Testing Processes Research Articles

Modernization of electromechanical transmission for dump trucks based on long-term operation data

In open pit mining, overburden and mineral haulage is carried out mostly by dump trucks. The number of high-capacity machines persistently grows. Such dump trucks mainly use electromechanical transmission systems. As a rule, dump trucks operate all day and all year round, with idle time for scheduled maintenance. The climatic service conditions assume variations in temperature, humidity and atmospheric pressure in wide ranges during a year, moreover, these physical quantities can fluctuate violently within a short time, for instance, within a day. The road conditions – road grade, hardness, roughness, length, altitude difference – differ greatly per open pit mines. Furthermore, air dustiness, including current-conducting coal- or metal-bearing dust should be taken into account. On the whole, these numerous and diverse factors are impossible to embrace in full at the stages of machine engineering and testing. Thus, during operation, it is necessary to accumulate and analyze the hand-on experience, and to improve all components of electromechanical transmission. This article gives data on four-year operation of BelAZ dump trucks with capacity of 90 and 240 t equipped with electromechanical transmission in open pit coal and ore mines. The traction electrical equipment faults are generalized, their causes are revealed, design modifications are described, as well as the processes of manufacturing and testing and the algorithm of diagnosis are reported. The low count of faults during operation and the high availability factor of the dump trucks equipped with electromechanical transmissions manufactured by RUSELPROM is reflective of the proficiency and competency of the machine engineering, design and testing. The modifications introduced into the electromechanical transmissions are mostly aimed at cost reduction and performance buildup, i.e. at competitive recovery of the product. The obtained results are used in engineering of traction electric equipment set for dump trucks of higher capacity (136, 180, 360 t).

The Development of a Web-Based Application Security Testing Framework in Addis Ababa, Ethiopia

The web-based application is the most significant platform to promote business. Nowadays the number of customers of web-based applications increases rapidly. This study aims to use both qualitative and quantitative research methodology to study the challenge the software companies in Addis Ababa, Ethiopia are facing, in the process of software security testing while developing a web-based application. Most software company and organization in Ethiopia test the web-based application at the end of the SDLC which make it less secure. Data for this study was collected using questionnaires and an interview. This research was applied in eight software companies and in two well-known organizations that develop their software. The contribution of this study is to mitigate the challenges the developer has when they perform security testing in a WBA. This study also contributes to having visible data about security testing performance and processes in Addis Ababa, Ethiopia. Keywords: Software securıty testıng, Web-based application, Case of addıs Ababa.

Numerical analysis of Cattaneo–Christov heat flux model over magnetic couple stress Casson nanofluid flow by Lavenberg–Marquard backpropagated neural networks

The purpose of study is to present a new nanofluidic model and its solution that describes steady couple stress magnetic Casson nanofluid flow in the presence of Cattaneo-Christov heat flux (CMCN-CCHF) system past a shrinking plat via aesthetics stochastic computing procedure based on supervised learning Lavenberg-Marquard technique as a backpropagated neural networks (LMT-BNNs). Physical quantities involve in the fluidic system i.e., magnetic parameter, Casson parameter, couple stress parameter, thermal relaxation coefficient and Prandtl number are assessed by suitable variations, which satisfy the dynamics of presented fluid model effectively. These are exploited for the construction of dataset for LMT-BNNs through a well-known deterministic homotopy analysis method. Testing, training and validation processes of LMT-BNNs are incorporated for the analysis of the CMCN-CCHF model. Accuracy of the proposed model through the statistical approaches ensures the reliability of newly developed LMT-BNNs computational intelligence solver. The reliability, stability and preciseness of the proposed LMT-BNNs for the solution of CMCN-CCHF via variety of statistical measures such as mean square error (MSE), error histogram, performance procedures, and regression/correlation curve fitting graphs are shown. Casson fluid parameter causes to enhance the skin friction profile but the presence of magnetic parameter retards the skin friction and local Nusselt number.

Single Molecule Level and Label-Free Determination of Multibiomarkers with an Organic Field-Effect Transistor Platform in Early Cancer Diagnosis.

The single molecule level determination with a transistor (SiMoT) platform has attracted considerable attention in the recognition of various ultralow abundance biomolecules, while complicated labeling and testing processes limit its further applications. Recently, organic field-effect transistor (OFET)-based biosensors are good candidates for constructing an advanced label-free SiMoT platform due to their facile fabrication process, rapid response time, and low sample volume with a wide range of detection. However, the sensitivity of most OFET-based biosensors is in the order of nM and pM, which cannot meet the detection requirements of ultralow abundance protein. Herein, a label-free SiMoT platform is demonstrated by integrating pillar[n]arene as a signal amplifier, and the detection limit can reach 4.75 aM. Besides, by simultaneous determination of α-fetoprotein, carcinoembryonic antigen, and prostate antigen, the proposed multiplexed OFET-based SiMoT platform provides a key step in reliable early cancer diagnosis.

Application of Levenberg-Marquardt technique for electrical conducting fluid subjected to variable viscosity.

In the present study, design of intelligent numerical computing through backpropagated neural networks (BNNs) is presented for numerical treatment of the fluid mechanics problems governing the dynamics of magnetohydrodynamic fluidic model (MHD-NFM) past a stretching surface embedded in porous medium along with imposed heat source/sink and variable viscosity. The original system model MHD-NFM in terms of PDEs is converted to nonlinear ODEs by introducing the similarity transformations. A reference dataset for BNNs approach is generated with Adams numerical solver for different scenarios of MHD-NFM by variation of parameter of viscosity, parameter of heat source and sink, parameter of permeability, magnetic field parameter, and Prandtl number. To calculate the approximate solution for MHD-NFM for different scenarios, the training, testing, and validation processes are conducted in parallel to adapt neural networks by reducing the mean square error (MSE) function through Levenberg–Marquardt backpropagation. The comparative studies and performance analyses through outcomes of MSE, error histograms, correlation and regression demonstrate the effectiveness of proposed BNNs methodology.

Multi-view 3D object retrieval leveraging the aggregation of view and instance attentive features

In multi-view 3D object retrieval tasks, it is pivotal to aggregate visual features extracted from multiple view images to generate a discriminative representation for a 3D object. The existing multi-view convolutional neural network employs view pooling for feature aggregation, which ignores the local view-relevant discriminative information within each view image and the global correlative information across all view images. To leverage both types of information, we propose two self-attention modules, namely, View Attention Module and Instance Attention Module, to learn view and instance attentive features, respectively. The final representation of a 3D object is the aggregation of three features: original, view-attentive, and instance-attentive. Furthermore, we propose employing the ArcFace loss together with the cosine-distance-based triplet-center loss as the metric learning guidance to train our model. As the cosine distance is used to rank the retrieval results, our angular metric learning losses achieve a consistent objective between the training and testing processes, thereby facilitating discriminative feature learning. Extensive experiments and ablation studies are conducted on four publicly available datasets on 3D object retrieval to show the superiority of the proposed method over multiple state-of-the-art methods.

Modeling stage\u2013discharge\u2013sediment using support vector machine and artificial neural network coupled with wavelet transform

Many real water issues involve rivers’ sediment load or the load that rivers can bring without degrading the fluvial ecosystem. Therefore, the assessment of sediments carried by a river is also crucial in the planning and designing of various water resource projects. In the current study, five different data-driven techniques, namely artificial neural network (ANN), wavelet-based artificial neural network (WANN), support vector machine (SVM), wavelet-based support vector machine (WSVM), and multiple-linear regression (MLR) techniques, were employed for time-series modeling of daily suspended sediment concentration (SSC). Hydrological datasets containing the daily stage (h), discharge (Q), and SSC for 10 years (2004–2013) from June to October at Adityapur and Ghatshila station of Subernrekha river basin, Jharkhand, India, were considered for analysis. The Gamma test was used to determine the input variables in the first step. Various combinations were made by lagging the maximum three-day time step for predicting current-day SSC. The outcomes of ANN, SVM, WAAN, WSVM, and MLR models were evaluated with the actual values of SSC based on statistical metrics. Pearson correlation coefficient (PCC), root-mean-square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Wilmot index (WI) as well as visual inspection of time variation, scatter plots, and Taylor diagrams. Our results stated that the WSVM model discovered the best trustworthy models among all existing models. PCC, RMSE, NSE, and WI values were 0.844 and 0.781, 0.096 g/l and 0.057 g/l, 0.711 and 0.591, 0.907 and 0.878, respectively, throughout the training and testing processes at the Adityapur site. Also, at the Ghatshila location, it was the most accurate model. During the training and testing stages, PCC, RMSE, NSE, and WI values were 0.928 and 0.751, 0.117 g/l and 0.095 g/l, 0.861 and 0.541, 0.962 and 0.859, respectively. Our findings showed that the WANN model was the second-best model during the testing phase for both sites. Hence, the WSVM technique can model SSC at this location and other similar (i.e., geomorphology and flow regime type) rivers.

Exploring “quality” in cord blood transfusion: uncertainties, bionetworks, and collaborations

Umbilical cord blood unit (CBU) “quantity” continues to grow internationally, while cord blood transplantation (CBT) “quality” remains poorly defined and subject to uncertainty. CBT “quality” is affected by both the product (i.e. CBUs) and CBT processes, with “best practice” varying across countries. To improve overall CBT “quality”, we need to better understand the uncertainty associated with CBUs and CBT processes and how staff manage it. In this qualitative study, we conducted in-depth semi-structured interviews with individuals working in CBT in UK and Japan. We found that understanding of CBT quality by the cord blood community is underpinned by the quality of the CBU, the expertise and collaboration of scientific and clinical stakeholders, trust in collection and testing processes and international accreditation. Importantly, we found that local and individual experience is used to manage uncertainty within CBT, and we propose that selection guidelines should acknowledge the extent of uncertainty in decision-making.

Optimized Design and Implementation of Testing Laboratory Based on Blockchain Technology

Abstract Traditional testing laboratories suffer from inefficient management of testing equipment, high maintenance efficiency costs, non-transparent testing processes and easy tampering of testing data. This paper makes use of blockchain technology somewhat to optimise the design of data traceability for testing laboratories. Through the optimised design of this paper, an intelligent testing process is achieved for the traditional testing laboratory. At the same time, the level of secure storage of data is improved, making the testing process and test results safer and more reliable. This paper uses blockchain technology to optimise the testing laboratory and achieves significant results. The novel way of using blockchain technology to do data traceability explored in this paper has reference significance and application value for the optimisation and upgrading of traditional laboratories.

Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic.

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.

The Experience of Tests during the COVID-19 Pandemic-Induced Emergency Remote Teaching

The dire circumstances presented by the COVID-19 pandemic have had a severely debilitating global impact on education, and led to an urgent transition from the onsite environment (OSE) to the online environment (OLE) for teaching and learning. In that regard, this paper describes the experiences of us and students of our involvement in oral and written tests in multiple software engineering-related courses during 2020 and 2021. The challenges encountered along with the interventions are discussed, and educational lessons based on the reactions and responses of the students are given. The results of a preliminary survey of the students of their learning experience in the OLE are presented and, related to it, the comments from the students highlighting their preferences of the OSE or the OLE are included. The test procedures, processes, and/or practices herein are, in principle, generalizable and potentially applicable to other courses in computer science or software engineering, during emergency remote teaching or even otherwise.

Data-driven optimization of accessory combinations for final testing processes in semiconductor manufacturing

This paper proposes an integrated framework to predict first yield, in order to find the best-practice accessory combination for the final testing (FT) process in semiconductor manufacturing. Firstly, the entity embedding method is adopted to convert the categorical data of accessory combination into multidimensional vectors. Several possible algorithms in machine learning are then examined to establish the yield prediction model and select the one that is the best fit. Once the best machine learning model has been determined, the genetic algorithm (GA) embedded in the yield prediction model is used to search for the best-practice accessory combination. This is the combination that gives the highest first yield estimate. The synergy between machine learning and the search heuristic produces an intelligent predictive system that can circumvent the adverse yield rates generated by using inappropriate accessory combinations. The Overall Equipment Effectiveness (OEE) of the FT process can be maintained in a highly stable condition.

Electrical Resistivity and Strength Parameters of Prismatic Mortar Samples Based on Standardized Sand and Lunar Aggregate Simulant

In situ resource utilization (ISRU) and automation are necessary. The logical first step of intention is to focus on our neighbor, the Moon, first. This work aims to expand our knowledge of the lunar aggregate simulant (LAS) based on ilmenite rock, which is available in Central Europe. Prismatic mortar samples were prepared based on standard sand and LAS. The measurements were conducted using several different destructive (DT) strength related tests and non-destructive (NDT) electrical resistivity measurement methods. The results were compared, and mutual correlation was evaluated. Finally, the ratio between volumetric (bulk) and surface resistivity tested on prismatic samples is presented. The results showed an average ratio of 7.19 for sand and 7.97 for ilmenite. The results show the potential feasibility of evaluating the properties of non-standard composite materials using durability-related NDT. The experimental testing presented that combines the DT and NDT methods in one sample represents a potential streamlining of the processes of future testing.

Impact of the COVID-19 Pandemic on Diabetes Care Among a North Carolina Patient Population.

In this study, researchers reviewed electronic health record data to assess whether the coronavirus disease 2019 pandemic was associated with disruptions in diabetes care processes of A1C testing, retinal screening, and nephropathy evaluation among patients receiving care with Wake Forest Baptist Health in North Carolina. Compared with the pre-pandemic period, they found an increase of 13-21 percentage points in the proportion of patients delaying diabetes care for each measure during the pandemic. Alarmingly, delays in A1C testing were greatest for individuals with the most severe disease and may portend an increase in diabetes complications.

Tiered approach for evaluation of anti-melanogenic activity of trans-N-coumaroyltyramine derivatives.

Skin hyperpigmentation is commonly treated by topical drug application. Several naturally occurring compounds exhibit attractive biological effects including anti-melanogenic activity. Chemically modified derivatives of those compounds are expected to be more efficient. However, efficacy and safety testing processes are of significant consideration to identify the most effective compound among them. Herein, we demonstrated a tiered approach to investigate the antipigmentation activity of 17 trans-N-coumaroyltyramine derivatives. First, we evaluated the inchemico antityrosinase activity, then the cytotoxicity of the most potent derivatives using a mitochondrial activity-based assay, followed with the invitro anti-melanogenic activity in two dimensional (2D) monolayer human melanocytes. The selected derivatives were topically applied on a three dimensional (3D) pigmented-reconstructed human epidermis (pRhE) containing melanocytes and keratinocytes to evaluate their depigmenting activity. Two of the 17 derivatives displayed a significant reduction in pigmentation in the 3D pRhE, comparable to kojic acid, a known tyrosinase inhibitor. In addition, a molecular docking experiment indicated an interaction of the three derivatives and tyrosinase, suggesting that these derivatives have potent anti-melanogenic activity through tyrosinase inhibition. Our findings provide an alternative approach for investigating skin-whitening agents, thereby facilitating the research and development of skin-whitening products that need not be tested on animals.

GENERALIZED STRUCTURE OF THE INFORMATION AND REFERENCE DATABASE OF ARMAMENT AND MILITARY EQUIPMENT OF THE INFORMATION SYSTEM OF TESTING SUPPORT

The hybrid and undisguised aggression against Ukraine by the Russian Federation over the past eight years has led to an understanding of the urgent need to arm the Armed Forces with the latest armament and military equipment, and has spurred a significant increase in domestic military developments. This, in turn, led to an increase in the need to test military products, and the receipt of test requests became widespread. This determines the relevance of the development and implementation of information support system for testing, the purpose of which is to automate test processes and improve the efficiency of the test organization as a whole.
 The article is a continuation of a series of publications that highlight the principles and approaches to the creation of information support system for testing samples of armament and military equipment, which is planned to be implemented at the State Scientific Research Institute of Armament and Military Equipment Testing and Certification. Based on the proposals on the functionality of the information system, a generalized structure of the information and reference database of armaments and military equipment has been developed. It is proposed to expand the current classifier of armament and military equipment while keeping the structure of the code, which provides coding and entering information into the database of modern and promising models of armament and military equipment of foreign production.
 It is assumed that the proposed structure of the information and reference database will provide functions to clarify and supplement the tactical and technical requirements for the tested model of armament and military equipment, which in turn will be the basis for developing programs and test methods, and will provide information needs for automated comparative assessment military-technical level of the studied product.
 It is expected that the introduction of an information system to support the testing of armament and military equipment can significantly increase the capacity of the test organization while ensuring the proper quality of the results.

Development of Science Module Based on the Meaning of Temperature and Heat Meaning Learning Model

This research is motivated by the rampant degradationvalues, attitudes, and character of students, especially junior high school students; In addition, there is also the lack of science learning resources owned by students that contain character education. The reseach used research and development (Reseach and Development / R & D) with the ADDIE approach, namely: Analysis, Design, Development, Implementation, Evaluation. Data analysis was carried out by qualitative analysis and quantitative analysis. The data analysis carried out is numerical (quantitative) and descriptive (qualitative) data. Numerical data (quantitative) in the form of numbers obtained from validators and student response questionnaires. While descriptive data (qualitative) in the form of suggestions/comments given by the validator during the validation process and students during the small-scale and large-scale test processes both written and unwritten. The results obtained from research that has been carried out in MTs are as follows. First, the level of validity of the science module based on the meaning learning model of 5 expert validators, namely 2 material experts 86.58%, 2 education experts 83.77%, and 1 science teacher 78.04%, an average of 82.79% in the very category. valid/usable without revision. Second, the response of students to the science module based on the meaning learning model with the results of the small group trial getting an average of 76.8% with the attractive/agree category with the number of students 5, then in the large group trial it got an average of 77,84% with the attractive/agree category with the number of students 30. The results of this study show that this module shows an increase in positive response and can be used as a learning material independently by students.

Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition

Recurrent neural networks (RNNs) have been widely used to solve sequence problems due to their capability of modeling temporal dependency. Despite the rich varieties of RNN models proposed in the literature, the problem of different sampling rates or performing speeds in sequence tasks has not been explicitly considered in the network and the corresponding training and testing processes. This paper addresses the problem of different sampling rates or performing speeds in the skeleton-based action recognition with RNNs. Specifically, the recently proposed independently recurrent neural network (IndRNN) is used as the RNN network due to its well-behaved and easily regulated gradient backpropagation through time. Samples are extracted with variable sampling rates and thus of different lengths, then processed by IndRNN with different time steps. In order to accommodate the differences in terms of gradients introduced by the backpropagation through time under variable time steps, a learning rate adjustment method is further proposed in the paper. Different learning rate adjustment factors are obtained for different layers by analyzing the gradient behavior under IndRNN. Experiments on skeleton-based action recognition are conducted to verify its effectiveness, and the results show that the proposed variable rate IndRNN network can significantly improve the performance over the RNN models under the conventional training strategies.

Performance Comparison of Quantized Control Synthesis Methods of Antenna Arrays

There is a great potential in small satellite technology for testing new sensors, processes, and technologies for space applications. Antennas need careful design when developing a small satellite to establish stable communication between the ground station and the satellite. This work is motivated by the design of an antenna array for a future rotatorless base station for the VZLUSAT group of Czech nano-satellites. The realized antenna array must cover a relatively broad range of elevation and azimuth angles, and the control must be fast enough to track the satellite in low Earth orbits. The paper deals with possibilities of synthesis of quantized control of the antenna array. It compares quantization influence for well-known deterministic synthesis methods. It shows the method for decreasing computational cost of synthesis using optimization approach and presents the multi-criteria optimization as a tool for reaching required radiation pattern shape and low sensitivity to quantization at the same time.

Analysis of Efficiency of Human Resource Management Evaluation Model Based on SOM Neural Network

The success of human resource management is directly related to whether enterprises can stand in the fierce market competition. In the actual operation of enterprises, the human resource management faces various risks. Here, we take the human resource management evaluation standard as the research object and establish the human resource management benefit evaluation model through a SOM neural network. The training, studying, and testing processes of the model are performed. Besides, the human resource management evaluation system is built on the basis of this model. The evaluation research on the efficiency of human resource management has a good influence in many aspects. Researchers have tried to decipher the “black box” relationship between human resource management and organizational benefits, and an effective human resource management evaluation tool is obtained. This model involves both theoretical construction and empirical research. It is of great significance to summarize the progress and existing problems of current research and abroad and put forward some suggestions for future research.