against SARS-CoV-2. Multinomial regression models were run to estimate associations between demographic factors, political affiliation and voting, concerns about individual / family health and vaccination intention. ResultsA total of 74.0 % of parents (n = 288) intended to vaccinate their children with the SARS-CoV-2 vaccine, 18.3 % (n = 71) did not intend to vaccinate and 7.7 % (n = 30) were unsure. The strongest predictor of parental hesitancy was whether a parent had themselves been vaccinated. Other factors including past voting behaviour, dissatisfaction with the government’s response to the pandemic, and relatively less concern about contracting SARS-CoV-2 were also correlated with hesitancy. Parents of older children were more likely to indicate plans to vaccinate their child(ren). Analysis of the reasons for hesitancy showed parents are concerned about the safety and side effects of the vaccine, as well as with processes of testing and approval. InterpretationA considerable proportion of Canadian parents of younger school-aged children (ages 3–11) were unsure and/or hesitant about vaccinating their children against SARS-CoV-2. As well, a much larger proportion who are not necessarily hesitant have also not had their children vaccinated. Given the evolving nature of SARS-CoV-2, including the continued emergence of new variants, reaching younger children will be important for population health. Health providers should continue to work with government institutions to ensure clear communication regarding the safety, efficacy, and importance of child vaccines for reaching public health goals.

Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation

Relative permeability is a substantial parameter for estimating multi-phase fluid flow in porous rocks. It is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Relative permeability measurement of rock samples in laboratory can be carried out using steady-state or non-steady-state techniques. Permeability measurement is relatively difficult and time consuming. Because of the difficulty in measurement, empirical models are often used to estimate relative permeability or extrapolate to limited laboratory data. Artificial neural network (ANN) is a method that can be applied to obtain complex correlations of parameters that influence each other. In this study, ANN is used to predict the relative permeability of oil and water. The proposed model evaluates the relative permeability of a phase as a function of rock absolute permeability, porosity, depth, permeability of other phases and water saturation. A total of 159 relative permeability data from Talang Akar Formation were used for the training and testing processes. Based on the comparison between measured and calculated data, the correlation coefficients for relative permeability to water and oil using ANN method are 0.77 and 0.94 respectively. While those using regression analysis are 0.88 and 0.73 respectively.

Application and Practice of Intelligent Algorithms in Computer Software Design and Testing

With the rapid development of information technology, software has become a part of social life. However, for complex software systems, traditional design and testing methods face a series of problems such as low testing coverage and low efficiency. The main purpose of this article is to study the optimization of software design and testing processes using intelligent algorithms, in order to improve the quality and efficiency of software. This article established a real-time optimization and feedback adjustment mechanism during the software design and testing phase, enabling intelligent algorithms to respond promptly to the environment and requirements. Monitoring and control strategies were designed to ensure the algorithm runs stably for a long time. Test method 1 (traditional method) had 100 test cases, a total execution time of 1200 seconds, and an average execution time of 12 seconds per test case; test method 2 (Intelligent Algorithm Optimization) had 100 test cases, a total execution time of 800 seconds, and an average execution time of 8 seconds per test case. The research results of this article play a good promoting role in improving software testing coverage and software structure optimization, and also provide new ideas and methods for the promotion of intelligent algorithms in software engineering.

SOFTWARE TESTING AND ITS ASPECTS

The software testing topic is becoming more and more popular. This article discusses the software testing concept, software testing types, how it works and where it is applied. Software testing is the most important phase of the software development life cycle, so this article is about ensuring the quality of all software applications types by executing certain types of testing methods and streamlined software testing processes. The object of the work is the software testing process. The subject of the research is software testing and its life cycle. The purpose of the work is to review the software testing con-cept, testing types, software testing capabilities, software testing stages, as well as current state presentation of issue. The theoretical method was chosen as the research method. In this paper, an optimized testing process is considered, which considers all testing life cycle stages and also considers the testing types. The article describes the current state of issue. The work objectives have been achieved. The assigned tasks have been completed.

Analysis and Implementation of CNN in Real- time Classification and Translation of Kanji Characters

This research explores the training outcomes of the Convolutional Neural Network (CNN) algorithm applied to Kanji character recognition. It employs a CNN architecture with 10 layers for recognizing digital image of Kanji characters from N5 to N1 levels. The training of the CNN model reveals varying accuracies, influenced by factors such as architecture, training data size, and data quality. The lowest accuracy, occurring at the beginning of training, highlights challenges like poor random weight initialization and suboptimal architecture. Conversely, the highest accuracy demonstrates the optimal predictive ability of the model after multiple training iterations. The iterative training process refines the model's accuracy over time, with initial challenges paving the way for a better understanding of the data for future improvements. In the subsequent analysis and system development phase, two algorithms are compared to assess the effectiveness of the CNN algorithm in Kanji character recognition. Testing is conducted at various complexity levels to systematically evaluate accuracy. This testing involves complexity levels at each Kanji character level to assess system accuracy. The developed model shows potential for real-time classification of Kanji characters, with a focus on error rate and accuracy during training and testing. The built model can essentially be used to evaluate the ability to recognize Kanji characters effectively. The model's performance can be assessed based on the error rate and accuracy achieved during the training and testing processes.

Structure and calculations of the test stand for piston hydraulic cylinder trials

When testing finished products of mechanical engineering, including hydraulic engineering, a large amount of energy has to be expended, which, as a rule, is irretrievably lost, while contaminating the environment. In this regard, the problem of energy saving in testing processes is given great attention. One of the directions of energy saving in testing of hydraulic machines is energy recovery. The purpose of the work is to develop the structure and circuit diagram of the test bench for piston hydraulic cylinders, providing the recovery of part of the energy used for testing. Piston hydraulic cylinders are widely used in hydraulic drives of mobile machines and in various stationary technological equipment. Working time of a hydraulic cylinder up to failure is regulated by the manufacturer, as a rule the number of operation cycles or the total length of the hydraulic cylinder piston passage is indicated. In the process of achieving the goal proposed structure of the test bench for piston hydraulic cylinders and developed its schematic diagram. On the basis of application of the theory of volumetric stiffness the modeling of the stand functioning process is made. A computer program based on the SimInTech software package has been developed, preliminary calculations of the test bench functioning process have been made, and the characteristics of the test bench functioning have been obtained.

Construction Project Delay Risk Assessment Based on 4M1E Framework and Afghanistan Situation

In the realm of construction project management, delays present a significant impediment, particularly within complex socio-political contexts such as Afghanistan. This study endeavors to elucidate the multifaceted nature of construction project delays in Afghanistan, employing the 4M1E (Man, Machine, Material, Method, and Environment) framework to conduct a comprehensive risk assessment. The research methodology entailed the development of a structured questionnaire grounded in an extensive review of pertinent literature, targeting 30 recognized causes of project delays. This instrument was administered to a representative sample of 144 professionals across the Afghan construction industry spectrum, including clients, consultants, and contractors. Analytical rigor was applied through the deployment of frequency, severity, and importance indices to evaluate the collected data. This analysis culminated in the distillation of ten paramount delay risk factors, encapsulating elements such as governmental policy stability modifications in project scope and design alongside delays in material testing and approval processes. A comparative dimension was incorporated to benchmark these findings against global standards, thereby enhancing the robustness of the study’s conclusions. Moreover, the research delineates the congruence and discordance among different respondent cohorts, bolstering the integrity of the identified delay factors through a validation of internal consistency and reliability. The strategic application of the 4M1E framework, contextualized within the Afghan construction landscape, furnishes pivotal insights for stakeholders, equipping them with a nuanced understanding necessary for the proactive mitigation of delay risks. The implications of this study are far-reaching, promising to augment project completion efficiency, budget adherence, and overall project success, with particular resonance for environments paralleling the intricacies of Afghanistan. Doi: 10.28991/CEJ-2024-010-01-06 Full Text: PDF

ALGORITMA PRINCIPAL COMPONENT ANALYSIS UNTUK MENINGKATKAN PERFORMA FUZZY C-MEANS PADA KLASTERISASI DATASET BERDIMENSI TINGGI

High-dimensional data is very difficult to group, because the data growth is exponential in terms of data format and the number of values for each dimension is impossible to calculate. To increase the efficiency and accuracy of processing high-dimensional data, before testing, data cleaning and data reduction processes were carried out using the Principal Component Analysis (PCA) method. There are challenges in comparing the quality of clusters at different membership values resulting in different final clusters, this is due to the presence of noise and outliers in the processed data. Therefore, the PCA method can be used to reduce data sets from high-dimensional data to low-dimensional data and eliminate noise and outliers. The Fuzzy C-Means (FCM) method with different initialization is used to group data into clusters based on similar data, so that related data is placed in the same cluster. Based on this process, a comparison of the results of the method without PCA and with PCA was obtained and the results obtained from the implementation of PCA+FCM with the initialization of a multi-variate Gaussian distribution were higher with an accuracy level of 87.07.

Improving laboratory turnaround times in clinical settings: A systematic review of the impact of lean methodology application.

Lean methodology, originally developed in the manufacturing sector, is a process management philosophy focused on maximizing value by eliminating waste. Its application in laboratory settings, particularly concerning laboratory turnaround times (TAT), involves a systematic approach to identifying inefficiencies and optimizing processes to enhance value for end customers. This systematic review was registered in PROSPERO with identification number (CRD42024552350) and reported based on the 2020 PRISMA checklist. An extensive search strategy was performed using PubMed, Scopus, and Embase databases and gray literatures. Advanced searching was used using Boolean operators (AND & OR). After articles were exported to endnote x8, duplications were removed and articles were selected based on titles, abstracts, and full texts. The illegibility of the articles was independently assessed by the three authors (NC, DMB, and BBT), and the disagreements were settled through scientific consensus. Methodological quality was assessed using JBI critical appraisal checklist. In this review, electronic databases search yielded 1261 articles, of which 7 met the inclusion criteria. The review demonstrated, implementation of lean principle into the routine laboratory testing had an overall impact 76.1% on reducing laboratory TAT. Transportation, manual data processing, inefficient workflow, and the heavy workload were identified as the main wasteful procedures. To eliminate these non-value-added steps, several intervention techniques were implemented, including the use of a barcoding system, process redesign, workflow optimization, hiring additional staff, and relocating the sample collection room closer to the result distribution center. Lean implementation is crucial in the medical laboratory industry for optimizing processes, reducing TAT, and ultimately enhancing customer satisfaction. As a result, all clinical laboratories should adopt and implement lean principles in their routine testing processes. The medical laboratory industry should also proactively look for and apply lean tools, provide ongoing training, and foster awareness among laboratory staffs.

Standardising workforce cost estimates across Australian jurisdictions: genomic testing as a use case.

IntroductionLabour costs are a key driver of healthcare costs and a key component of economic evaluations in healthcare. We undertook the current study to collect information about workforce costs related to clinical genomic testing in Australia, identifying key components of pay scales and contracts, and incorporating these into a matrix to enable modelling of disaggregated costs.MethodsWe undertook a microcosting study of health workforce labour costs in Australia, from a health services perspective. We mapped the genomic testing processes, identifying the relevant workforce. Data was collected on the identified workforce from publicly available pay scales. Estimates were used to model the total cost from a public health services employer perspective, undertaking deterministic and probabilistic sensitivity analyses.ResultsWe identified significant variability in the way in which pay scales and related conditions are both structured and the levels between jurisdictions. The total costs (2023-2024 Australian dollars) ranged from 160,794 (113,848-233,350) for administrative staff to 703,206 (548,011-923,661) for pathology staff (full-time equivalent). Deterministic sensitivity analysis identified that the base salary accounts for the greatest source of uncertainty, from 24.8% (20.0-32.9%) for laboratory technicians to 53.6% (52.8-54.4%) for medical scientists.ConclusionVariations in remuneration levels and conditions between Australian jurisdictions account for considerable variation in the estimated cost of labour and may contribute significantly to the uncertainty of economic assessments of genomic testing and other labour-intensive health technologies. We outline an approach to standardise the collection and estimation of uncertainty for Australian health workforce costs and provide current estimates for labour costs.

Improving drinking water quality through proficiency testing-the impact of testing method and accreditation status on Escherichia coli detection by Canadian environmental testing laboratories.

Water quality testing is crucial for protecting public health, especially considering the number of boil water advisories annually issued across Canada that impact daily life for residents in affected areas. To overcome these challenges, the development of drinking water safety plans and accessibility to regular testing using simple, rapid, and accurate materials are necessary. However, the significance of monitoring the accuracy of environmental microbiology testing laboratories cannot be overlooked. Participation in external quality assessment programs, such as those that include proficiency testing (PT), is a necessary risk management resource that ensures the effectiveness of these testing processes. Proficiency Testing Canada (PTC), in collaboration with the Canadian Microbiological Proficiency Testing (CMPT) program based at the University of British Columbia, have implemented a drinking-water microbiology PT program since 1996. Both PTC and CMPT are ISO/IEC 17043:2010-accredited EQA providers. The drinking water program provided PT challenges to subscribing testing laboratories twice per year. Each challenge consisted of four samples containing unknown concentrations of Escherichia coli (E. coli) and Enterobacter spp. Results from participants were assessed for accuracy based on the method of testing. This cross-sectional study evaluated 150 rural and metropolitan testing sites across Canada between 2016 and 2022. Multivariable logistic regression analysis was conducted to examine the impact of different testing methods and laboratory accreditation status on the proficiency scores. This approach enabled us to assess the association between multiple independent variables and the likelihood of achieving specific proficiency scores, providing insights into how testing methods and accreditation status affect overall performance. After adjusting for rural residence, testing time, and survey year, the membrane filtration method was positively associated with the likelihood of scoring satisfactory results compared to the enzyme-substrate method (OR: 1.75; CI: 1.37-2.24), as well as accreditation status (OR: 1.47; CI: 1.16-1.85). The potential for improvement in environmental laboratory testing performance through the implementation of regulated PT in drinking water safety plans is proposed, along with the need for reliable testing methods applicable to rapid drinking water microbiology testing.

ПРОДУКТОВЫЙ ПОДХОД ПРИ ПРОВЕДЕНИИ A/B-ТЕСТИРОВАНИЯ ЦИФРОВЫХ СЕРВИСОВ

The methodology of A/B testing has been clarified from the perspective of productbased approach to digital product management. The issue of data collection in the modern product management and its formalization within the concepts of unit economics has been raised. The verification of hypotheses through statistical A/B testing has been studied. To accomplish this, the design of multiple experiments, its mathematical apparatus, the regulation of testing processes, as well as methods of interpreting results using publicly available datasets of user behavior, have been examined.

Examining The Effect of Pre-processed Covid-19 Images On Classification Performance Using Deep Learning Method

In recent years, researchers have been using different artificial intelligence models to process x-ray images and make a determination about the patient's condition. Pre-processing is applied to medical images by many researchers. In this way, researchers know that the results they will obtain will be better and that their study results will be more accepted in the literature. As with all other medical images, pre-processing of Covid-19 images is generally done to obtain better classification results. In this study, some pre-processing was done with Covid-19 images. Experimental studies were performed using the ResNet18 deep learning model. According to experimental studies carried out on non pre-processed images, an average accuracy of 0.85206% was obtained in the test processes, while an accuracy rate of 0.93086% was obtained in the test processes obtained from pre-processed images. It was observed that better results were obtained by processing pre-processed images with the same model.

Systematic Review of Artificial Intelligence Application in Higher Education

The role of Higher Education in any society is inevitable when it comes to harmonious personality development of the youth and nation-building. The promising higher education system is research-oriented and is open to newer dimensions of teaching-learning pedagogy. One of the recent advancements in this sector is the emergence of the application of artificial intelligence which has eventually led to the emergence of New possibilities and challenges as well. The Massification of Higher Education and the diversified nature of curriculum has no doubt increased the workload of the teacher and the nature of the job has also undergone drastic changes. With changing times the teaching-learning methods including testing , measuring and evaluative processes involved in it , have experienced lots of changes recently with the interventions of the latest technology in the process. It is surprising to see how the application of artificial intelligence has aptly equipped not only teachers and teachers but also the whole system involved in the higher education system with its latest techniques. Despite all this, the use and application of artificial intelligence in higher education scenarios is still unclear to some extent especially in a country like India with different kinds of limitations when it comes to the use of technology. This paper tries to analyze the advantages and disadvantages of artificial intelligence and its role in the higher education system and finally the role of teacher in this respect.

Revealing the Foundations: The Strategic Influence of Test Design in Automation

In the ever-changing field of software testing and automation, this study brings attention to the frequently underestimated yet crucial role of test design. By highlighting its significant influence on the success of automation initiatives, this investigation aims to reveal the fundamental principles that serve as the basis of effective testing strategies. Going beyond the surface, the study explores the complexities of test design, uncovering its strategic impact on optimizing the creation of test cases, enhancing test coverage, and ultimately improving the efficiency and dependability of automated testing processes. Through the use of real-life examples and practical insights, this study strives to elucidate the methods by which professionals can harness the strategic potential of test design, leading to a shift in the way automation is understood and implemented. As we delve into the various layers of this foundational element, a deeper comprehension emerges, empowering testing experts to strategically employ test design for comprehensive and influential automation endeavors.

An automated detection and classification of brain tumor from MRIs using Water Chaotic Fruitfly Optimization (WChFO) based Deep Recurrent Neural Network (DRNN)

AbstractDue to the complexity of the images and dearth of anatomical models, it is highly difficult to accurately represent the various deformations in each component of the medical images. In recent years, a significant number of children and adults have affected from brain tumors, which is one of the most terrible types of disease affects the people around the world. Moreover, the Magnetic Resonance Imaging (MRI) based brain tumor detection is one of a significant study area in the field of medical imaging. Since, the use of computerized methods aids in the detection and treatment of disease by the medical professionals. The development of an automated method for the accurate detection and classification of tumors from brain MRIs. In this framework, a tanh normalization process is used to smooth out the input brain MRIs with less noise artefacts and improved quality. Then, a group feature extraction model is used to extract the relevant features from the normalized image, which includes both Speeded Up Robust Features (SURF) and Grey Level Co-occurrence Matrix (GLCM) features. The Water Chaotic Fruitfly Optimization (WChFO) method is used to identify the best features for increasing the speed of classifier training and testing processes with less time. Moreover, a Deep Recurrent Neural Network (DRNN) model is used to classify the type of brain tumor for accurate early diagnosis and treatment. The most well-known benchmarking datasets, like BRATS and Kaggle, employed for analysis in order to assess the effectiveness and results of the proposed brain tumor diagnosis system. By using the proposed WChFO-DRNN technique, the accuracy of the tumor detection system is increased to 99.2% with the sensitivity, specificity of 99% and time consumption of 0.2s.

Healthcare Risk Management and Countermeasures of Clinical Medical Laboratory Testing

Clinical laboratory test-data report is not only an important basis for clinical diagnosis and treatment, but also an important medical evidence. However, at present, in clinical practice, there is a general situation that the medical test-data report have no pay necessary attention in medicine evidence. As a result, in cases of medical malpractise disputes, the medical test-data report often becomes the evidence of litigation against the hospital. This study aims to analyze the healthcare risks in clinical medical laboratory testing and propose effective risk management strategies. The article first identifies key risk points in clinical medical laboratory testing, including risks before, during, and after testing, as well as other related risks. It then discusses existing issues in healthcare risk management, such as non-standardized testing processes, inadequate risk warning mechanisms, and insufficient credibility of test results. Based on these analyses, the article puts forward targeted management strategies, including standardizing testing processes, improving warning mechanisms, and enhancing the credibility of test results. The purpose of this study is to enhance the safety and accuracy of clinical medical laboratory testing, reduce healthcare risks, and ensure patient safety.

Employee mental health risk prediction and coping management based on neural network

Abstract In this paper, after dissecting the neural network model, the initial weights and thresholds of the BP neural network are optimized through selection, crossover, mutation and other operations by using the global optimization-seeking ability of the genetic algorithm. The model for predicting employee mental health risks is initially constructed by selecting structural design, structural parameters, and genetic operators. The feasibility of the model in mental health risk prediction was explored based on the indicators of training time, model error and prediction accuracy, and the prediction model was utilized to predict the mental health risk of the employees in Company A and the coping plan for the employees’ mental health risk was established. The results show that the accuracy of the neural network model is 90% and 85%, respectively, during the training and testing processes. The sensitivity and specificity of the training set are 90.00% and 75.06%, respectively, the Yoden index is 0.78, and the Kappa coefficient is 0.69, and the sensitivity and specificity of the test set are 92.00% and 78.05%, which is a good performance, based on which the study is able to predict the risk of mental health of the employees and to guarantee the mental health of the employees in real-time.

A modular artificial intelligence and asset administration shell approach to streamline testing processes in manufacturing services

The increasing demand for personalized products and cost-effectiveness has highlighted the necessity of integrating intelligence into production systems. This integration is crucial for enabling intelligent control that can adapt to variations in features, parts, and conditions, thereby enhancing functionalities while reducing costs. This research emphasizes the incorporation of intelligence in testing processes within production systems. We introduce a novel approach for controlling testing functionality using an asset administration shell enriched with modular artificial intelligence. The proposed architecture is not only effective in controlling the execution behavior through services but also offers the distinct advantage of a modular design. This modularity significantly contributes to the system’s adaptability and scalability, allowing for more efficient and cost-effective solutions as different machine-learning models may be substituted to meet requirements. The effectiveness of this approach is validated through a practical use case of leak testing, demonstrating the benefits of the modular architecture in a real-world application.