Random Testing Research Articles

SolarKey: Battery-free Key Generation Using Solar Cells

Solar cells have been widely used for offering energy for Internet of Things (IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this article, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing-based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%. We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random.

WALLAX: A memristor-based Gaussian random number generator

Generating Gaussian random numbers is essential in many applications such as cryptography, games, and computer simulations. Although software Gaussian Random Number Generators (GRNG) are widely used, hardware designs have been explored for their faster speed and lower computational cost. However, hardware GRNGs usually occupy large silicon areas when implemented in Complementary Metal Oxide Semiconductor (CMOS) technology, especially for their essential Uniform Random Number Generator (URNG) part. Here, we present a memristor-based GRNG, named WALLAX, conceived from the Wallace method to generate random numbers iteratively. This GRNG circuit benefits not only from the fully parallel analog-based Vector Matrix Multiplication (VMM) feature of memristive crossbars but also harness the intrinsic stochastic switching behaviour of the memristive devices to efficiently produce truly random numbers. The vector-matrix multiplication of WALLAX is implemented on the memristive crossbar, while its random fetching step is realized by a URNG based on the stochastic switching nature of memristors. WALLAX successfully passes all the tests in the NIST 800-22 randomness test suite with 105 numbers generated and five goodness-of-fit tests with various pool sizes and effectively reduces the power and area consumption by 68.78% and 70.0% compared to digital implementations of the same GRNG method. The impact brought by memristor non-idealities is investigated by simulating the proposed structure with 1000 pools under various scenarios. Wire resistance and the stuck of state, each result in a 2.2% and 12.3% reduction in test pass rate within the tested range, respectively.

A Modified Randomization Test for the Level of Clustering

Suppose a researcher observes individuals within a county within a state. Given concerns about correlation across individuals, it is common to group observations into clusters and conduct inference treating observations across clusters as independent. However, a researcher that has chosen to cluster at the county level may be unsure of their decision, given knowledge that observations are independent across states. This article proposes a modified randomization test as a robustness check for the chosen level of clustering in a linear regression setting. Existing tests require either the number of states or number of counties to be large. Our method is designed for settings with few states and few counties. While the method is conservative, it has competitive power in settings that may be relevant to empirical work.

Innovation of Pedagogy: Development of Digital Classroom to Increasing Student’s In Foreign Language Profiency

This study aims to develop a digital classroom to improve the foreign language skills of MA MUQ Langsa students in facing digital-based learning challenges according to the demands of the 21st century. To find out the effectiveness of using digital classes in foreign language learning. The method in this study is the research and development (R&D) method. The results of this study were testing foreign language learning materials with a score of 4.00 (good category) and media testing with media experts obtained a score of 4.00 (good category). Meanwhile, for random testing of students, a score of 4.00 (good category) was obtained. The average score obtained at the pretest was 38.88 and the posttest was 39.00. So that it can be explained from the results of alpha and beta testing and seeing the effectiveness of product development, it can be concluded that the product is feasible and effective for learning foreign languages. This of course will be very beneficial for teachers in transferring teaching materials and providing innovation in student learning spaces.

Multi-bit quantum random number generator from path-entangled single photons

Measurement outcomes on quantum systems exhibit inherent randomness and are fundamentally nondeterministic. This has enabled quantum physics to set new standards for the generation of true randomness with significant applications in the fields of cryptography, statistical simulations, and modeling of the nondeterministic behavior in various other fields. In this work, we present a scheme for the generation of multi-bit random numbers using path-entangled single photons. For the experimental demonstration, we generate a path-entangled state using single photons from spontaneous parametric down-conversion (SPDC) and assign a multi-qubit state for them in path basis. One-bit and two-bit random numbers are then generated by measuring entangled states in the path basis. In addition to passing the NIST tests for randomness, we also demonstrate the certification of quantumness and self-certification of quantum random number generator (QRNG) using Clauser, Horne, Shimony and Holt (CHSH) inequality violation. We also record the significantly low autocorrelation coefficient from the raw bits generated and this along with CHSH violation rules out multi-photon events and ensure the protection from photon splitting attack. Distribution of photons along multiple paths resulting in multiple bits from one photon extends the limit on bit generation rate imposed by the detection dead time of the individual detector. Thus, the path-entangled states can generate higher bitrates compared to scheme using entangled photon pair which are limited by the coincidence counts. We demonstrate this by generating a high rate of about 80 Mbps when the single photon detector saturates at around 28 Mcps and still show violation of CHSH inequality.

A Randomization-Based Theory for Preliminary Testing of Covariate Balance in Controlled Trials

Randomized trials balance all covariates on average and are the gold standard for estimating treatment effects. Chance imbalances nevertheless exist more or less in realized treatment allocations and intrigue an important question: what should we do if the treatment groups differ with respect to some important baseline characteristics? A common strategy is to conduct a preliminary test of the balance of baseline covariates after randomization, and invoke covariate adjustment for subsequent inference if and only if the realized allocation fails some prespecified criterion. Although such practice is intuitive and popular among practitioners, the existing literature has so far only evaluated its properties under strong parametric model assumptions in theory and simulation, yielding results of limited generality. To fill this gap, we examine two strategies for conducting preliminary test-based covariate adjustment by regression, and evaluate the validity and efficiency of the resulting inferences from the randomization-based perspective. The main result is 2-fold. First, the preliminary-test estimator based on the analysis of covariance can be even less efficient than the unadjusted difference in means, and risks anticonservative confidence intervals based on normal approximation even with the robust standard error. Second, the preliminary-test estimator based on the fully interacted specification is less efficient than its counterpart under the always-adjust strategy, and yields overconservative confidence intervals based on normal approximation. In addition, although the Fisher randomization test is still finite-sample exact for testing the sharp null hypothesis of no treatment effect on any individual, it is no longer valid for testing the weak null hypothesis of zero average treatment effect in large samples even with properly studentized test statistics. These undesirable properties are due to the asymptotic non-normality of the preliminary-test estimators. Based on theory and simulation, we echo the existing literature and do not recommend the preliminary-test procedure for covariate adjustment in randomized trials.

Analysis of neural network connections based on memristors and their multiple offset phenomena

The article suggests a construction method of a magnetron memristor connecting a three-dimensional Hopfield neural network and a Rulkov neuron in order to build a more complex and achieve more bio-like neural network properties, which has rarely been proposed before. It is discovered that the dynamical behavior of this high-dimensional neural network system is rich, and that the system exhibits many dynamical behaviors depending on the parameter changes. It is possible to change the attractor’s amplitude and its offset boosting behavior by varying various parameters. Changing the system parameters and modifying the system’s initial value may result in initial offset boosting behavior. Combining nonlinear dynamics research methodologies, such as phase diagram, bifurcation diagram, Lyapunov exponential spectrum, and time series diagram, demonstrates the system’s complex dynamical behavior. By analyzing the system complexity and random sequence test, we found that the system has the characteristics of large complexity and strong pseudo-randomness. Eventually, the hardware realizability is proved by the construction of the DSP platform.

Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map

In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.

Evaluation of statistical models for forecasting area, production and productivity of groundnut in Gujarat

Groundnut is one of the most important oilseed crops in India. Forecasting is used to provide decision making and planning for the future effectively and efficiently. The study was carried out to estimate the trends of area, production and productivity of groundnut crop in Gujarat for the period of 1991-92 to 2019-20. The data were collected from the Directorate of Agriculture, Gandhinagar, Gujarat. The linear, quadratic and cubic models were fitted on original data along with moving average method. The ARIMA models were also developed to forecast area, production and productivity of groundnut. Among the fitted polynomial and ARIMA models, the suitable model was identified on basis of significance of regression coefficient, AR and MA, AIC and SBC values, adjusted R2, RMSE, MAE, normality and randomness test etc. The study revealed that, for the area, cubic model on five year moving average and for production and productivity, linear model on five year moving average were found best in Gujarat. The ARIMA(2,1,0) model wasfound suitable to forecast and explain the pattern of groundnut area in Gujarat. It was also observed that none of the ARIMA models tested was suitable for predicting groundnut production and productivity in Gujarat.

Gut Microbiome and Lipidome Signatures in Irritable Bowel Syndrome Patients from a Low-Income, Food-Desert Area: A Pilot Study.

Irritable bowel syndrome (IBS) is a common gastroenterological disorder with triggers such as fructose. We showed that our IBS patients suffering from socioeconomic challenges have a significantly high consumption of high-fructose corn syrup (HFCS). Here, we characterize gut microbial dysbiosis and fatty acid changes, with respect to IBS, HFCS consumption, and socioeconomic factors. Fecal samples from IBS patients and healthy controls were subjected to microbiome and lipidome analyses. We assessed phylogenetic diversity and community composition of the microbiomes, and used linear discriminant analysis effect size (LEfSe), analysis of compositions of microbiomes (ANCOM) on highly co-occurring subcommunities (modules), least absolute shrinkage and selection operator (LASSO) on phylogenetic isometric log-ratio transformed (PhILR) taxon abundances to identify differentially abundant taxa. Based on a Procrustes randomization test, the microbiome and lipidome datasets correlated significantly (p = 0.002). Alpha diversity correlated with economic factors (p < 0.001). Multiple subsets of the phylogenetic tree were associated with HFCS consumption (p < 0.001). In IBS patients, relative abundances of potentially beneficial bacteria such as Monoglobaceae, Lachnospiraceae, and Ruminococcaceae were lower (p = 0.007), and Eisenbergiella, associated with inflammatory disorders, was higher. In IBS patients, certain saturated fatty acids were higher and unsaturated fatty acids were lower (p < 0.05). Our study aims first to underscore the influence of HFCS consumption and socioeconomic factors on IBS pathophysiology, and provides new insights that inform patient care.

‘3D-QSAR-based, pharmacophore modelling, virtual screening, and molecular docking studies for identification of hypoxia-inducible factor-1 inhibitor with potential bioactivity

Iron overload is a primary cause of vital organ failure in patients with blood transfusion-dependent beta-thalassemia, and the hypoxia-inducible factor-1 α (HIF-1α) plays an important role in iron homeostasis pathway. HIF-1α modulation as a potential therapeutic target approach for iron chelation in hepatocyte cells. In this study, we used a 3D quantitative structure-activity relationship (QSAR) analysis to predict the inhibitory activity of HIF-1α inhibitors for iron chelation in liver cells. These feature descriptors were used to build a 3D-QSAR model, which was validated using Cost analysis and Fischer's randomization test. The model was used to virtually search the chemical compound libraries for potential inhibitor candidates with least inhibitory activity. The High-throughput Docking (Libdock) approach was used to dock large repositories of chemical molecules. Following Libdock score screening, the protein-ligand poses were docked using docking optimization (Cdocker) method. Binding energy were calculated for the protein-ligand poses of lowest –Cdocker Energy and –Cdocker Interaction. Further, side chain hopping method was used to generate lead novel ligand from best hit pose of ligand. Molecular dynamics simulation study to evaluate the lead novel ligand. Our study demonstrates the utility of 3D-QSAR pharmacophore screening in predicting the inhibitory activity for target. Inhibition strategy for iron chelation provides an alternative routes for reducing the iron content.

OR25-03 Self-administration Of Post-cycle Therapy Is Associated With Increased Probability Of Subsequent Normalisation Of Reproductive Hormones Following Anabolic-androgenic Steroid Cessation In Men

Abstract Disclosure: B. Grant: None. J. Campbell: None. A. Pradeep: None. A.D. Burns: None. P. Bassett: None. P. Saket: None. S. Minhas: None. W.S. Dhillo: None. J. McVeigh: None. S. Bhasin: None. C.N. Jayasena: Grant Recipient; Self; Logixx Pharma Ltd. Context: Millions of men worldwide illicitly use anabolic-androgenic steroids (AAS) for muscle growth. However, AAS can cause death, cardiomyopathy, stroke, and psychosis. AAS use suppresses endogenous testosterone secretion for several months following cessation. Therefore, AAS cessation causes tiredness, low mood, insomnia, absent sexual function, and suicidality. There is currently no treatment recommended to reduce symptoms when men stop AAS. Some men empirically self-administer post-cycle therapy (PCT) drugs such as human chorionic gonadotrophin (hCG), selective oestrogen receptor modulators (SERM) and aromatase inhibitors (AI), aiming to restore endogenous testosterone. hCG directly stimulates endogenous testosterone production, while SERMs and AIs reduce the oestrogenic negative feedback on gonadotrophin secretion. Currently evidence is lacking to support the use of PCT in AAS-induced hypogonadism. Methods: Clinical audit from a single addiction service clinic, of 613 men stopping AAS in Scotland between 2015-2022. Men attended for a single, non-fasting, random blood test performed within 12 months of AAS cessation, with or without PCT use. Primary endpoint was the combination of reference range levels of serum LH, serum FSH and total testosterone, as a surrogate marker of biochemical recovery from hypogonadism. Results: PCT use was reported by 76% of men. Men using PCT had a significantly higher serum total testosterone following AAS cessation compared to men who did not (mean total testosterone nmol/L: 11.3 ± 6.7, no PCT; 12.8 ± 7.6, PCT; P=0.024). PCT use was associated with a higher probability (223/466, 48%, PCT; 56/147; 38%, no-PCT; P=0.04), and shorter time interval between stopping AAS and blood test of normalised reproductive hormones (13.3 ± 8.8 weeks, PCT;18.7 ± 12.0 weeks, no-PCT; P&amp;lt;0.01). The odds of biochemical normalisation during multivariable analysis were statistically significant when: (1) PCT was used (P=0.01); (2) fewer AAS were used (P=0.003); (3) shorter time of AAS use (P=0.02); (4) AAS used stopped for a longer time (P=0.03). Discussion: Our data provide primary evidence that self-administered PCT drugs may be associated with improved biochemical recovery from AAS-induced hypogonadism. These data require corroboration within an interventional study to determine causality. Nevertheless, these data may have important therapeutic implications for the future treatment of men who are motivated to stop AAS. Presentation Date: Saturday, June 17, 2023

Prediction of the Transit Time of Coronal Mass Ejections with an Ensemble Machine-learning Method

Coronal mass ejections (CMEs), a kind of violent solar eruptive activity, can exert a significant impact on space weather. When arriving at the Earth, they interact with the geomagnetic field, which can boost the energy supply to the geomagnetic field and may further result in geomagnetic storms, thus having potentially catastrophic effects on human activities. Therefore, accurate forecasting of the transit time of CMEs from the Sun to the Earth is vital for mitigating the relevant losses brought by them. XGBoost, an ensemble model that has better performance in some other fields, is applied to the space weather forecast for the first time. During multiple tests with random data splits, the best mean absolute error (MAE) of ∼5.72 hr was obtained, and in this test, 62% of the test CMEs had absolute arrival time error of less than 5.72 hr. The average MAE over all random tests was ∼10 hr. It indicates that our method has a better predictive potential and baseline. Moreover, we introduce two effective feature importance ranking methods. One is the information gain method, a built-in method of ensemble models. The other is the permutation method. These two methods combine the learning process of the model and its performance to rank the CME features, respectively. Compared with the direct correlation analysis on the sample data set, they can help select the important features that closely match the model. These two methods can assist researchers to process large sample data sets, which often require feature selection in advance.

Рsychoacoustic testing to assess the functional maturation of the central audiotory system

The age-appropriate development of the central auditory system is crucial for a child’s normal auditory and speech development. If there are any issues with this development, it can lead to central auditory processing disorders (APD) and problems with psychoverbal and general development. Psychoacoustic testing is an informative and accessible diagnostic tool for identifying signs of APD. This testing can be performed on children as young as four years old, provided there are normative data available for different age groups. The purpose of this study was to assess the functional state of the central auditory system using psychoacoustic methods in healthy children of different ages. Materials Methods. We examined 125 healthy full-term children between the ages of 4 and 17 years who had normal peripheral hearing and no speech, language, cognitive, or academic problems. The children were divided into five age groups: 4–5 years 11 months, 6–7 years 11 months, 8–9 years 11 months, 10–11 years 11 months, and 12 years and older. In addition to traditional audiological examinations, all children underwent tests to assess the functional state of the central parts of the auditory system, including tests for the perception of rhythmic sequences of stimuli, Random Gap Detection Test, monaural low redundant speech testing in quiet and in noise, alternating binaural speech testing, dichotic digits test, and a simplified version of the Russian matrix sentence test in noise (RUMatrix). The results showed that the tests used were sensitive to the functional state of various structures of the central auditory system, and signs of maturation in the “bottom-up” direction were demonstrated as the children grew older. The rate of evolutionary processes varied depending on the age group of the subjects. It was also shown that the morphofunctional development of the central auditory system is not completed by adolescence. Conclusion. These findings can be used to differentiate between the immaturity of the central auditory system, APD, and speech-language disorders of different types in children of different ages. Overall, this study emphasizes the importance of early detection and intervention for any issues related to the central auditory system in children.

The association between anti-glutamic acid decarboxylase IgG and hemoglobin A1C among newly diagnosed type 1 diabetes of some Iraqi children in Karbala City

Abstract Background: Type 1 diabetes mellitus (T1DM) is brought on by immune system dysfunction intolerance, invasion of beta cells by autoreactive immunological T cells, degradation of beta cell activity and survival, and extended exogenous insulin treatment. One of the circulation autoantibodies against antigens of pancreatic cell that produce insulin is glutamic acid decarboxylase (GAD IgG antibody) that indicate the stress and/or death of the islet cell in T1D. A blood glucose monitoring test called hemoglobin A1C (HbA1c) is used to determine the average blood sugar level over three months (February, March, and April of 2022). Objective: The purpose of this study is to assess the frequency of anti-GAD IgG and HbA1c in T1DM children, as well as their correlation. Materials and Methods: Seventy individuals with new-onset T1DM (confirmed by clinical signs, including increased urination, intense hunger, intense thirst, and abrupt weight loss, along with a random blood sugar test that is higher than 200 mg/dL and a fasting blood sugar test that is higher than 126 mg/dL) are male and female, ranging in age from 1 to 12 years. Blood was drawn for the biochemical and immunological tests (including HbA1c, fasting blood sugar, C-peptide, anti-islets antibodies, and anti-GAD antibodies). Results: Ninety-four percent of the T1DM patients had positive anti-GAD IgG with a mean concentration of 147 IU/mL, and 6% had negative anti-GAD IgG with a mean value of 9 IU/mL. At the same time, the findings of the current study’s calculation of HbA1c levels revealed that individuals with newly diagnosed T1DM had a total mean average of HbA1c of 10.53. Conclusion: Specifically in T1D that had arisen in extremely young children, autoimmunity was discovered to take part in the glycemic index expressed by glycated hemoglobin average.

Automated and Efficient Test-Generation for Grid-Based Multiagent Systems

Automatic generation of random test inputs is an approach that can alleviate the challenges of manual test case design. However, random test cases may be ineffective in fault detection and increase testing cost, especially in systems where test execution is resource- and time-consuming. To remedy this, the domain knowledge of test engineers can be exploited to select potentially effective test cases. To this end, test selection constraints suggested by domain experts can be utilized either for filtering randomly generated test inputs or for direct generation of inputs using constraint solvers. In this paper, we propose a domain specific language (DSL) for formalizing locality-based test selection constraints of autonomous agents and discuss the impact of test selection filters, specified in our DSL, on randomly generated test cases. We study and compare the performance of filtering and constraint solving approaches in generating selective test cases for different test scenario parameters and discuss the role of these parameters in test generation performance. Through our study, we provide criteria for suitability of the random data filtering approach versus the constraint solving one under the varying size and complexity of our testing problem. We formulate the corresponding research questions and answer them by designing and conducting experiments using QuickCheck for random test data generation with filtering and Z3 for constraint solving. Our observations and statistical analysis indicate that applying filters can significantly improve test efficiency of randomly generated test cases. Furthermore, we observe that test scenario parameters affect the performance of the filtering and constraint solving approaches differently. In particular, our results indicate that the two approaches have complementary strengths: random generation and filtering works best for large agent numbers and long paths, while its performance degrades in the larger grid sizes and more strict constraints. On the contrary, constraint solving has a robust performance for large grid sizes and strict constraints, while its performance degrades with more agents and long paths.

A Comparative Study of Manual and Digital Methods and Stereopsis Assessment in Young Adults

Reduced stereopsis or depth perception may be the early indicator of abnormal motor functions in people therefore, the stereopsis test is considered ideal for visual screening. It can accomplish the need for the assessment of stereopsis by using a cost-effective smartphone application. Objective: To compare the stereo acuity values of manual and digital stereopsis tests by using the Random Dot Stereo Acuity test with Lea Symbols (Manual) and the SAT App for Android (Digital). Methods: A comparative cross-sectional study was carried out in the Eye department at The University of Lahore Teaching Hospital from February 2023 to May 2023. A total of 62 subjects aged 15-35 years were included in our study, out of which 31 were emmetropes and 31 were ametropes. Screening of subjects was done which included torch light examination and basic refraction. Manually stereopsis was measured using Random Dot stereo acuity test with Lea symbols and digitally it was measured by SAT application. Results: The mean ± SD stereo acuity value of the manual test (N=62) was 60.5242 ± 36.47607 seconds of arc and the mean stereo acuity value of the digital test (N=62) was 70.0968 ± 28.29569 seconds of arc. P-value was 0.006 which confirmed that our results were statistically significant. Conclusions: Our study highlighted the comparison of digital and manual stereopsis tests. We obtained different stereo acuity values from both tests on the same individuals. Hence, the manual test gave better values of stereopsis than the digital test.

Penerapan Machine Learning Untuk Prediksi Masa Studi Mahasiswa di Perguruan Tinggi X

Universities play a role in producing quality resources from their graduate students, so that the quality and accreditation of tertiary institutions are things that need attention. One indicator of higher education accreditation is student graduation on time, so student graduation must be an important concern for tertiary institutions. Based on the results of the documentation, the percentage of students graduating on time is lower than students who are not completing their studies on time, therefore it is necessary to analyze the student's study period to overcome the study period that graduates are not on time using the machine learning method with the Naïve Bayes Classifier algorithm to predict student study period. The research method uses the Naïve Bayes Classifier algorithm method which is part of Artificial Intelligence (AI), which consists of preprocessing, input, process and output. because this method has high accuracy and can work better in real-world cases. The results of predicting the timeliness of student study time at college X with 3553 data using the Naïve Bayes Classifier algorithm method, using WEKA tools succeeded in predicting student study time with 70% data taining and 30% as random testing data with the system. Using 11 attributes, namely study program, GPA, mother's occupation, mother's income, entry period, father's occupation, father's income, route of entry, gender, and school of origin, obtained a percentage of precision value of 54.545%, recall value of 67.220%, and the accuracy level reaches 79.925% which is categorized as good, using the ROC curve calculation to form almost close to (0.1) with an AUC value of 0.844 which is categorized as very good. Based on the results of the percentage accuracy rate, ROC curve and AUC value, the Naïve Bayes Classifier predicts student graduation in the "Good" category

Relationship Between Gestational Diabetes Mellitus and Postpartum Diastasis Recti Abdominis in Women in the First Year Postdelivery.

Postpartum diastasis recti abdominis (DRA) influences women's appearance and health. Gestational diabetes mellitus (GDM) can affect the structure of the rectus abdominis muscles. However, the relationship between GDM and postpartum DRA is unknown. The objective of this study was to investigate the relationship between GDM and postpartum DRA. This retrospective cohort study included 241 women in the first year postdelivery. Women with GDM were matched with those without GDM using propensity score matching. They underwent an oral glucose tolerance test during pregnancy and a random blood glucose test before delivery. At follow-up, DRA was diagnosed by palpation, and interrectus distance was measured using ultrasound to evaluate the severity of DRA. The strength of the rectus abdominis was evaluated using the manual muscle testing method. Among the 241 participants, 174 (72.2%) had postpartum DRA, and 46 women with GDM were matched with 46 women without GDM on the basis of propensity scores. Women with GDM had higher odds of experiencing postpartum DRA (adjusted odds ratio = 4.792; 95% CI = 1.672 to 13.736) and larger interrectus distance values at the upper part of the rectus abdominis than those without GDM. There was a weak and positive correlation between the fasting oral glucose tolerance test level and the interrectus distance values (0.267 ≤ r ≤ 0.367). GDM was associated with postpartum DRA in women in the first year of delivery. Women with GDM had larger interrectus distance values at the upper part of the rectus abdominis than those without GDM. The fasting oral glucose tolerance test level showed a positive and weak correlation with the severity of postpartum DRA. Women with GDM have higher odds of experiencing postpartum DRA than those without GDM. The upper part of the rectus abdominis deserves increased focus during and after rehabilitation. Controlling the fasting oral glucose tolerance test level may help reduce the severity of postpartum DRA.

Spectral decoupled correction algorithm for multi-input multi-output random vibration control

The multi-output response spectral replication correction algorithms for the multi-shaker random vibration test are investigated. In the existing matrix power correction algorithm, the control of auto and cross spectral densities is coupled which may bring some unexpected errors. The ideal spectral correction mode is that the control of auto and cross spectral densities should be independent of each other. For this purpose, a novel spectral decoupled correction algorithm is proposed with a spectral decoupling decomposition method in this work. Two lower triangular matrices are introduced to modify the auto and cross spectral densities separately during equalization procedure. Due to the positive definite constraint for the power spectral matrix, the coherence coefficient and phase difference in the cross spectral densities cannot be controlled independently when the number of the control channels is more than two. A simulation example is provided and the control results with the matrix power correction algorithm and proposed spectral decoupled correction algorithm are compared. Finally, a tri-axial shaker random vibration test is carried out and the results prove the effectiveness and feasibility of the proposed method.