Optimal Test Research Articles

A MACHINE LEARNING-BASED ACCELERATED PYROLYSIS CHARACTERIZATION AND OPTIMIZATION OF HIGH-TEMPERATURE COMPOSITES

Manufacturing polymer-based composites for high-temperature applications involves a series of complex steps during which the material undergoes several transformations. These typically include a lay-up step, a curing process, a high-temperature pyrolytic process to convert the resin phase into amorphous carbon, followed by several resin backfill steps, and finally, graphitization to achieve the desired crystalline structure of carbon atoms. The parameters used during the pyrolysis process significantly affect the degradation reactions, the final yield, laminate permeability, and hence the final properties of the material. Typically, extensive testing is required to characterize pyrolysis kinetics and identify optimal processing conditions. This paper addresses these challenges through a novel probabilistic machine-learning (ML)-based approach for the accelerated characterization and optimization of the pyrolysis process, utilizing theory-based transformations of limited experimental data affected by noise and errors. Gaussian Process Regression (GPR), a Bayesian probabilistic approach to regression, is used to determine optimal test parameters to accurately characterize pyrolysis kinetics and achieve the desired yield while satisfying specific constraints. This approach can be used to improve the processing efficiency of high-temperature composites and increase their performance with minimal experimental effort.

Whole genome case-control study of central nervous system toxicity due to antimicrobial drugs.

A genetic predisposition to central nervous system (CNS) toxicity induced by antimicrobial drugs (antibiotics, antivirals, antifungals, and antiparasitic drugs) has been suspected. Whole genome sequencing of 66 cases and 833 controls was performed to investigate whether antimicrobial drug-induced CNS toxicity was associated with genetic variation. The primary objective was to test whether antimicrobial-induced CNS toxicity was associated with seventeen efflux transporters at the blood-brain barrier. In this study, variants or structural elements in efflux transporters were not significantly associated with CNS toxicity. Secondary objectives were to test whether antimicrobial-induced CNS toxicity was associated with genes over the whole genome, with HLA, or with structural genetic variation. Uncommon variants in and close to three genes were significantly associated with CNS toxicity according to a sequence kernel association test combined with an optimal unified test (SKAT-O). These genes were LCP1 (q = 0.013), RETSAT (q = 0.013) and SFMBT2 (q = 0.035). Two variants were driving the LCP1 association: rs6561297 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51-8.46]) and the regulatory variant rs10492451 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51-8.46]). No common genetic variant, HLA-type or structural variation was associated with CNS toxicity. In conclusion, CNS toxicity due to antimicrobial drugs was associated with uncommon variants in LCP1, RETSAT and SFMBT2.

Characterization of FDM 3D Printed Parts Using TPU + PETG Filaments For Shin Guard Products

3D printing machines are used to print products that support sports activities, such as shin guards. During sports, shin guards are protective equipment to prevent injury to the lower legs. Filaments that are suitable for making shin guards are thermoplastic polyurethane (TPU) and polyethylene terephthalate (PETG) because they have impact resistance properties needed to protect the feet during sports. The variation is the level parameter layer height, nozzle temperature, printing speed, and bed temperature. Next, an impact test will be carried out to determine the optimal parameter variation on the 3D printing machine, which is expected to be a reference for printing quality products. This study uses a 3D printer, Ender v3, to print specimens and shin guard products. The material used is TPU+PETG filament. The Taguchi method with the orthogonal matrix L9(3)4 was repeated thrice for each experiment. After that, an analysis of variance was carried out. Parameter variations used in the study were layer height (0.1 mm, 0.2 mm, 0.3 mm), nozzle temperature (220℃, 225℃, 230℃), printing speed (45mm/s, 45mm/s, 50mm/s) and bed temperature. (70℃, 75℃, 80℃). In this study, Charpy impact testing will be carried out. The combination of factors that can produce an optimal impact test is layer height level 2 (0.2 mm), nozzle temperature level 1 (220℃), printing speed level 3 (50 mm/s) and bed temperature level 2 (75℃) with an impact strength value the highest was 27.20 and the lowest was 11.07. The combination of factors that have the most significant effect on the impact test strength values is layer height 63.97%, nozzle temperature 6.19%, printing speed 2.07% and bed temperature 4.74%.

Comparison of efficacy and reliability of six commercial COVID-19 diagnostic PCR kits

Abstract Objectives All countries have been deeply affected by the coronavirus disease 2019 pandemic, both economically and in situations that strain health systems, such as workforce and workload. Therefore, various measures should be taken to control the disease and prevent its spread. Since the disease onset, real-time PCR tests have been used as the gold standard for disease diagnosis. Owing to the rapid progress of the pandemic and the spread of the disease, validation, consistency, and optimization tests of some commercial kits have been conducted directly in the field. Therefore, it is important to compare the results of these kits and improve the existing ones. Methods We compared five kits (Bioexen, Polgen, Coronex, Diagen, and Anatolia) donated to the TOBB Economics and Technology University Hospital PCR laboratory with the KrosGen kit to detect severe acute respiratory syndrome coronavirus 2. A total of 244 samples were selected and analyzed using five different severe acute respiratory syndrome coronavirus 2 PCR detection kits. Results Positive and negative results from the six kits were compared using the working protocols of the kits, primers, and cycle threshold (Ct) values. Five of the six kits have reliable compatibility for Ct<30 but decreases for Ct≥30. Therefore, it is important to evaluate the performance of these kits for reduced viral loads. Conclusions Using a suitable kit with high compatibility for Ct≥30 is important for detecting patients with a low viral load and helping prevent disease spread.

High Aspect Ratio Composite Wings: Geometrically Nonlinear Aeroelasticity, Multi-Disciplinary Design Optimization, Manufacturing, and Experimental Testing

In the field of aerospace engineering, the design and manufacturing of high aspect ratio composite wings has become a focal point of innovation and efficiency. These long, slender wings, constructed with advanced materials such as carbon fiber and employing efficient manufacturing methods such as vacuum bagging, hold the promise of significantly lighter aircraft, reduced fuel consumption, and enhanced overall performance. However, to fully realize these benefits, it is imperative to address a multitude of structural and aeroelastic constraints. This research presents a novel aeroelastically tailored Multi-objective, Multi-disciplinary Design Optimization (MMDO) approach that seamlessly integrates numerical optimization techniques to minimize weight and ensure structural integrity. The optimized wing configuration is then manufactured, and a Ground Vibration Test (GVT) and static deflection analysis using the Digital Image Correlation (DIC) system are used to validate and correlate with the numerical model. Within the fully automated in-house Nonlinear Aeroelastic Simulation Software (NAS2) package (version v1.0), the integration of analytical tools offers a robust numerical approach for enhancing aeroelastic and structural performance in the design of composite wings. Nonlinear aeroelastic analyses and tailoring are included, and a population-based stochastic optimization is used to determine the optimum design within NAS2. These analytical tools contribute to a comprehensive and efficient methodology for designing composite wings with improved aeroelastic and structural characteristics. This comprehensive methodology aims to produce composite wings that not only meet rigorous safety and performance standards but also drive cost-efficiency in the aerospace industry. Through this multidisciplinary approach, the authors seek to underscore the pivotal role of tailoring aeroelastic solutions in the advanced design and manufacturing of high aspect ratio composite wings, thereby contributing to the continued evolution of aerospace technology.

A forecasting model for oil prices using a large set of economic indicators

AbstractThis paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil‐market‐specific variables are important predictors. Our findings also suggest that forecasts of the change in the Brent oil futures prices from the multilayer perceptron that exploits the informational content of all and oil‐market‐specific predictors exhibit higher statistical forecast accuracy than the random walk. Tests of forecast optimality indicate that the forecasts generated using oil‐market‐specific predictors are optimal. We discuss the policymaking and practical relevance of our results.

Implementation Genetic Algorithm for Optimization of Kotlin Software Unit Test Case Generator

Unit testing has a significant role in software development and its impacts depend on the quality of test cases and test data used. To reduce time and effort, unit test generator systems can help automatically generate test cases and test data. However, there is currently no unit test generator for Kotlin programming language even though this language is popularly used for android application developments. In this study, we propose and develop a test generator system that utilizes genetic algorithm (GA) and ANTLR4 parser. GA is used to obtain the most optimal test cases and data for a given Kotlin code. ANTLR4 parser is used to optimize the mutation process in GA so that the mutation process is not totally random. Our model results showed that the average value of code coverage in generated unit tests against instruction coverage is 95.64%, with branch coverage of 76.19% and line coverage of 96.87%. In addition, only two out of eight generated classes produced duplicate test cases with a maximum of one duplication in each class. Therefore, it can be concluded that our optimization with GA on the unit test generator is able to produce unit tests with high code coverage and low duplication.

SEM, XRD and FTIR Analysis of Fly Ash Polymer Composite and Investigate the Micro Structural Changes, Physical, Mechanical, Thermal Conductivity and Wear Behavior at Different Proportions

Industrial waste such as fly ash poses environmental problems, yet it is utilized as an inexpensive and readily available construction material. However, these bricks have low strength. Thus, extensive research aims to increase the strength of these bricks. The current study develops a new systematic procedure to produce fly ash composite bricks with higher compressive strength. Here, fly ash is blended with cold-setting resin at varying proportions and water treated at different temperatures to find a solution for the brick industry. Under optimal test conditions, the obtained fly ash-resin powder bricks have a compressive strength, hardness, water absorption, density and thermal conductivity of 11.24MPa, 47.37HV, 19.09%, 1.68 g/cm3, and 0.055 W/mK respectively. The sliding wear behavior is also examined. X-ray diffraction, FTIR analysis and scanning electron microscopy are used to study the structure-property correlation of these composites.

The impact of supraventricular arrhythmias on the outcomes of guideline-compliant implantable cardioverter defibrillator programming.

Several implantable cardioverter defibrillators (ICD) programming strategies are applied to minimize ICD therapy, especially unnecessary therapies from supraventricular arrhythmias (SVA). However, it remains unknown whether these optimal programming recommendations only benefit those with SVAs or have any detrimental effects from delayed therapy on those without SVAs. This study aims to assess the impact of SVA on the outcomes of ICD programming based on 2015 HRS/EHRA/APHRS/SOLAECE expert consensus statement and 2019 focused update on optimal ICD programming and testing guidelines. Consecutive patients who underwent ICD insertion for primary prevention were classified into four groups based on SVA status and ICD programming: (1) guideline-concordant group (GC) with SVA, (2)GC without SVA, (3) nonguideline concordant group (NGC) with SVA, and (4)NGC without SVA. Cox proportional hazard models were analyzed for freedom from ICD therapies, shock, and mortality. Seven hundred and seventy-two patients (median age, 64 years) were enrolled. ICD therapies were the most frequent in NGC with SVA (24.0%), followed by NGC without SVA (19.9%), GC without SVA (11.6%), and GC with SVA (8.1%). Guideline concordant programming was associated with 68% ICD therapy reduction (HR 0.32, p = .007) and 67% ICD shock reduction (HR 0.33, p = .030) in SVA patients and 44% ICD therapy reduction in those without SVA (HR 0.56, p = .030). Programming ICDs in primary prevention patients based on current guidelines reduces therapy burden without increasing mortality in both SVA and non-SVA patients. A greater magnitude of reduced ICD therapy was found in those with supraventricular arrhythmias.

Development of a Mutation Operator in a Real-Coded Genetic Algorithm for Bridge Model Optimization

A mutation operator in a real-coded genetic algorithm is developed and applied for efficient bridge-model optimization. A mutation operator that changes uniformly or dynamically with a crossover operator is proposed to address optimization problems. The performance of the combined genetic operators was verified using a variety of available test problems based on the convergence and search speed of the global optimal solution. It is shown that the genetic algorithm proposed in this study yields relatively better results than the available algorithms and is more effective in constrained optimization problems. The performance of the proposed genetic algorithm is also verified through a sample study using a field load test for the model optimization of an existing bridge.

DIY google trends indicators in social sciences: A methodological note

Various branches of the social sciences often require high-frequency measures of collective behavior. Examining peoples' Google search patterns enables uncovering otherwise hard-to-measure social trends or constructing different leading indicators. Google Trends (GT) data presents a unique and rich dataset that enables a deeper look at the popularity of peoples’ search interests. This paper delves into the main empirical approaches of constructing GT indicators, conducts a thorough methodological review, and elucidates the nuances and potentials of GT data. Specifically, the paper furnishes a step-by-step methodological guide for constructing GT indicators: from data pre-treatment to composite estimation using an array of machine learning techniques (principal component analysis, XGBoost, and dynamic factor models). We scrutinize the prevalent construction strategies of GT indicators, compare the use of keyword-based vs. category-based estimations, single indicators vs. composite indicators, and evaluate the significance of various data transformations. Illustrating the utility of our methodological framework through a case study, we create GT indicators for US retail trade and examine their forecasting accuracy in comparison to a benchmark autoregressive model. Our findings reveal that XGBoost yields the optimal test sample fit among composite indicators, while category-specific indicators like Shopping and Retail Trade exhibit worse predictive accuracy. As an additional contribution, we provide a detailed and user-friendly R code for replicating our analysis.

Optimal environmental testing frequency for outbreak surveillance

Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection vs. sampling, we derived equations for the expected combined cost per unit time of disease burden and surveillance for a specified sampling frequency, and thus the sampling frequency for which the expected total cost per unit time is lowest.

Optimal test case generation for boundary value analysis

Boundary value analysis (BVA) is a common technique in software testing that uses input values that lie at the boundaries where significant changes in behavior are expected. This approach is widely recognized and used as a natural and effective strategy for testing software. Test coverage is one of the criteria to measure how much the software execution paths are covered by the set of test cases. This paper focuses on evaluating test coverage with respect to BVA by defining a metric called boundary coverage distance (BCD). The BCD metric measures the extent to which a test set covers the boundaries. In addition, based on BCD, we consider the optimal test input generation to minimize BCD under the random testing scheme. We propose three algorithms, each representing a different test input generation strategy, and evaluate their fault detection capabilities through experimental validation. The results indicate that the BCD-based approach has the potential to generate boundary values and improve the effectiveness of software testing.

Urine-Based Biomarker Test Uromonitor® in the Detection and Disease Monitoring of Non-Muscle-Invasive Bladder Cancer-A Systematic Review and Meta-Analysis of Diagnostic Test Performance.

Optimal urine-based diagnostic tests (UBDT) minimize unnecessary follow-up cystoscopies in patients with non-muscle-invasive bladder-cancer (NMIBC), while accurately detecting high-grade bladder-cancer without false-negative results. Such UBDTs have not been comprehensively described upon a broad, validated dataset, resulting in cautious guideline recommendations. Uromonitor®, a urine-based DNA-assay detecting hotspot alterations in TERT, FGFR3, and KRAS, shows promising initial results. However, a systematic review merging all available data is lacking. Studies investigating the diagnostic performance of Uromonitor® in NMIBC until November 2023 were identified in PubMed, Embase, Web-of-Science, Cochrane, Scopus, and medRxiv databases. Within aggregated analyses, test performance and area under the curve/AUC were calculated. This project fully implemented the PRISMA statement. Four qualifying studies comprised a total of 1190 urinary tests (bladder-cancer prevalence: 14.9%). Based on comprehensive analyses, sensitivity, specificity, positive-predictive value/PPV, negative-predictive value/NPV, and test accuracy of Uromonitor® were 80.2%, 96.9%, 82.1%, 96.6%, and 94.5%, respectively, with an AUC of 0.886 (95%-CI: 0.851-0.921). In a meta-analysis of two studies comparing test performance with urinary cytology, Uromonitor® significantly outperformed urinary cytology in sensitivity, PPV, and test accuracy, while no significant differences were observed for specificity and NPV. This systematic review supports the use of Uromonitor® considering its favorable diagnostic performance. In a cohort of 1000 patients with a bladder-cancer prevalence of ~15%, this UBDT would avert 825 unnecessary cystoscopies (true-negatives) while missing 30 bladder-cancer cases (false-negatives). Due to currently limited aggregated data from only four studies with heterogeneous quality, confirmatory studies are needed.

Multi-objective design method for high-power microwave source devices based on preferred PIC/gNSGA-II

To solve the challenging problem of multi-objective optimization for high-power microwave sources, a specialized preference-based multi-objective optimization method, namely PIC/gNSGA-II, is proposed. Additionally, to achieve the quantitative optimization of the frequency spectrum and power of high-power microwave source devices, objective functions for both the spectrum and power are designed. PIC/gNSGA-II is employed to carry out structural optimization tests on relativistic backward wave oscillators (RBWO). The optimized results produced novel device structures that meet the specified frequency bands: in the X-band, the average output power is 845.85 MW, and the operating frequency is 9.53 GHz; in the Ku-band, the average output power is 167.25 MW, and the operating frequency is 14.73 GHz. This demonstrates that by controlling the parameters of such objective functions, the code can simultaneously optimize the spectrum and power of RBWOs. Comparing the optimization results of PIC/gNSGA-II with those of NSGA-II, PIC/gNSGA-II demonstrates better optimization efficiency and the ability to design device structures that meet various performance criteria.

Design, optimization and prototype testing of a double-stack MRPC detector for positron emission tomography (PET)

In this work, the performance of an RPC detector with a 70%Ar-30%CO2 gas mixture for a PET imaging system is investigated. A detailed GEANT4 simulation was performed to improve the detection efficiency by optimizing the thickness and material of the resistive components, the number of gas gaps and selecting an appropriate design and structure of the MRPC detectors. In previous studies, for the detection of gammas by MRPC detectors, a high atomic number (Z) converter such as Pb was suggested. However, in this study, by simulating some commercial glasses for MRPC detectors, it was found that, with a suitable design and using glasses with low-Z materials, without any separate converter, similar detection efficiencies and better spatial resolution are accessible. To achieve similar detection efficiency, the low-Z glasses, require a higher number of gas gaps. This complexity in fabrication of a real detector may be overcome in the future by using cost-effective 3D printing methods. With the proposed design, all double-stack structures have lower detection efficiency for low energy gammas, which is an advantage for rejecting Compton gammas. To verify the simulation results, a 5-layer double-stack MRPC detector was constructed and tested. The experimental detection efficiency of the proposed detector extracted by the coincidence method is 1.46%, which, with an error of less than 3.3%, is in good agreement with the simulation (1.51%). The measured count rate is around 17% larger than the ones estimated by the coincidence method, which is attributed to some spurious pulses generated in the RPC detector.

Optimal precedence tests under single and double-sampling framework

The precedence test is a nonparametric, two-sample test for stochastic comparison of lifetime data. The power of the precedence test increases with increasing sample size. However, the power curve of the precedence test follows a concave pattern that says the rate of increase in power decreases with increasing sample size. In this article, we intend to find the optimal sample size for the precedence test under the single and double-sampling frameworks. A genetic algorithm is used to find the optimal sample size.

Locally optimal tests against periodic linear regression in short panels

This paper aims to detect the periodic coefficients of a Linear Regression model with Panel Data. Nonparametric locally and asymptotically optimal tests are proposed for testing the null hypothesis of a traditional linear regression model, against an alternative of a Periodic Coefficient Regression model. The Uniform Local Asymptotic Normality (ULAN) property is established for Periodic Linear Regression (PLR) model, to construct Locally asymptotically optimal tests. The rank-based version of the optimal parametric test is provided in particular cases. The performance of the proposed tests is valid with numerical simulations using the Monte Carlo study. Real data was studied for applying the periodic linear regression model which compared to both traditional regression and random coefficient regression models.

Strategy optimization for breakout games based on improved BFS algorithm and image recognition techniques

This paper develops a Dungeon Game and details the development process of the City Game. The Dungeon Game is an adventure-themed role-playing game in which the player takes on the role of a character he or she creates and engages in a variety of adventurous activities in a set fictional world. It also allows the use of various props to add interest and realism to the game. In the development of this game, the pathfinding algorithm is crucial to the character's movement and behavior, and the previous traditional pathfinding method can no longer meet the needs of the game, so we optimize the original algorithm, adding dynamic programming and backtracking method in the original algorithm to improve the original algorithm's pathfinding logic to make the character pathfinding in the game more reasonable, to improve the game's performance and user experience. performance and user experience. At the same time, this paper tries to incorporate an image recognition technique based on ANN neural network into the game by adding figures as images into the game, to enrich the content of the game and make it have more possibilities, which increases the playability for the players. In the path optimization test, although the response time is slightly inaccurate, the overall response time is gradually decreasing as the optimization algorithms improve. These algorithm improvements have a more significant increase in the efficiency of the game.

First‐principles study on the surface properties and floatability of magnesite tailings and its main gangue

AbstractThe accumulation of magnesite tailings (MT) poses challenges such as resource wastage, land occupation, dust generation, and environmental pollution, thereby jeopardizing both physical and mental health. Urgent attention is required for the proper treatment of this solid waste material. An in‐depth investigation into enhancing the flotation processes of MT is essential. A comprehensive comprehension of the surface properties of MT and its principal gangue minerals assumes paramount importance in facilitating the desilication and decalcification of MT via flotation. In this investigation, a first‐principles study grounded in density functional theory was employed to scrutinize the surface properties, as well as the similarities and differences in flotability, of four minerals‐quartz, magnesite, dolomite, and calcite. The findings reveal that quartz's primary cleavage plane is (1 0 1), whereas that of magnesite, dolomite, and calcite is (1 0 4). The surfaces of magnesite, dolomite, and calcite exhibit chemical similarities, with Ca atoms demonstrating higher reactivity than Mg atoms. The hydrogen bonding between dodecylamine and quartz emerges as the most robust, while adsorption energies with the three carbonate minerals exhibit minimal disparity. The ongoing focus lies on the selection and optimization tests of decalcification reagents. A moderate quantity of dodecylamine manifests a certain desilication effect. However, excessive dosage compromises selectivity. The first‐principles approach offers guiding significance for elucidating the surface properties of MT and its primary vein minerals, along with investigating the adsorption mechanisms of flotation regents.