Random Testing Research Articles

Dissimilarity measures based on the application of Hamming distance to generate controlled probabilistic tests

Objectives. The problem of constructing dissimilarity measures based on the application of the Hamming distance to generate controlled random binary test sets is solved. The main goal of this article is to develop methods for determining the Hamming distance for the achievability of finding the difference between test sets when they coincide according to estimates of other difference measures.Methods. Based on the Hamming distance used in the theory and practice of generating controlled random tests, new dissimilarity measures are proposed for two binary test n-bit patterns. The basis of the proposed dissimilarity measures is the formation of sets of Hamming distances for initial sets, represented as sequences of characters from different alphabets.Results. The indistinguishability of pairs of binary test sets Ti and Tk is shown using a dissimilarity measure based on the application of the Hamming distance. In this case, different pairs of sets may have identical Hamming distance values. To construct new measures of difference, the original binary test sequences are represented as sequences consisting of characters belonging to different alphabets. Various strategies are proposed for applying new measures of difference based on the use of one of three rules in generating controlled probability tests. It is shown that in all three cases of dissimilarity measures, only the first few of their components areinformative, as a rule, no more than two or three. Accordingly, the computational complexity for all three options is comparable and does not exceed 3n comparison operations. The experimental studies carried out confirm the effectiveness of the proposed dissimilarity measures and their low computational complexity.Conclusion. The proposed dissimilarity measures expand the possibilities of generating test sets when forming controlled random tests. It is shown that test sets that are indistinguishable when using the Hamming distance as a dissimilarity measure have different values of the proposed dissimilarity measures, which makes it possible to more accurately classify randomly generated sets that are candidate test cases

Recognition and Prediction of Precursory Feature Signals of Coal Mine Rock Burst Based on Random Forest and MK Trend Test

This paper aims to identify and predict the precursory characteristic signals of coal mine rock burst by employing the Random Forest and MK (Mann-Kendall) trend test methods. Initially, the study conducts data preprocessing and analysis on indicators such as electromagnetic radiation intensity, acoustic wave intensity, and type. It uses clustering methods to distinguish various data types and analyzes the data after visual representation. The visualization of data illustrates the distribution and trends, which aids in understanding the characteristics of the data, such as the upward trend observed in precursory feature data. During the data preprocessing phase, diagrams of various classes of data for Acoustic Emission (AE) and Electromagnetic Radiation (EMR) intensity are presented, providing an intuitive reference for subsequent analysis. The research utilizes the Random Forest algorithm and MK trend test to recognize precursor signals and predict their occurrence intervals. The Random Forest model is chosen for its efficiency and accuracy in handling classification and regression issues, while the MK trend test provides a statistical basis for identifying precursory signals by analyzing monotonic trends within the dataset. This study not only enhances the accuracy of precursory signal identification for coal mine rock bursts but also offers scientific early warning and control measures for coal mine safety production, which is of significant practical value.

Thermomechanical Collaborative Design and Experimental Verification of a Sandwich Structure with a Polyimide Foam Core

This paper introduces the technology and performance of a new sandwich structure consisting of carbon fiber composite face sheets, a polyimide foam core, and an aluminum honeycomb panel with both heat insulation and load-bearing functions. The heat-resistant performance of the sandwich structure was evaluated. The vacuum thermal conductivity of the sandwich structure in the vertical direction ranges from 0.54 to 0.92 (W/[m⋅K]) with increasing temperature from −170 to 135°C. A multilayer finite element model of carbon fiber cloth, polyimide foam, and aluminum honeycomb was established. Finally, a comprehensive evaluation of the mechanical and thermal insulation performance of the sandwich structure was conducted through random vibration testing and thermal vacuum testing. The results showed that the sandwich structure had good stiffness and thermal insulation performance, successfully verifying the effectiveness of the collaborative design.

An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE.

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer's disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R2 = 0.701, R2test = 0.76, Q2CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model's robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer's activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (-12.6 kcal/mol), (-13 kcal/mol), and (-12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (-10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors.

A qualitative study of the first COVID-19 pandemic lockdown: The impact on alcohol consumption and driving behaviours

COVID-19 influenced life in a multitude of ways beyond immediate health impacts. This included driving behaviours. People drove less often during the early months of the pandemic, yet crash rates increased. There were reports of increased alcohol consumption during this same time which was considered to impact on driving behaviours such as drink driving. This study explores the thoughts and opinions of Queensland, Australian drivers (n = 798) concerning driving behaviours, alcohol consumption, and drink driving behaviours during the early phases of the pandemic (April – August 2020). Participants responded to qualitative questions in online questionnaires over three phases that prompted thoughts in these domains. Bronfenbrenner’s bioecological model was found to explain participants’ comments with the six layers seeming to impact driving, alcohol consumption, and drink driving behaviours. That is, these behaviours were influenced at an individual level (e.g., coping mechanisms), the microsystem level (e.g., reduced opportunities for socialisation), the mesosystem (e.g., work demands impacting family), the exosystem (e.g., large scale random breath testing was suspended and road police deployed to border control), the macrosystem (e.g., Australian drinking culture), and the chronosystem (the pandemic). Understanding the perspectives of individuals during a sudden shift in life caused by COVID-19 highlights the broader impact of the pandemic on people’s behaviours. In addition, this study also provides important insights into the intricacies of driving, drinking and drink driving behaviours. These understandings can inform future research and countermeasures to prevent these risky behaviours.

Vascular flora of Punta Arenas city: comparative analysis of composition, life forms, and biogeographic origins

The composition of the vascular flora in Punta Arenas city, found in the city’s public spaces, was studied. The species were identified and recorded in a database, which was supplemented with information on taxonomic classification, growth habit, Raunkiaer’s life form, origin status (native or exotic), and original continent-level distribution. These data were compared with studies conducted in five other cities in central Chile, together with an analysis of compositional similarity with these cities, by using the additive complement of Simpson’s index (1– βsim). In Punta Arenas, 119 species were identified, showing a higher proportion of Gymnosperms compared to central Chilean cities. The most represented families were Asteraceae (16 species), Fabaceae (14), Rosaceae (14), Poaceae (12), and Pinaceae (10), which together accounted for 55% of the floristic richness. The compositional similarity between Punta Arenas and the other central Chilean cities ranged from 0.187 to 0.315, showing lower similarity than expected by chance (Montecarlo randomization test; P < 0.05). The primary origin distribution of Punta Arenas’ vascular flora was European, unlike central Chilean cities where it was Asian. Finally, the proportion of exotic species (91.6%) and the number of exotic species per native species (16 exotics/native) were the highest documented for cities in Chile and higher than in other 114 cities worldwide. These results indicate that Punta Arenas’ urban flora differs from the flora in central Chilean cities, not only in taxonomic composition but also in growth habit, biogeographical origin, and high level of exoticism. These differences are likely due to the city’s territorial isolation and extreme southern location (53°S), leading to a unique urban flora configuration.

How do perceptions of procedural justice, police legitimacy, and legitimacy of laws influence intentions to drug drive?

Introduction: There is a need for improved drug driving enforcement to promote greater driver compliance with drug driving laws. In Australia, Roadside Drug Testing (RDT) suffers from operational challenges that undermine its effectiveness in reducing drug driving. Objective: To identify potential improvements to RDT, this study investigated the extent to which drivers perceive RDT to be procedurally just and that the policing of drug driving and the associated laws are legitimate. These perceptions were then compared with those applying to Random Breath Testing (RBT) and examined in relation to their respective influence on intentions to drug and drink drive in the future. Method: A sample of 1,483 licensed drivers from three Australian states completed an online survey. Results: Those participants who reported engaging in drug driving perceived RDT to be less procedurally just than non-drug drivers. Similarly, drug drivers perceived the police and associated drug driving laws to be less legitimate than non-drug drivers. Furthermore, drug drivers who had been tested at an RDT operation in the past perceived RDT to be less procedurally just and considered drug driving policing and laws to be less legitimate, compared with the corresponding perceptions of drink drivers who had been tested at an RBT operation. A regression analysis indicated that stronger intentions to drug drive in the future were associated with lower perceptions of police legitimacy and the legitimacy of drug driving laws, but not with the elements of procedural justice. However, follow-up analyses indicated that the influence of procedural justice on intentions was mediated by the two legitimacy variables, thus weakening its direct impact on intentions. Practical applications: The results highlight the need for road safety authorities to enhance the perceived legitimacy of drug driving enforcement and associated laws. Changes to current police practices and/or drug-driving laws may also be needed to enhance the effectiveness of RDT.

Research on multi-apparent defects detection of concrete bridges based on YOLOR

To enhance the accuracy of detecting apparent defects in concrete bridges, this study proposes an improved model based on the YOLOR. Initially, a concrete bridges defect (CBD) dataset comprising 7009 images was established, encompassing nine distinct types of defects: pockmark, spalling, cavity, whitening, reticular crack, single crack, rust expansion, exposed rebar and leakage. Subsequently, using the CBD dataset, performance comparison experiments involving YOLOv3, YOLOv4, YOLOR-640, and YOLOR-1280 were conducted to select the optimal model. Based on the characteristics of the apparent defects in bridges, the anchor boxes and localization loss function were optimized respectively, thereby proposing an improved model named YOLOR-BDIM. The results indicate that the mAP of YOLOR-BDIM increased by 2.7 % compared with initial model. Furthermore, YOLOR-BDIM achieved a detection accuracy of 97.5 % during the random tests, showing excellent detection performance even in complex environments.

Vibration fatigue assessment and crack propagation mechanism of directionally solidified superalloy with film cooling holes

Film cooling represents a critical protective measure for turbine blades, yet the presence of film cooling holes (FCHs) under vibrational loads can significantly impact structural strength and integrity. This study conducts random fatigue tests on DZ125L directionally solidified superalloy specimens with FCHs. It investigates how various FCH types and vibration signal intensities influence the vibration fatigue behavior of DZ125L alloy. Characterization of vibration fatigue fracture and surface cracks of FCH specimens utilizes ultra depth of field microscopy and scanning electron microscopy. Additionally, the crack propagation mechanism for FCH specimens under random processes is proposed based on finite element stress distribution and fracture morphology. Results reveal the generation of two types of cracks, namely hole cracks and edge cracks, under vibration load in FCH specimens. The crack propagation process produces water wave-like fatigue striations. Notably, a low stress zone exists between the two dangerous holes in the multi-hole specimen, mitigating the expansion trend of hole cracks between the FCHs compared to cracks expanding towards the edges. Furthermore, two novel models, New1 and New2, are introduced to enhance the applicability of the frequency domain method for predicting the fatigue life of FCH specimens under random processes. Accuracy and error analyses of the models suggest that New2, incorporating the FCH stress concentration coefficient KT and intensity function f(ξ), exhibits superior accuracy and stability.

Building Test Batteries Based on Analyzing Random Number Generator Tests within the Framework of Algorithmic Information Theory.

The problem of testing random number generators is considered and a new method for comparing the power of different statistical tests is proposed. It is based on the definitions of random sequence developed in the framework of algorithmic information theory and allows comparing the power of different tests in some cases when the available methods of mathematical statistics do not distinguish between tests. In particular, it is shown that tests based on data compression methods using dictionaries should be included in test batteries.

Pengaruh Model Process Oriented Guided Inquiry Learning terhadap Keterampilan Berpikir Kritis Siswa dalam Pembelajaran Geografi di SMA Negeri 1 Singaraja

The study’s objectives were to: (1) describe how the Process Oriented Guided Inquiry Learning (POGIL) model was utilized in teaching geography to students at SMA Negeri 1 Singaraja, (2) analyze the variation in students’ critical thinking abilities before and after POGIL’s implementation in their geography studies, (3) analyze the impact of the POGIL onenhancing students' critical thinking in geography learning. Employing a quasi-experimental design, subjects were divided into experimental and control groups through random selection post-equality testing. ata collection involved observations, document reviews, and test. Analysis included qualitative descriptions and inferential statistics using t-tests. The results of the research is: (1) effective execution of POGIL in geography education by the teacher, (2) a boost in the experimental group students’ critical thinking compared to the control group, (3the study revealed significant disparities in students’ critical thinking skills when comparing geography education utilizing the POGIL model to traditional teaching methods. This shows that the Process Oriented Guided Inquiry Learning model has a significant effect on improving students' critical thinking skills in geography learning.

Randomized compression of rank-structured matrices accelerated with graph coloring

A randomized algorithm for computing a data sparse representation of a given rank-structured matrix A (a.k.a. an H-matrix) is presented. The algorithm draws on the randomized singular value decomposition (RSVD), and operates under the assumption that methods for rapidly applying A and A∗ to vectors are available. The algorithm uses graph coloring algorithms to analyze the hierarchical tree that defines the rank structure to generate a tailored probability distribution from which to draw the random test matrices. The matrix is then applied to the test matrices, and in a final step the matrix itself is reconstructed by the observed input–output pairs. The method presented is an evolution of the “peeling algorithm” of Lin et al. (2011). For the case of uniform trees, the new method substantially reduces the pre-factor of the original peeling algorithm. More significantly, the new technique leads to dramatic acceleration for many non-uniform trees since it constructs test matrices that are optimized for a given tree. The algorithm is particularly effective for kernel matrices involving a set of points restricted to a lower dimensional object than the ambient space, such as a boundary integral equation defined on a surface in three dimensions.

Exploring miRNAs' Based Modeling Approach for Predicting PIRA in Multiple Sclerosis: A Comprehensive Analysis.

The current hypothesis on the pathophysiology of multiple sclerosis (MS) suggests the involvement of both inflammatory and neurodegenerative mechanisms. Disease Modifying Therapies (DMTs) effectively decrease relapse rates, thus reducing relapse-associated disability in people with MS. In some patients, disability progression, however, is not solely linked to new lesions and clinical relapses but can manifest independently. Progression Independent of Relapse Activity (PIRA) significantly contributes to long-term disability, stressing the urge to unveil biomarkers to forecast disease progression. Twenty-five adult patients with relapsing-remitting multiple sclerosis (RRMS) were enrolled in a cohort study, according to the latest McDonald criteria, and tested before and after high-efficacy Disease Modifying Therapies (DMTs) (6-24 months). Through Agilent microarrays, we analyzed miRNA profiles from peripheral blood mononuclear cells. Multivariate logistic and linear models with interactions were generated. Robustness was assessed by randomization tests in R. A subset of miRNAs, correlated with PIRA, and the Expanded Disability Status Scale (EDSS), was selected. To refine the patient stratification connected to the disease trajectory, we computed a robust logistic classification model derived from baseline miRNA expression to predict PIRA status (AUC = 0.971). We built an optimal multilinear model by selecting four other miRNA predictors to describe EDSS changes compared to baseline. Multivariate modeling offers a promising avenue to uncover potential biomarkers essential for accurate prediction of disability progression in early MS stages. These models can provide valuable insights into developing personalized and effective treatment strategies.

A comprehensive study of the novel 4D hyperchaotic system with self-exited multistability and application in the voice encryption

This paper describes a novel 4-D hyperchaotic system with a high level of complexity. It can produce chaotic, hyperchaotic, periodic, and quasi-periodic behaviors by adjusting its parameters. The study showed that the new system experienced the famous dynamical property of multistability. It can exhibit different coexisting attractors for the same parameter values. Furthermore, by using Lyapunov exponents, bifurcation diagram, equilibrium points’ stability, dissipativity, and phase plots, the study was able to investigate the dynamical features of the proposed system. The mathematical model’s feasibility is proved by applying the corresponding electronic circuit using Multisim software. The study also reveals an interesting and special feature of the system’s offset boosting control. Therefore, the new 4D system is very desirable to use in Chaos-based applications due to its hyperchaotic behavior, multistability, offset boosting property, and easily implementable electronic circuit. Then, the study presents a voice encryption scheme that employs the characteristics of the proposed hyperchaotic system to encrypt a voice signal. The new encryption system is implemented on MATLAB (R2023) to simulate the research findings. Numerous tests are used to measure the efficiency of the developed encryption system against attacks, such as histogram analysis, percent residual deviation (PRD), signal-to-noise ratio (SNR), correlation coefficient (cc), key sensitivity, and NIST randomness test. The simulation findings show how effective our proposed encryption system is and how resilient it is to different cryptographic assaults.

On-chip source-device-independent quantum random number generator

Quantum resources offer intrinsic randomness that is valuable for applications such as cryptography, scientific simulation, and computing. Silicon-based photonics chips present an excellent platform for the cost-effective deployment of next-generation quantum systems on a large scale, even at room temperature. Nevertheless, the potential susceptibility of these chips to hacker control poses a challenge in ensuring security for on-chip quantum random number generation, which is crucial for enabling extensive utilization of quantum resources. Here, we introduce and implement an on-chip source-device-independent quantum random number generator (SDI-QRNG). The randomness of this generator is achieved through distortion-free on-chip detection of quantum resources, effectively eliminating classical noise interference. The security of the system is ensured by employing on-chip criteria for estimating security entropy in a practical chip environment. By incorporating a photoelectric package, the SDI-QRNG chip achieves a secure bit rate of 146.2 Mbps and a bare chip rate of 248.47 Gbps, with all extracted secure bits successfully passing the randomness test. Our experimental demonstration of this chip-level SDI-QRNG shows significant advantages in practical applications, paving the way for the widespread and cost-effective implementation of room-temperature secure QRNG, which marks a milestone in the field of QRNG chips.

Radiation Feature Fusion Dual-Attention Cloud Segmentation Network

In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study proposes an innovative solution by integrating radiative feature analysis with cutting-edge deep learning technologies, developing a refined cloud segmentation method. The core innovation lies in the development of FFASPPDANet (Feature Fusion Atrous Spatial Pyramid Pooling Dual Attention Network), a feature fusion dual attention network improved through atrous spatial convolution pooling to enhance the model’s ability to recognize cloud features. Moreover, we introduce a probabilistic thresholding method based on pixel radiation spectrum fusion, further improving the accuracy and reliability of cloud segmentation, resulting in the “FFASPPDANet+” algorithm. Experimental validation shows that FFASPPDANet+ performs exceptionally well in various complex scenarios, achieving a 99.27% accuracy rate in water bodies, a 96.79% accuracy rate in complex urban settings, and a 95.82% accuracy rate in a random test set. This research not only enhances the efficiency and accuracy of cloud segmentation in high-resolution remote sensing images but also provides a new direction and application example for the integration of deep learning with radiative algorithms.

Fatigue Analysis of Composite Bolted Joints under Random and Constant Amplitude Fatigue Loadings.

This paper attempts to analyze the random fatigue life and failure modes of joints using two calculation methods. Three kinds of tests were carried out, which were the static test, constant amplitude fatigue test and the random fatigue test, and four kinds of joints were designed. After the static test, the joint was subjected to a constant amplitude fatigue test by selecting different percentages of load according to the static strength. In order to predict the random fatigue life more precisely, two calculation methods were carried out, which were the linear cumulative damage method and the equivalent loading finite element method. Based on the linear cumulative damage hypothesis, the fatigue life of the joint was established as a function of the load amplitude, and then, the random life prediction was calculated by the amplitude distribution of the random loading. Another method was the equivalent loading method, which was to obtain the equivalent constant amplitude fatigue loading of the random loading spectrum. The finite element model was established based on the stiffness and strength degradation rule. The equivalent random life and fatigue failure modes of the joint were modeled. The two life prediction methods show good agreement with the fatigue experimental result, and all prediction results were included in a scatter band of the factor of 2.

Dynamic Key Generation Using GWO for IoT System

One well-known technological advancement that significantly impacts many things is the Internet of Things (IoT). These include connectivity, work, healthcare, and the economy. IoT can improve life in many situations, including classrooms and smart cities, through work automation, increased output, and decreased worry. However, cyberattacks and other risks significantly impact intelligent Internet of Things applications. Key generation is essential in information security and the various applications that use a distributed system, networks, or Internet of Things (IoT) systems. Several algorithms have been developed to protect IoT applications from malicious attacks; since IoT devices usually have small memory resources and limited computing and power resources, traditional key generation methods are inappropriate because they require high computational power and memory usage. This paper proposes a method of Dynamic Key Generation Method (DKGM) to overcome the difficulty using a specific chaotic map called the Zaslavskii Map and a swarm intelligent algorithm for optimization called Grey Wolf Optimizer (GWO). DKGM's ability to generate several groups-seed numbers using the Zaslavskii map depends on various initial parameters. GWO selects strong generated numbers depending on the randomness test as a fitness function. Three wolfs GWα, GWβ, and GWΩ, are used to simulate the behavior of a pack of grey wolves when attacking prey. The speed and position of each wolf are updated depending on the best three wolves. Finally, use the sets GWα in the round, GWβ in the subkey, and GWΩ in shifting operations of the Chacha20 hash function. The dynamic procedure was used to improve the high-security analysis of the DKGM approach over earlier methods. Simulations show that the suggested method is preferable for IoT applications.

Ethyl acetate in e-liquids: Implications for breath testing.

Electronic cigarette liquids (e-liquids) can contain a variety of chemicals to impart flavors, smells and pharmacological effects. Surveillance studies have identified hundreds of chemicals used in e-liquids that have known health and safety implications. Ethyl acetate has been identified as a common constituent of e-liquids. Ethyl acetate is rapidly hydrolyzed to ethanol in vivo. Animal studies have demonstrated that inhaling >2,000 mg/L ethyl acetate can lead to the accumulation of ethanol in the blood at concentrations >1,000 mg/L, or 0.10%. A "Heisenberg" e-liquid was submitted to the Laboratory for Forensic Toxicology Research for analysis after a random workplace drug test resulted in a breath test result of 0.019% for a safety-sensitive position employee. Analysis of this sample resulted in the detection of 1,488 ± 6 mg/L ethyl acetate. The evaluation of purchased "Heisenberg" e-liquids determined that these products contain ethyl acetate. The identification of ethyl acetate in e-liquids demonstrates poor regulatory oversight and enforcement that potentially has consequences for breath ethanol testing and interpretations. The accumulation of ethanol in the breath from the ingestion/inhalation of ethyl acetate from an e-liquid used prior to a breath test may contribute to the detection of ethanol.

Discussion on the relationship between gut microbiota and glioma through Mendelian randomization test based on the brain gut axis.

In the realm of Gut-Brain axis research, existing evidence points to a complex bidirectional regulatory mechanism between gut microbiota and the brain. However, the question of whether a causal relationship exists between gut microbiota and specific types of brain tumors, such as gliomas, remains unresolved. To address this gap, we employed publicly available Genome-Wide Association Study (GWAS) and MIOBEN databases, conducting an in-depth analysis using Two-Sample Mendelian Randomization (MR). We carried out two sets of MR analyses. The preliminary analysis included fewer instrumental variables due to a high genome-wide statistical significance threshold (5×10-8). To enable a more comprehensive and detailed analysis, we adjusted the significance threshold to 1×10-5. We performed linkage disequilibrium analysis (R2 <0.001, clumping distance = 10,000kb) and detailed screening of palindromic SNPs, followed by MR analysis and validation through sensitivity analysis. Our findings reveal a causal relationship between gut microbiota and gliomas. Further confirmation via Inverse Variance Weighting (IVW) identified eight specific microbial communities related to gliomas. Notably, the Peptostreptococcaceae and Olsenella communities appear to have a protective effect, reducing glioma risk. This study not only confirms the causal link between gut microbiota and gliomas but also suggests a new avenue for future glioma treatment.