Automatic Test Research Articles

Metagenomic Analysis during Co-Digestion Buffalo Sludge and Tomato Pomace Post Thermal Stress: A Case Study

The tomato industry and buffalo farming generate waste, including sludge (BS) and tomato pomace (TP), which can significantly impact their economic and environmental sustainability. The case study tracked changes in microflora composition after a thermal shock during anaerobic co-digestion. The inoculum-to-substrate ratio was 0.5 based on volatile solid content under mesophilic conditions. An Automatic Methane Potential Test System was used to monitor the process before and after thermal stress (50°C) occurred for three days. Next-generation sequencing analyzed the bacterial and archaeal communities. The pH decreased, and methane production plateaued due to the high volatile solid content (87 g/L). After thermal stress, the pH returned to neutral, and the batch resumed biogas production. The cumulative CH4 production reached 3,115 Nml. The biogas had a maximum methane peak of 78.5% compared to 58.4% in BS. The taxonomic classification showed that Firmicutes (51.7%) and Bacteroidetes (29.9%) represented 81.6% of the total OTUs among the bacteria. Fonticella, the most abundant Clostridiaceae (average 4.3%), was absent in BS and increased (up to 17.1%) in TP during methane production. Methanocorpusculum was the most abundant in the archaeal community. However, Metanosarcina showed a stronger correlation with methane production. Brief thermal stress significantly altered bacterial and archaeal populations and allowed to resume biogas production.

Parameter identification and uncertainty evaluation in quasi-dynamic test of solar thermal collectors with Monte Carlo method

This work presents a comparison between two methods used for parameter identification and calculation of parameter uncertainty applied to the measured data of solar thermal collectors when tested according to ISO 9806:2017. One method is using a weighted least square (WLS) fit and the partial derivative approach described in the GUM (the Guide to the Expression of Uncertainty in Measurement). The second is using the Monte Carlo (MC) method, also described in GUM. Uncertainty evaluation by Monte Carlo method is based on a probabilistic approach and is an alternative way for identification of parameters and determination of the uncertainties. In this work the results were obtained according to Quasi-Dynamic Test (QDT) method for a flat plate collector and an evacuated tubular collector. The least squares (LS) method and a nonlinear regression method (MPFit) are used in the identification of parameters for each iteration of the MC method. For the implemented MC method computation times are also discussed. One disadvantage of the MC method is the computation time which depends on the number of samples in the experimental test quantity files, however with this study we think that the advantages of the MC method outweigh the disadvantages, and it is useful even as a complementary tool in QDT testing of collectors.

Automatic Test Pattern Generation for Robust Quantum Circuit Testing

Quantum circuit testing is essential for detecting potential faults in realistic quantum devices, while the testing process itself also suffers from the inexactness and unreliability of quantum operations. This article alleviates the issue by proposing a novel framework of automatic test pattern generation (ATPG) for robust testing of logical quantum circuits. We introduce the stabilizer projector decomposition (SPD) for representing the quantum test pattern and construct the test application (i.e., state preparation and measurement) using Clifford-only circuits, which are rather robust and efficient as evidenced in the fault-tolerant quantum computation. However, it is generally hard to generate SPDs due to the exponentially growing number of the stabilizer projectors. To circumvent this difficulty, we develop an SPD generation algorithm, as well as several acceleration techniques that can exploit both locality and sparsity in generating SPDs. The effectiveness of our algorithms are validated by (1) theoretical guarantees under reasonable conditions and (2) experimental results on commonly used benchmark circuits, such as Quantum Fourier Transform (QFT), Quantum Volume (QV), and Bernstein-Vazirani (BV) in IBM Qiskit.

Red-Emitting Coumarin-Derived fluorescent probe for thiol detection and indirect recognition of β-Lactamase

Biothiols, comprising cysteine (Cys), homocysteine (Hcy), and glutathione (GSH), play a pivotal role in maintaining the delicate redox balance essential for life processes, intricately intertwined with the well-being of living organisms. Deviations in their concentrations can precipitate a range of diseases. Herein, we presented a novel fluorescent probe, DIcou-DNBS, derived from coumarin, which possessed red emission and a substantial Stokes shift (122 nm), tailored for biothiol detection. DIcou-DNBS demonstrated rapid-responsiveness (6 min) towards Cys, Hcy and GSH with detection limits of 0.015 µM, 0.032 µM and 0.059 µM, respectively. Furthermore, its excellent biocompatibility enabled successful applications in biothiol fluorescence imaging of living cells and zebrafish. Beyond biothiol sensing, DIcou-DNBS leveraged its unique property to indirectly detect β-lactamases, making it a valuable tool in inhibitor screening and susceptibility testing of both sensitive (Staphylococcus aureus ATCC 29213) and resistant (Enterobacter cloacae ATCC 13047) bacterial strains. This assay system anticipates to furnish a potent instrument for visualization studies across biological and medical domains.

A Wireless Ad Hoc Network Communication Platform and Data Transmission Strategies for Multi-Bus Instruments

As automatic test technology advances, the number of programmable instruments in a single test system increases. Traditional wired communication methods have a limited range and involve complex cable layouts. Single-function wireless converters provide a viable alternative, but they have limitations. These include complicated configuration, issues with multi-system collaboration, and data blocking. This paper proposes a wireless ad hoc network platform for multi-bus instruments based on a low-cost ESP-12H WiFi module. The platform supports GPIB, RS232, RS485, and CAN bus interface instrument access. It features easy configuration, ad hoc networking, and self-repairing capabilities. A relay multi-hop network with a tree topology expands capacity and coverage. Additionally, a dynamic window-receiving mode and an improved multi-priority queue ensure data transmission integrity. The experimental results show that the platform’s networking time is less than 10 s, and the coverage range reaches 50 m in complex indoor environments. It also shows good stability when running for a long time. However, due to hardware and software design limitations, the actual upload speeds fall short of the theoretical values. For example, RS232 and RS485 are about 10% slower than the theoretical values, and GPIB is about 80% slower. Further optimization is required in the future.

Reporter Mice for Gene Editing: A Key Tool for Advancing Gene Therapy of Rare Diseases.

Most rare diseases are caused by mutations and can have devastating consequences. Precise gene editing by CRISPR/Cas is an exciting possibility for helping these patients, if no irreversible developmental defects have occurred. To optimize gene editing therapy, reporter mice for gene editing have been generated which, by expression of reporter genes, indicate the efficiency of precise and imprecise gene editing. These mice are important tools for testing and comparing novel gene editing methodologies. This review provides a comprehensive overview of reporter mice for gene editing which all have been used for monitoring CRISPR/Cas-mediated gene editing involving DNA double-strand breaks (DSBs). Furthermore, we discuss how reporter mice can be used for quickly checking genetic alterations by base editing (BE) or prime editing (PE).

Smartphone-enabled mesoporous silica nanotube chemosensors for quick and selective mercury detection in water and cosmetics

The pervasive presence of mercury in water and cosmetics poses significant health risks, necessitating the development of a method for the in-situ monitoring and extraction of mercury ions. This study introduces a novel approach utilizing Mesoporous Silica Nanotubes (MSNTs) with a unique worm-like structure, providing an expansive surface area ideal for the adsorption of a Hg2+ ion chromophore, N,N,N,N′-Tetramethyl-4,4′-diaminobenzophenone. This configuration enables rapid and visible detection of toxic mercury, with a color transition from yellow to green that is easily discernible by the naked eye. The sensitivity of the Mercury nano-sensor (MNS) is remarkably high, with a detection limit of 1.9 × 10-8 M as determined by digital image analysis, and 4.9 × 10-8 M via spectrophotometric methods—both well below the WHO guidelines for drinking water. The MNS’s low detection threshold, coupled with its reusability after simple regeneration, positions it as an effective tool for preliminary water testing. The findings suggest that the MNS, requiring only 10 mg for measurements, offers a promising solution for the real-time visualization of mercury ions, enhancing safety measures in water and cosmetic products.

Reliability analysis of 50000 kN hydraulic support test bench under shrinkage test conditions

The large mining-height hydraulic support test-bed serves as a crucial tool for heavy hydraulic support factory inspections and type testing. With the successful development of the ZY29000/45/100D hydraulic support, the need for a large high-mining hydraulic support test-bed has become evident. This paper combines the theory of rigid-flexible coupling in virtual prototypes and ADAMS multi-body dynamic simulation technology to analyze the strength of the 50,000 kN hydraulic support test-bed. ANSYS classic modules were employed to address the flexibility of key components within the test-bed. The fatigue life of the test-bed was analyzed using NSOFT software, the von Mises yield criterion, and the Goodman modified curve. The S-N curve of material parameters for the test-bed components was used to conduct a shrinkage test. The results indicate that the lowest position is the most vulnerable during the shrinkage test. Among the key components, the stress on the pin bearing is 356.6 MPa, which is 167.9 %, 123.3 %, and 41.6 % higher than that of the pillar, movable beam, and base, respectively. The fatigue life of the base under low, medium, and high shrinkage conditions is1.1×107, 4.0×108, and 1.7×108, with damage values of 8.8×10-8, 2.5×10-9, and 5.7×10-9, respectively. The base’s life under low shrinkage conditions is the shortest, followed by that under high shrinkage conditions. These research findings provide technical guidance and an optimization foundation for the successful development of a 50000 kN hydraulic support test-bed. They also offer a new method and approach for analyzing and predicting the fatigue life of key components in large-scale industrial and mining equipment operating under complex conditions, with promising practical applications.

Establishing and Characterizing the Molecular Profiles, Cellular Features, and Clinical Utility of a Patient-Derived Xenograft Model Using Benign Prostatic Tissues

Benign prostatic hyperplasia (BPH) is a common condition marked by the enlargement of the prostate gland, which often leads to significant urinary symptoms and a decreased quality of life. The development of clinically relevant animal models is crucial for understanding the pathophysiology of BPH and improving treatment options. This study aims to establish a patient-derived xenograft (PDX) model using benign prostatic tissues to explore the molecular and cellular mechanisms of BPH. PDXs were generated by implanting fresh BPH (transition zone) and paired normal (peripheral zone) prostate tissue from 8 patients under the renal capsule of immunodeficient male mice. Tissue weight, architecture, cellular proliferation, apoptosis, prostate-specific marker expression, and molecular profiles of PDXs were assessed after 1 week and 1, 2, or 3 months of implantation by immunohistochemistry, enzyme-linked immunosorbent assay, transcriptomics, and proteomics. Responses to finasteride, a standard-of-care therapy, were evaluated. PDXs maintained histologic and molecular characteristics of the parental human tissues. BPH, but not normal PDXs, demonstrated significant increases in weight and cellular proliferation, particularly at 1 month. Molecular profiling revealed specific gene and protein expression patterns correlating with BPH pathophysiology. Specifically, an increased immune and stress response was observed at 1 week, followed by increased expression of proliferation markers and BPH-specific stromal signaling molecules, such as BMP5 and CXCL13, at 1 month. Graft stabilization to preimplant characteristics was apparent between 2 and 3 months. Treatment with finasteride reduced proliferation, increased apoptosis, and induced morphologic changes consistent with therapeutic responses observed in human BPH. Our PDX model recapitulates the morphologic, histologic, and molecular features of human BPH, offering a significant advancement in modeling the complex interactions of cell types in BPH microenvironments. These PDXs respond to therapeutic intervention as expected, providing a valuable tool for preclinical testing of new therapeutics that will improve the well-being of BPH patients.

2309: Automation of the couch sag Quality Control test for Accuray Radixact

2309: Automation of the couch sag Quality Control test for Accuray Radixact

1998: Automation of Dose Stability Test in Helical Irradiations on Tomotherapy linear accelerators

1998: Automation of Dose Stability Test in Helical Irradiations on Tomotherapy linear accelerators

Test generation algorithm for QCA circuits targeting novel defects and its corresponding fault models

Considering the scaling limitations of current Complementary Metal Oxide Semiconductor (CMOS) technology, Quantum-dot-Cellular Automata (QCA) is emerging as one of the alternatives. QCA being at the molecular scale, defects are more likely to occur in it. Therefore, substantial development of QCA-oriented defects, its corresponding fault models and test generation is required. In this paper, a test generation algorithm for a QCA combinational circuit is proposed. The FAN (A Fanout Oriented) test generation algorithm is extended for QCA. The proposed Automatic Test Pattern Generator (ATPG) for QCA targets Single Stuck at Fault (SSF) set produced by novel Multiple Missing Cells (MMC) defects. The proposed ATPG is based on the QCA-oriented test generation properties and guided by proposed testability measures.The MCNC benchmark circuits are synthesized into QCA using proposed synthesis algorithms to check the effectiveness of the proposed ATPG. The ATPG is developed using C++ and tested on MCNC benchmark circuits. Further, ATPG-generated test vectors are validated at the QCA device level to demonstrate their correctness. The QCADesigner-E tool is used for the device-level implementation of the MCNC benchmark circuit.

Methods for recognizing surface defects on thin-sheet materials for visual testing automation (Review)

Methods for recognizing surface defects on thin-sheet materials for visual testing automation (Review)

Ray-tracing versus Born approximation in full-sky weak lensing simulations of the MillenniumTNG project

ABSTRACT Weak gravitational lensing is a powerful tool for precision tests of cosmology. As the expected deflection angles are small, predictions based on non-linear N-body simulations are commonly computed with the Born approximation. Here, we examine this assumption using DORIAN, a newly developed full-sky ray-tracing scheme applied to high-resolution mass-shell outputs of the two largest simulations in the MillenniumTNG suite, each with a 3000 Mpc box containing almost 1.1 trillion cold dark matter particles in addition to 16.7 billion particles representing massive neutrinos. We examine simple two-point statistics like the angular power spectrum of the convergence field, as well as statistics sensitive to higher order correlations such as peak and minimum statistics, void statistics, and Minkowski functionals of the convergence maps. Overall, we find only small differences between the Born approximation and a full ray-tracing treatment. While these are negligibly small at power-spectrum level, some higher order statistics show more sizeable effects; ray-tracing is necessary to achieve per cent level precision. At the resolution reached here, full-sky maps with 0.8 billion pixels and an angular resolution of 0.43 arcmin, we find that interpolation accuracy can introduce appreciable errors in ray-tracing results. We therefore implemented an interpolation method based on non-uniform fast Fourier transforms (NUFFT) along with more traditional methods. Bilinear interpolation introduces significant smoothing, while nearest grid point sampling agrees well with NUFFT, at least for our fiducial source redshift, $z_s=1.0$, and for the 1 arcmin smoothing we use for higher order statistics.

Measuring Financial Well-Being in Generation Z in Indonesia

Financial well-being is one of the elements of wellness that everyone aspires to pursuit the quality in their life. This topic has also been raised in several countries with various research subjects. However, in Indonesia only a few studies have examined financial well-being. This research aims to examine the influence of financial knowledge, financial behaviour, financial stress and financial socialization on the financial well-being of generation Z in Indonesia. By using non-purposive sampling technique, 461 respondents who live in Indonesia were collected. All respondents completed an online survey containing valid and reliable instruments. Data were analysed using SEM technique with Smart-PLS 4.0 as a tool for hypothesis testing. The results showed that financial knowledge and financial behaviour have no effect on financial well-being, while financial stress and financial socialization have a positive and significant effect on financial well-being.

Augmented testing to support manual GUI-based regression testing: An empirical study

ContextManual graphical user interface (GUI) software testing presents a substantial part of the overall practiced testing efforts, despite various research efforts to further increase test automation. Augmented Testing (AT), a novel approach for GUI testing, aims to aid manual GUI-based testing through a tool-supported approach where an intermediary visual layer is rendered between the system under test (SUT) and the tester, superimposing relevant test information.ObjectiveThe primary objective of this study is to gather empirical evidence regarding AT’s efficiency compared to manual GUI-based regression testing. Existing studies involving testing approaches under the AT definition primarily focus on exploratory GUI testing, leaving a gap in the context of regression testing. As a secondary objective, we investigate AT’s benefits, drawbacks, and usability issues when deployed with the demonstrator tool, Scout.MethodWe conducted an experiment involving 13 industry professionals, from six companies, comparing AT to manual GUI-based regression testing. These results were complemented by interviews and Bayesian data analysis (BDA) of the study’s quantitative results.ResultsThe results of the Bayesian data analysis revealed that the use of AT shortens test durations in 70% of the cases on average, concluding that AT is more efficient. When comparing the means of the total duration to perform all tests, AT reduced the test duration by 36% in total. Participant interviews highlighted nine benefits and eleven drawbacks of AT, while observations revealed four usability issues.ConclusionThis study presents empirical evidence of improved efficiency using AT in the context of manual GUI-based regression testing. We further report AT’s benefits, drawbacks, and usability issues. The majority of identified usability issues and drawbacks can be attributed to the tool implementation of AT and, thus, can serve as valuable input for future tool development.

Improving Web Element Localization by Using a Large Language Model

ABSTRACTWeb‐based test automation heavily relies on accurately finding web elements. Traditional methods compare attributes but do not grasp the context and meaning of elements and words. The emergence of large language models (LLMs) like GPT‐4, which can show human‐like reasoning abilities on some tasks, offers new opportunities for software engineering and web element localization. This paper introduces and evaluates VON Similo LLM, an enhanced web element localization approach. Using an LLM, it selects the most likely web element from the top‐ranked ones identified by the existing VON Similo method, ideally aiming to get closer to human‐like selection accuracy. An experimental study was conducted using 804 web element pairs from 48 real‐world web applications. We measured the number of correctly identified elements as well as the execution times, comparing the effectiveness and efficiency of VON Similo LLM against the baseline algorithm. In addition, motivations from the LLM were recorded and analysed for 140 instances. VON Similo LLM demonstrated improved performance, reducing failed localizations from 70 to 40 (out of 804), a 43% reduction. Despite its slower execution time and additional costs of using the GPT‐4 model, the LLM's human‐like reasoning showed promise in enhancing web element localization. LLM technology can enhance web element localization in GUI test automation, reducing false positives and potentially lowering maintenance costs. However, further research is necessary to fully understand LLMs' capabilities, limitations and practical use in GUI testing.

Protocol for contextual co-design of a pre-consultation tool for cascade testing in familial hypercholesterolaemia (FH)

Protocol for contextual co-design of a pre-consultation tool for cascade testing in familial hypercholesterolaemia (FH)

Learning Scatter Artifact Correction in Cone-Beam X-Ray CT Using Incomplete Projections with Beam Hole Array

X-ray cone-beam computed tomography (CBCT) is a powerful tool for nondestructive testing and evaluation, yet the CT image quality can be compromised by artifact due to X-ray scattering within dense materials such as metals. This problem leads to the need for hardware- and software-based scatter artifact correction to enhance the image quality. Recently, deep learning techniques have merged as a promising approach to obtain scatter-free images efficiently. However, these deep learning techniques rely heavily on training data, often gathered through simulation. Simulated CT images, unfortunately, do not accurately reproduce the real properties of objects, and physically accurate X-ray simulation still requires significant computation time, hindering the collection of a large number of CT images. To address these problems, we propose a deep learning framework for scatter artifact correction using projections obtained solely by real CT scanning. To this end, we utilize a beam-hole array (BHA) to block the X-rays deviating from the primary beam path, thereby capturing scatter-free X-ray intensity at certain detector pixels. As the BHA shadows a large portion of detector pixels, we incorporate several regularization losses to enhance the training process. Furthermore, we introduce radiographic data augmentation to mitigate the need for long scanning time, which is a concern as CT devices equipped with BHA require two series of CT scans. Experimental validation showed that the proposed framework outperforms a baseline method that learns simulated projections where the entire image is visible and does not contain scattering artifacts.

Self-Healing Test Automation Framework using AI and ML

Self-Healing Test Automation Framework using AI and ML