Random Testing Research Articles

Multi-shaker time-varying root mean square non-stationary random vibration environmental control test

The current multi-shaker random control methods concentrate on the simulation of stationary vibrations. This work presents an innovative technique for multi-shaker non-stationary random vibration environmental control test. Firstly, an amplitude modulation method is introduced to generate multi-input non-stationary random signals with time-varying root mean square values, whose kurtoses and moving RMS values are specified by amplitude modulation functions. Then, the generated signals are applied as excitation signals for the non-stationary random vibration environmental control test. The response signals of a linear vibration system under non-stationary excitations are derived in the frequency domain. The derived formulas for kurtoses and moving RMS values are also applicable to non-stationary response signals. Meanwhile, the stationary power spectral densities of response signals are put forward. When the amplitude modulation functions are zero mean Gaussian distributions, the power spectral densities of the response signals can be directly calculated. Ultimately, the control of kurtoses, moving RMS values and stationary power spectral densities of response non-stationary random signals is realized. Simulation and experimental examples both adopt a two-input two-output beam vibration system and the results prove the correctness and practicability of the proposed method. The presented control procedure is concise and efficient in simulating multi-shaker non-stationary random vibration environments.

A novel ‘3D + digital twin + 3D’ upscaling strategy for predicting the detailed multi-physics distributions in a commercial-size proton exchange membrane fuel cell stack

With the rapid development of proton exchange membrane fuel cell (PEMFC) commercialization, a comprehensive knowledge of multi-physics fields in large-scale PEMFC stacks has become ever more critical. Although conventional three-dimensional computational fluid dynamic (CFD) models have achieved great success, the application in the commercial-size stack-scale simulation remains inapplicable due to enormous computational resource requirements. Herein, based on the latest 3D CFD model, multi-physics digital twin (DT) technology and 3D stack flow distribution prediction model, a novel multi-scale upscaling prediction model is proposed. The voltage, water and thermal management characteristics of a 164-cell PEMFC stack with an active electrode area of 292.5 cm2 are studied and analyzed in details. For the analysis of commercial-size PEMFC stacks, the most comprehensive multi-physics fields are covered in this paper to date. And the results suggest that by introducing the DT technology, the time requirement of the multi-physics field prediction for unit scale prediction can be reduced by hundreds of thousands of times with a maximum global relative deviation of 1% under 10 groups of random test conditions, giving a solution from the cell scale to stack scale performance prediction, design, heat and thermal management in the PEMFC research and application.

Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied

Improving Movement Behavior in People after Stroke with the RISE Intervention: A Randomized Multiple Baseline Study.

Objective: High amounts of sedentary behavior increase the risk of cardiovascular disease. This study aimed to determine the preliminary effectiveness and feasibility of the RISE intervention to support community-dwelling people with stroke, who are highly sedentary, to reduce and interrupt sedentary time. Additionally, the added value of including participatory support was determined. Methods: A randomized, multiple-baseline study was conducted including 14 participants. All received the RISE intervention, a 15-week blended behavioral intervention in which a primary care physiotherapist provided personalized coaching in the home setting by using behavior-change techniques and the RISE eCoaching system, including an activity monitor and app to provide real time feedback. Half of the participants (randomly allocated) received participatory support from someone from their social network (e.g., partner or close friend) who joined them in the intervention. Preliminary effectiveness was determined with significant changes in total sedentary time and fragmentation (interruption) of sedentary time using a randomization test. Feasibility was assessed by adherence with the intervention protocol, safety, and satisfaction with the intervention. Results: Participants significantly reduced total sedentary time (p = 0.01) by 1.3 h on average and increased their fragmentation (p < 0.01). Subgroup analyses showed significant improvements in both outcomes only in the group with participatory support. Thirteen (92.9%) participants completed the intervention, no related adverse events occurred, and the reported participant satisfaction was sufficient. Conclusions: The RISE intervention appears promising to support people with stroke who are highly sedentary to reduce and interrupt their sedentary time. Participatory support appears to contribute to greater results. Trial registration: ISRCTN international trial registry, 10694741.

Fatigue life sensitivity research for electric driving system in driving load cases and random vibration load cases

In this paper, the fatigue characteristics of the electric drive system in driving load cases and random vibration load cases are analyzed numerically and experimentally. The fatigue life of the driving system under these two load cases is analyzed by using the time domain method and frequency domain method respectively. The results show that the damage of rotating parts (gear and bearing) mainly occurs in driving load cases, and the damage of gear and bearing in the driving load cases is 71.78% and 43.46% respectively. Fixed components (housing and suspension support) have certain damage in driving load cases and random vibration load cases, and the damage is greater in driving load cases. The damage of housing and suspension support in driving load cases is 10.6% and 0.167% respectively; the damage under random vibration load cases is 1.32 × 10−11 and 3.12 × 10−11 respectively. Based on the above conclusions, it is suggested that the driving load case tests for electric driving system are necessary, but it is not necessary to do random vibration tests only for electric driving system, or the number of random vibration tests can be reduced. This not only ensures the reliability of the components, but also helps to reduce the test cost.

Mid-infrared spectroscopy determines the provenance of coastal marine soils and their organic and inorganic carbon content

Vegetated coastal ecosystems (VCE), encompassing tidal marshes, mangroves, and seagrasses, serve as significant ‘blue’ carbon (C) sinks. Improving our understanding of VCE soils and their spatial and temporal dynamics is essential for conservation efforts. Conventional methods to characterise the dynamics and provenance of VCE soils and measure their total organic carbon (TOC) and inorganic carbon (TIC) contents are cumbersome and expensive.We recorded the mid-infrared (MIR) spectra and measured the TOC and TIC content of 323 subsamples across consistent depths from 106 soil core samples. Using the spectra of each VCE, we determined their mineral and organic composition by depth. We then used a regression tree algorithm, cubist, to model TOC and TIC contents. We rigorously validated the models to test their performance with a 10-fold cross-validation, bootstrapping, and a separate random test dataset.Our analysis revealed distinct mineralogical and organic MIR signatures in VCE soils that correlated with their position within the seascape. The spectra showed decreased clay minerals and increased quartz and carbonate with distance from freshwater inputs. The mineralogy of tidal marsh and mangrove soils differed with depth, showing larger absorptions due to carbonate and quartz and weakening clay minerals and organics absorptions. The mineralogy of the seagrass soils remained the same with depth. The cubist models to estimate TOC and TIC content were accurate (Lin's concordance correlation, ρc≥ 0.92 and 0.93 respectively) and interpretable, confirming our understanding of C in these systems. These findings shed light on the provenance of the soils and help quantify the flux and accumulation of TOC and TIC, which is crucial for informing VCE conservation. Moreover, our results indicate that MIR spectroscopy could help scale the measurements cost-effectively, for example, in carbon crediting schemes and to improve inventories. The approach will help advance blue C science and contribute to the conservation and protection of VCE.

SFIDMT-ART: A metamorphic group generation method based on Adaptive Random Testing applied to source and follow-up input domains

Context:The performance of metamorphic testing relates strongly to the quality of test cases. However, most related research has only focused on source test cases, ignoring follow-up test cases to some extent. In this paper, we identify a potential problem that may be encountered with existing metamorphic group generation algorithms. We then propose a possible solution to address this problem. Based on this solution, we design a new algorithm for generating effective source and follow-up test cases. Objective:To improve the performance (test effectiveness and efficiency) of metamorphic testing. Methods:We introduce the concept of the input-domain difference problem, which is likely to affect the performance of metamorphic group generation algorithms. We propose a new test-case distribution criterion for metamorphic testing to address this problem. Based on our proposed criterion, we further present a new metamorphic group generation algorithm, from a black-box perspective, with new distance metrics to facilitate this algorithm. Results:Our algorithm performs significantly better than existing algorithms, in terms of test effectiveness, efficiency and test-case diversity. Conclusions:Through experiments, we find that the input-domain difference problem is likely to affect the performance of metamorphic group generation algorithms. The experimental results demonstrate that our algorithm can achieve good test efficiency, effectiveness, and test-case diversity.

An Interview with Tsong Yueh Chen - 2024 SIGSOFT Awardee

Tsong Yueh Chen received the 2024 SIGSOFT Outstanding Research Award for contributions to software testing through the invention and development of metamorphic testing. He is currently a Professor of Software Engineering at the Department of Computer Science and Software Engineering, Swinburne University of Technology, Australia. Previously, he pursued a PhD degree from the University of Melbourne and an MSc degree and DIC from the Imperial College of Science and Technology, London, U.K. Before joining Swinburne University of Technology, he has lectured at The University of Hong Kong and The University of Melbourne. His research interests fall within the area of software engineering, with a focus on software analysis and testing, debugging, and program repair. He invented adaptive random testing and metamorphic testing. In 2021, the article he co-authored on "Adaptive Random Testing: The ART of test case diversity" (2010) was awarded with the Grand Champion of the Most Influential Paper Award of the "Journal of Systems and Software".

Ferroelectric Stochasticity in 2D CuInP2S6 and Its Application for True Random Number Generator.

True random number generators (TRNGs), which create cryptographically secure random bitstreams, hold great promise in addressing security concerns regarding hardware, communication, and authentication in the Internet of Things (IoT) realm. Recently, TRNGs based on nanoscale materials have gained considerable attention for avoiding conventional and predictable hardware circuitry designs that can be vulnerable to machine learning (ML) attacks. In this article, a low-power and low-cost TRNG developed by exploiting stochastic ferroelectric polarization switching in 2D ferroelectric CuInP2S6 (CIPS)-based capacitive structures, is reported. The stochasticity arises from the probabilistic switching of independent electrical dipoles. The TRNG exhibits enhanced stochastic variability with near-ideal entropy, uniformity, uniqueness, Hamming distance, and independence from autocorrelation variations. Its unclonability is systematically examined using device-to-device variations. The generated cryptographic bitstreams pass the National Institute of Standards and Technology (NIST) randomness tests. This nanoscale CIPS-based TRNG is circuit-integrable and exhibits potential for hardware security in edge devices with advanced data encryption.

Transcriptomic changes in the posterior pallium of male zebra finches associated with social niche conformance

Animals plastically adjust their physiological and behavioural phenotypes to conform to their social environment—social niche conformance. The degree of sexual competition is a critical part of the social environment to which animals adjust their phenotypes, but the underlying genetic mechanisms are poorly understood. We conducted a study to investigate how differences in sperm competition risk affect the gene expression profiles of the testes and two brain areas (posterior pallium and optic tectum) in breeding male zebra finches (Taeniopygia castanotis). In this pre-registered study, we investigated a large sample of 59 individual transcriptomes. We compared two experimental groups: males held in single breeding pairs (low sexual competition) versus those held in two pairs (elevated sexual competition) per breeding cage. Using weighted gene co-expression network analysis (WGCNA), we observed significant effects of the social treatment in all three tissues. However, only the treatment effects found in the pallium were confirmed by an additional randomisation test for statistical robustness. Likewise, the differential gene expression analysis revealed treatment effects only in the posterior pallium (ten genes) and optic tectum (six genes). No treatment effects were found in the testis at the single gene level. Thus, our experiments do not provide strong evidence for transcriptomic adjustment specific to manipulated sperm competition risk. However, we did observe transcriptomic adjustments to the manipulated social environment in the posterior pallium. These effects were polygenic rather than based on few individual genes with strong effects. Our findings are discussed in relation to an accompanying paper using the same animals, which reports behavioural results consistent with the results presented here.

Bounding Random Test Set Size with Computational Learning Theory

Bounding Random Test Set Size with Computational Learning Theory

Volitional modulation of neuronal activity in the external globus pallidus by engagement of the cortical-basal ganglia circuit.

Volitional modulation of neural activity is not confined to the cortex but extends to various brain regions. Yet, it remains unclear whether neurons in the basal ganglia structure, the external globus pallidus (GPe), can be volitionally controlled. Here, we employed a volitional conditioning task to compare the volitional modulation of GPe and primary motor cortex (M1) neurons as well as the underlying circuits and control mechanisms. The results revealed that the volitional modulation of GPe neuronal activity engaged both M1 and substantia nigra pars reticulata (SNr) neurons, indicating the involvement of the cortex-GPe-SNr loop. In contrast, the volitional modulation of M1 neurons primarily occurred through the engagement of M1 local circuitry. Furthermore, lesioning M1 neurons did not affect the volitional learning or volitional control signal in GPe, whereas lesioning of GPe neurons impaired the learning process for the volitional modulation of M1 neuronal activity at the intermediate stage. Additionally, lesion of GPe neurons enhanced M1 neuronal activity when performing the volitional control task without reward delivery and a random reward test. Taken together, our findings demonstrated that GPe neurons could be volitionally controlled by engagement of the cortical-basal ganglia circuit and inhibit learning process for the volitional modulation of M1 neuronal activity by regulating M1 neuronal activity. Thus, GPe neurons can be effectively harnessed for independent volitional modulation for neurorehabilitation in patients with cortical damage. KEY POINTS: The cortical-basal ganglia circuit contributes to the volitional modulation of GPe neurons. Volitional modulation of M1 neuronal activity mainly engages M1 local circuitry. Bilateral GPe lesioning impedes volitional learning at the intermediate stages. Lesioning of GPe neurons inhibits volitional learning process by regulating M1 neuronal activity.

Fatigue life prediction considering conversion of mean stress for titanium alloy under multiaxial thermo-mechanical random loading

This paper presents a thermo-mechanical fatigue (TMF) life prediction method for titanium alloy TA15 under multiaxial random amplitude loading. Through strain-controlled TMF tests, it was found that under full-reversed cyclic loading, there was a significant presence of mean stress in the stress response. The induced mean stress led to the anomalous phenomenon where the fatigue life of thermal phase angle out-of-phase (TOP) was shorter than that of thermal phase angle in-phase (TIP). Therefore, a new damage calculation model was proposed, which can take into account the pure fatigue damage considering mean stress conversion and the cumulative environmental damage. Uniaxial TMF tests, constant amplitude multiaxial TMF tests, and random amplitude multiaxial TMF tests are conducted to verify the proposed method. The predicted results for all types of tests are in a good agreement with the experimental data.

The Black Lives Matter movement mitigates bias against racial minority actors

Watching movies is among the most popular entertainment and cultural activities. How do viewers react when a movie sequel increases racial minority actors in the main cast ("minority increase")? On the one hand, such sequels may receive better evaluations if viewers appreciate racially inclusive casting for its novel elements (the value-in-diversity perspective) and moral appeal (the fairness perspective on diversity). On the other hand, discrimination research suggests that if viewers harbor biases against racial minorities, sequels with minority increase may receive worse evaluations. To examine these competing possibilities, we analyze a unique panel dataset of movie series released from 1998 to 2021 and conduct text analysis of 312,457 reviews of these movies. Consistent with discrimination research, we find that movies with minority increase receive lower ratings and more toxic reviews. Importantly, these effects weaken after the advent of the Black Lives Matter (BLM) movement, especially when the movement's intensity is high. These results are reliable across various robustness checks (e.g., propensity score matching, random implementation test). We conceptually replicate the bias mitigation effect of BLM in a preregistered experiment: Heightening the salience of BLM increases White individuals' acceptance of racial minority increase in a movie sequel. This research demonstrates the power of social movements in fostering diversity, equality, and inclusion.

Learning how to become an autonomy-supportive teacher begins with perspective taking: A randomized control trial and model test

Using self-determination theory, we sought to explain how teachers learn autonomy-supportive teaching. We randomly assigned 28 teachers (35.3 years-old, grades 7–9) and their 1566 students (13.4 years-old, 50.8% female) to participate or not in an autonomy-supportive teaching workshop. Teacher participation in the workshop increased autonomy-supportive teaching. Results from a multilevel structural equation modeling analysis showed that teachers in the experimental, compared to the control, condition first learned perspective taking skill, then learned the three teaching practices of interest support, value support, and lesser teacher control, which then explained their students’ year-end gains in need satisfaction and declines in need frustration.

Calcium deposition in chicken eggshells: role of host genetics and gut microbiota

Eggshell is predominantly composed of calcium carbonate, making up about 95% of its composition. Eggshell quality is closely related to the amount of calcium deposition in the shell, which requires chickens to maintain a robust state of calcium metabolism. In this study, we introduced a novel parameter, Total Eggshell Weight (TESW), which measures the total weight of eggshells produced by chickens over a period of 10 consecutive d, providing valuable information on the intensity of calcium metabolism in chickens. Genome-wide association study (GWAS) was conducted to explore the genetic determinants of eggshell calcification in a population of 570 Rhode Island Red laying hens at 90 wk of age. This study revealed a significant association between a specific SNP (rs14249431) and TESW. Additionally, using random forest modeling and 2-tailed testing, we identified 3 genera, Lactobacillus in the jejunum, Lactobacillus, and Fournierella in the cecum, that exhibited a significant association with TESW. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of claudin-1 and occludin genes in individuals with low TESW and high abundance of jejunal Lactobacillus confirmed that the inhibitory effect of jejunal Lactobacillus on calcium uptake was achieved through the up-regulation of tight junctions in intestinal epithelial cells. Notably, both host and microbial factors influence TESW, displaying a mutually influential relationship between them. The microbiome-wide Genome-Wide Association Study (mb-GWAS) identified significant associations between these 3 genera and specific genomic variants, such as rs316115020 and rs316420452 on chromosome 5, rs313198529 on chromosome 11, linked to Lactobacillus in the cecum. Moreover, rs312552529 on chromosome 1 exhibited potential association with Fournierella in the cecum. This study highlights the influence of host genetics and gut microbiota on calcium deposition in eggshells during the late laying phase, providing a foundational reference for studying calcium metabolism in hens.

Prediction of Fetal Health Status Using Machine Learning

The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizingmachine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetaldiseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using severalfactors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings todistinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiasedand precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliability

Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method

Abstract Artificial neural networks, machine learning, and data preparation are normally implemented in a wide range of real-world problems, especially in geotechnical applications with optimistic prospects of accurate procedure outcomes. This technique has been utilized to precisely predict the top settlement of piles with various piles and soil parameters. Generally, the pile settlement is an essential requirement to produce a secure structure and has high-performance services. The current article presents the fitting of the artificial neural network (ANN) outcomes by calculating the coefficient of correlation R 2 between the predicted and the measured or calculated value of pile settlement. The ANN algorithm is developed using Python 3.9 IDLE and open-source libraries such as Keras, sklearn, Numpy, matplotlib, pandas, and Tensorflow. Because of random training and test performance, the model has been run at least ten times. The ANN model score and R 2 are compared for all runs in the testing phase. The higher score and R 2 values are chosen. Moreover, the Multivariate Linear Regression with the sklearn library is also offered in this article and utilized to produce a pile settlement formula by applying the same dataset used in ANN. The score and R 2 for choosing the first run of the ANN are 99.95% and 0.9631, respectively, while the correlation coefficient for the Multivariate Linear Regression in the training and testing phases is 0.972 and 0.919, respectively. Both techniques illustrate considerable results.

Prevalence of toxoplasmosis in Iraqi patients with diabetic type 2

Background. The obligate intracellular protozoan parasite Toxoplasma gondii is a member of the phylum Apicomplexa. In people with impaired immune systems, it can result in opportunistic infections. Diabetes mellitus is regarded as a metabolic disease that raises the host's vulnerability to and risk from several infections, including T. gondii infection. The main object of this study is to ascertain the toxoplasmosis seroprevalence and correlation among Iraqi patients with type 2 diabetes. Methods. The level of Toxoplasma antibodies IgG, IgM, and IgA was measured in 109 samples of Iraqi diabetes type 2 patients using immunochromatography rapid test, CMIA, and ELISA. Eighty samples, considered to be a healthy control group, were collected from a private laboratory in Baghdad, Iraq, between March 2022 and June 2022, with an age range of 15 to 85 years. Results. Comparing the diabetes patient group to the non-diabetic control group, the results showed that the diabetic patient group had the highest mean glucose levels in their fasting test (174.55 ± 3.96 mg/dL), random test (216.89 ± 4.96 mg/dL), and HbA1C (7.1 ± 0.178), respectively. Furthermore, the study demonstrated that, in an immunochromatography fast test, the group of diabetic patients had the largest distribution of IgG anti-Toxoplasma when compared to the non-diabetic control group. Additionally, all samples were seronegative for anti-Toxoplasma IgM and IgA, with significant differences in CMIA and ELISA. However, 51/109 (46.79%) and 30/80 (37.5%) of the diabetes group and the non-diabetic group, respectively, showed seropositive for anti-Toxoplasma IgG. Conclusion. This study found that no acute toxoplasmosis detected in the studied cases while chronic toxoplasmosis detected among diabetic type 2 patients.

A dataset of land use types in the Lijiang River Basin from 2016 to 2020

Land use monitoring is an important basis for land resource management. On the basis of Sentinel-2 remote sensing image data, we used multiple preprocessing methods such as original data calibration, Mosaic and tailoring, and established remote sensing interpretation markers as classification training samples. Then we adopted the maximum likelihood method to classify the land use situation of the Lijiang River Basin in Guilin City from 2016 to 2020. Moreover, in combined with the original random test samples, we used the confusion matrix analysis method to evaluate the accuracy of the dataset. The classification results show a relatively good classification effect of land use types in the Lijiang River Basin, with an overall classification accuracy above 95%, and the kappa coefficient above 0.8. On the whole, the land use types in the Lijiang River basin changed little, and the main land use type is woodland, accounting for about 65.2%. The spatial heterogeneity show that the construction land area expanded from 4.17% to 5.19% from 2016 to 2020. This dataset can provide data support for the study of spatio-temporal changes of land pattern and land use transfer and evolution.