Testing Techniques Research Articles

Scale-based screening and assessment of age-related frailty

As the population ages, the prevalence of age-related frailty increases sharply, which increases the risk of poor health status of older adults, such as disability, falls, hospitalization, and death. Across the globe, frailty is moving toward the forefront of health and medical research. Currently, frailty is believed to be preventable and reversible, so the early identification of frailty is critical. However, there are neither precise biomarkers of frailty nor definitive laboratory tests and corresponding clinical testing techniques and equipment in clinical practice. As a result, the clinical identification of frailty is mainly achieved through the widely used frailty scale, which is an objective, simple, time-saving, effective, economical, and feasible measurement tool. In this narrative review, we summarized and analyzed the various existing frailty scales from different perspectives of screening and evaluation, aiming to provide a reference for clinical researchers and practitioners to judge and manage frail older people accurately.

Pensions and Gratuities, Public Debt Servicing and Economic Growth of Nigeria

Government expenditure across the globe is generally intended to enhance citizens’ welfare through the provision of public goods and services and thus, induce growth of economies. Pensions and gratuities as well as public debt servicing are classified under transfer expenditure of government. Thus, this study broadly examined the effects of pensions and gratuities and public debt services expenditure on economic growth of Nigeria for the period 1981-2020. The time series data for the study period were sourced from the Central Bank of Nigeria (CBN) Statistical Bulletin as well as the National Bureau of Statistics (NBS). Real Gross Domestic Product (RGDP) served as the dependent variable while Government expenditure on pensions and gratuities (GEXPGR) and Government expenditure on public debt servicing (GEXPDS) served as independent variables. Inflation rate was introduced in this work as a control variable to ensure the robustness of the study. The Augmented Dickey-Fuller (ADF) unit root test technique was employed to test the stationarity of the variables in the model. The result revealed a mixed order of I(0) and I(1). Thus, the Auto-Regressive Distributed Lag (ARDL) Bounds test was adopted to test the long-run relationship (co-integration) among variables and estimate the long-run and short-run dynamics of the analysis. The findings of the study revealed that government expenditure on pensions and gratuities, and debt servicing had positive and significant effects on the economic growth of Nigeria in the long-run. In the short-run, government expenditure on pensions and gratuities had a positive and significant effect on economic growth of Nigeria while public debt servicing was found to be positive but non-significant. Inflation rate was found to have negative and non-significant effects on the economic growth of Nigeria in both the short and long-run for the period studied. The study therefore recommended that the Nigerian government should sustain and improve budgetary allocations to pensions and gratuities to guarantee relative comfort among pensioners and at the same time, ensure adequate and regular servicing of both domestic and external debts to induce further growth in the economy.

PENGGUNAAN MODEL PROBLEM BASED LEARNING (PBL) DALAM PEMBELAJARAN TENTANG NILAI-NILAI PANCASILA UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK DI KELAS V SD NEGERI NUNUFAFI KABUPATEN TIMOR TENGAH UTARA

This study aims to improve learning outcomes in learning about Pancasila values ​​using the Problem Based Learning (PBL) model in Class V of Nunufafi Elementary School, TTU Regency. The type of research used is classroom action research with stages, namely planning, implementation, observation, testing and reflection by taking the research location at Nunufafi Elementary School, TTU Regency. he subjects of this study were 14 class V students consisting of 10 boys and 4 girls. Data collection was carried out using observation and test techniques, the data that had been collected was processed and analyzed further using qualitative descriptive analysis. Student learning outcomes increased from cycle I to cycle II, namely the learning outcomes of cycle I can be seen that out of 14 students who completed 5 people (35.71%) with sufficient criteria and increased in cycle II, namely 13 people (92.85%) with very good criteria. Based on the results of the research and discussion, it can be concluded that the use of the Problem Based Learning (PBL) model can improve student learning outcomes on the material on the richness of Pancasila values ​​in class I V of Nunufafi Elementary School, TTU Regency.

A Fuzzing Tool Based on Automated Grammar Detection

Software testing is an important step in the software development life cycle to ensure the quality and security of software. Fuzzing is a security testing technique that finds vulnerabilities automatically without accessing the source code. We built a fuzzer, called JIMA-Fuzzing, which is an effective fuzzing tool that utilizes grammar detected from sample input. Based on the detected grammar, JIMA-Fuzzing selects a portion of the valid user input and fuzzes that portion. For example, the tool may greatly increase the size of the input, truncate the input, replace numeric values with new values, replace words with numbers, etc. This paper discusses how JIMA-Fuzzing works and shows the evaluation results after testing against the DARPA Cyber Grand Challenge (CGC) dataset. JIMA-Fuzzing is capable of extracting grammar from sample input files, meaning that it does not require access to the source code to generate effective fuzzing files. This feature allows it to work with proprietary or non-open-source programs and significantly reduces the effort needed from human testers. In addition, compared to fuzzing tools guided with symbolic execution or taint analysis, JIMA-Fuzzing takes much less computing power and time to analyze sample input and generate fuzzing files. However, the limitation is that JIMA-Fuzzing relies on good sample inputs and works primarily on programs that require user interaction/input.

Pengaruh Struktur Kepemilikan, Penerapan Digital Banking, dan Investasi Teknologi Informasi terhadap Kinerja Intellectual Capital: Studi Empiris pada Perusahaan Perbankan yang Terdaftar di BEI Tahun 2017-2021

The study aims to prove the effect of the implementation of digital banking, investment in information technology, and ownership structure such as institutional ownership and foreign ownership on intellectual capital performance. The population in this study was banking sector company in Indonesia Stock Exchange in 2017- 2021. Sample was determined using the purposive sampling method. Data analysis technique for hypothesis testing using multiple regression analysis. The of statistical analysis show that foreign ownership has no significant effect while institutional ownership, the implementation of digital banking, and investment in information technology have positive and significant effect to intellectual capital performance.

The use of artificial intelligence for automatic analysis and reporting of software defects

The COVID-19 pandemic marked a before and after in the business world, causing a growing demand for applications that streamline operations, reduce delivery times and costs, and improve the quality of products. In this context, artificial intelligence (AI) has taken a relevant role in improving these processes, since it incorporates mathematical models that allow analyzing the logical structure of the systems to detect and reduce errors or failures in real-time. This study aimed to determine the most relevant aspects to be considered for detecting software defects using AI. The methodology used was qualitative, with an exploratory, descriptive, and non-experimental approach. The technique involved a documentary review of 79 bibliometric references. The most relevant finding was the use of regression testing techniques and automated log files, in machine learning (ML) and robotic process automation (RPA) environments. These techniques help reduce the time required to identify failures, thereby enhancing efficiency and effectiveness in the lifecycle of applications. In conclusion, companies that incorporate AI algorithms will be able to include an agile model in their lifecycle, as they will reduce the rate of failures, errors, and breakdowns allowing cost savings, and ensuring quality.

PENINGKATAN HASIL BELAJAR SISWA TENTANG SISTEM PEREDARAN DARAH MANUSIA MELALUI MODEL PROBLEM BASED LEARNING DI KELAS V SDI NANO KABUPATEN TTS

This research aims to improve the learning outcomes of class V SDI Nano students through the application of problem-based learning. The research subjects were class V students at SDI Nano with a total of 21 students. The type of research used is two-cycle classroom action research. The testing technique uses test techniques, observation and documentation which are used as data collection techniques. Based on the research results, it can be said that in the first cycle, student learning outcomes reached 28.5% with a class average of 60.7 and a success index of less than 80%. Completeness of student learning outcomes in cycle II was 90.4% with an average of 91.4 and the success index had reached 80%. Thus, it can be concluded that applying the problem based learning model can improve the science learning outcomes of class V SDI Nano students.

Research Progress on Evaluating the Effectiveness and Safety of Transdermal Absorption of Cosmetics

This review systematically summarizes research progress in the evaluation of the effectiveness and safety of the transdermal absorption of cosmetics, focusing on the classification, applicability, and relevant regulations of different evaluation methods. Transdermal absorption refers to the process in which active ingredients enter the epidermis, dermis, and other parts of the skin through the skin barrier and exert their effects. The article first classifies the in vitro and in vivo evaluation methods of transdermal absorption. It also provides a detailed overview of the regulations and standards for in vitro transdermal absorption, compares the standards of different institutions, and highlights the diffusion cell method, developed by scientists at Procter & Gamble, which is the mainstream method for in vitro testing. In addition, the paper summarizes the utilization of modern testing techniques such as high-throughput screening technology, mass spectrometry imaging, Raman spectroscopy, and confocal microscopy in the study of transdermal absorption and introduces the working principles of various instruments and their specific application cases in transdermal absorption studies.

ANALISIS KETERAMPILAN GERAK LAY UP SHOOT DALAM PERMAINAN BOLA BASKET PADA PEMAIN TOGETHER BASKETBALL CLUB PAREPARE

Basketball is a sport that requires mastery of basic techniques, where each player is required to be able to put the ball into the ring and protect his team's ring. This study aims to examine the level of lay up shoot skills in Together Basketball Club Parepare members. The research was conducted with a qualitative descriptive approach through a survey method, using test and measurement techniques. Researchers involved all 22 players from the club as research samples. The measuring instrument used was the lay up shoot skill test, with data analysis using descriptive percentages. The research findings describe the distribution of players' lay up shoot skills as follows: 4.5% (1 player) categorized as “very less” 27.3% (6 players) categorized as “less” 27.3% (6 players) categorized as “medium” 40.9% (9 players) categorized as “good” 0% (0 players) categorized as “excellent”. With an average score of 16.68 points which is in the moderate category, the study concluded that the lay up shoot skills of Together Basketball Club Parepare players are classified in the average or moderate category.

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Solar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance. Diagnosing and resolving faults within these systems promptly is crucial to ensure reliability and efficiency in energy generation. Autoencoders and their variants have gained popularity in recent studies for detecting and diagnosing faults in solar arrays. However, traditional autoencoder models often struggle to capture the spatial and temporal relationships present in photovoltaic sensor data. This paper introduces a deep learning model that combines a graph convolutional network with a variational autoencoder to diagnose faults in solar arrays. The graph convolutional network effectively learns from spatial and temporal sensor data, significantly improving fault detection performance. We evaluated the proposed deep learning model on a recently published solar array dataset for an integrated power probability table mode. The experimental results show that the model achieves a fault detection rate exceeding 95% and outperforms the conventional autoencoder models. We also identified faulty components by analyzing the model’s reconstruction error for each feature, and we validated the analysis through the Kolmogorov–Smirnov test and noise injection techniques.

Study Regarding the Possibility of Using Ultrasonic Pulse Velocity (UPV) on Earthen Construction Evaluation

The determination of drying period, compressive strength, and air-dry density represent crucial parameters for assessing the quality and performance of earthen construction materials. This paper explores the possibilities of using the ultrasonic method as a non-destructive testing technique applied to earthen materials (specimens, elements, or structures) to determine these properties. The method relies on the measurement of ultrasonic pulse velocity (UPV), which is influenced by factors such as density, elasticity, and curing process. By analyzing the propagation of ultrasonic waves through earthen samples, valuable insights can be gained regarding their drying period, compressive strength, and density. The drying period of earthen samples can be determined using the ultrasonic method by monitoring the changes in pulse velocity over time. As the moisture content decreases during the drying process, the velocity of ultrasonic waves increases due to the reduced presence of water. This allows for the estimation of the drying period without the need for time-consuming and destructive testing methods. Compressive strength is also a critical parameter in assessing the structural integrity of earthen materials. The UPV method offers a non-destructive approach to determine the compressive strength of earthen samples. This provides a valuable tool for quality control and assessment of earthen construction materials. Density is another important property that influences their performance and the UPV method can be used to determine the density of earthen materials by measuring the ultrasonic pulse velocity and analyzing its relationship with density. This non-destructive approach allows for quick and efficient estimation of the compactness and quality of earthen mixes. Overall, the ultrasonic method offers a non-destructive and efficient approach in determining the drying period, compressive strength, and density of various soil compositions. By measuring the pulse velocity and analyzing its relationship with these properties, valuable insights can be gained regarding the quality and performance of earthen construction materials. This method has the potential to significantly improve the assessment and quality control processes in earthen construction, leading to more sustainable and reliable structures associated with the earthen techniques.

Advanced Analysis of Concrete Compressive Strength: Combining NDT Techniques with Digital Image Processing

Concrete is a fundamental material in civil engineering, widely used for its durability, versatility, and load-bearing capacity in infrastructure and building projects. The compressive strength of concrete is crucial as it directly impacts structural integrity, longevity, and safety. This study investigates the compressive strength of M50 grade concrete through a comprehensive approach that combines non-destructive and destructive testing techniques. Methods employed include the Rebound Hammer (RBH) test, Ultrasonic Pulse Velocity (UPV) test, Digital Image Processing (DIP), and conventional destructive testing. The integration of these techniques aims to provide accurate and reliable estimates of compressive strength, critical for evaluating concrete quality in structural applications. Specifically, this research compares the non-destructive techniques (RBH, SonReb Method, and DIP) against standard destructive testing results across different curing ages. Findings reveal a strong correlation between DIP and destructive testing, suggesting that image-based processing can serve as an effective non-destructive alternative in compressive strength assessment. The comprehensive analysis of M50 grade concrete presented in this study offers valuable insights for researchers and practitioners seeking enhanced, non-invasive methods for concrete strength evaluation.

Research on Production Decision Modelling Based on Sequential Testing and Minimum Objective Planning

In this paper, the decision-making problem encountered by an enterprise in the production process is studied in depth by using the sequential test and the minimum objective planning method. The study begins with a sampling and testing model for spare parts, simplifies the model based on normal distribution, and applies the sequential test technique to reduce the number of necessary samples and effectively optimize the cost of sampling and testing. Immediately after that, we constructed a minimum objective planning model to model the production process of spare parts and finished products and their related costs in detail. We solved the optimal production cost for each of the six real production scenarios faced by the enterprise. The results show that our model can not only effectively reduce production costs, but also significantly improve decision-making efficiency.

Observing Mixed Chemical Reactions at the Positive Electrode in the High-Performance Self-Powered Electrochemical Humidity Sensor.

Electrochemical humidity (ECH) sensors that integrate power generation and humidity sensing have attracted great research attention in recent years. However, the design of high-performance ECH sensors faces many challenges. Namely, the working mechanism of the ECH sensors is still controversial, and related self-powered applications have not been well implemented. To overcome these limitations, this study constructs an ECH sensor with high power-generation and humidity-sensing performance using the KCl/carbon black/halloysite nanotubes (KCl/CB/HNTs) as a humidity-sensing electrolyte. The proposed ECH sensor has a wide humidity-sensing response of 10.9%-91.5% relative humidity (RH), and a single ECH sensor can output 1.46 V with a maximum power of 133.2 μW at 91.5% RH. Particularly, the unprecedented mixed chemical reactions at the positive electrode, including the hydrogen evolution reaction and oxygen reduction reaction, are analyzed using multiple characterization and testing techniques. The analysis results provide solid experimental evidence for the current controversial working mechanism of ECH sensors. Due to the advantage of high-power generation, the proposed ECH sensor can be used for self-powered humidity detection. This study provides a valuable reference for improving the power generation of ECH sensors and solid evidence for clarifying their working mechanism, which could be beneficial for guiding the future development of ECH sensors.

Technology adoption performance evaluation applied to testing industrial REST APIs

Testing is an important task within software development. To write test cases and integrate them into an automated test suite requires a significant amount of work. Given a set of requirements and specifications of a software, testing is needed to verify its correctness. When done manually, it is an expensive and error prone task. To facilitate such work, automated test-case generation via tools could be useful. Test-case generation can be facilitated by deterministic algorithm-driven approaches or non-deterministic approaches such as with AI (e.g., evolutionary and LLM). The different approaches come with their strengths and weaknesses, which must be considered when integrating these approaches into a product test procedure in industry. Several novel testing techniques and tools have been developed in academia and industry, but how effective they are and how to integrate them in real-world large industrial scenarios is still unclear. In this paper, a systematic approach is presented to evaluate test-case generation methodologies and integrate them into a scalable enterprise setup. The specific context is black-box testing of REST APIs, based on their OpenAPI schemas. The aim is to facilitate IT product development and service delivery. The proposed Technology Adoption Performance Evaluation (TAPE) approach is evaluated by a case study within the Group IT of Volkswagen AG. We evaluated existing tools such as OpenAPI Generator, EvoMaster and StarCoder which are built on different technologies. Our results show that these tools are of benefit for test engineers to facilitate test-case specification and design within the Group IT of Volkswagen AG.

A Review on Risk Factors, Traditional Diagnostic Techniques, and Biomarkers for Pneumonia Prognostication and Management in Diabetic Patients

People of all ages can contract pneumonia, and it can cause mild to severe disease and even death. In addition to being a major cause of death for elderly people and those with prior medical conditions such as diabetes, it isthe world’s biggest infectious cause of death for children. Diabetes mellitus is a metabolic condition with a high glucose level and is a leading cause of lower limb amputation, heart attacks, strokes, blindness, and renal failure. Hyperglycemia is known to impair neutrophil activity, damage antioxidant status, and weaken the humoral immune system. Therefore, diabetic patients are more susceptible to pneumonia than people without diabetes and linked fatalities. The absence of quick, precise, simple, and affordable ways to identify the etiologic agents of community-acquired pneumonia has made diagnostic studies’ usefulness contentious. Improvements in biological markers and molecular testing techniques have significantly increased the ability to diagnose pneumonia and other related respiratory infections. Identifying the risk factors for developing severe pneumonia and early testing in diabetic patients might lead to a significant decrease in the mortality of diabetic patients with pneumonia. In this regard, various risk factors, traditional testing techniques, and pathomechanisms are discussed in this review. Further, biomarkers and next-generation sequencing are briefly summarized. Finding biomarkers with the ability to distinguish between bacterial and viral pneumonia could be crucial because identifying the precise pathogen would stop the unnecessary use of antibiotics and effectively save the patient’s life.

Free–free resonance method for the mechanical characterization of lime-stabilized rammed earth specimens

Rammed earth is a historical construction technique found in a large number of structures spread throughout the world. To conduct thorough and secure studies on these structures, it is necessary to know the mechanical characteristics of the material. Due to the heritage value of many structures built with this technique, testing techniques that may damage or alter them cannot be applied, so the use of non-destructive testing (NDT) is required in such characterization. Among these non-destructive techniques are those based on the propagation of elastic waves in the material, as they are completely harmless to the elements to be characterized. In this paper, the non-destructive technique of free–free resonance (FFR) is applied to specimens of rammed earth stabilized with different lime dosages in order to determine the dynamic modulus of elasticity of the material, marking the first time this technique is applied to specimens of this material. The values obtained with the free–free resonance method are compared with those obtained using another non-destructive test such as ultrasonic pulse velocity (UPV). This test has been used by a large number of researchers, providing reliable results. The percentage of lime influences the parameters obtained due to the effects of the different carbonation velocities of the specimens. It is demonstrated that the FFR method is applicable to rammed earth specimens and is shown to be an effective test for identifying specimens with internal damage. The values obtained during the investigation for both methods range from 1.25 for the samples with a higher amount of lime, with a 3:1 ratio, to 1.26 for the specimens made with a lower amount of lime, with a 4:1 ratio.

Wear Analysis of Polymer Brake Pads Using Non-Destructive Testing Techniques

The research investigates the wear characteristics of polymer and composite brake pads like aramid fibers, potassium titanate, and phenol formaldehyde resin, which are crucial for vehicle safety and performance. Non destructive testing (NDT) techniques, including spectroscopic methods and particle size distribution analysis, are employed to assess brake pad materials' durability without compromising component integrity. The study highlights the role of NDT in understanding friction material behavior, as well as recent developments and challenges in NDT methods like dynamometer noise measurement. It underscores the environmental benefits of integrating natural fibers in brake pads and the advancements in NDT technologies, such as ultrasonic scanning and laser-based techniques, which enhance brake system safety and efficiency. The research emphasizes the need for continuous innovation and adherence to quality standards in automotive engineering to develop more advanced and sustainable braking systems. Such investigations not only enhance our understanding of the behavior of materials under various types of stresses but also pave the way for further improvements in the performance and sustainability of brake pads.

Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and Support Vector Machine (SVM) algorithm

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among other factors, to the low homicide resolution rates. One method for generating forensic evidence in firearm discharges is the identification of gunshot residues (GSR), typically composed of inorganic particles containing primarily lead (Pb), barium (Ba), and antimony (Sb). The “gold standard” technique for GSR identification is Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) which is highly accurate but demands significant time and labor for each sample analysis. Other tests to detect GSR include the colorimetric rhodizonate test and spectroscopic techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), which are quicker, but exhibit lower reliability and a higher potential for false positives and potentially compromise the investigation. This study introduces a novel GSR analytical protocol as a potential alternative to SEM-EDS and colorimetric tests, which is based on LIBS analysis combined with dimensionality reduction performed by PCA and probabilistic Support Vector Machine (SVM) algorithm. The developed protocol accurately predicts both positive and negative samples, and establishes the probability of each prediction, allowing for the exclusion of questionable samples from the analysis. The initial tests show the protocol achieved 100 % accuracy in both training and external validation. As a stress test, the protocol is able to exclude questionable samples from the analysis and accurately predict true samples with 100 % correct classifications from a set of artificially contaminated samples containing elements characteristic of GSR, highlighting the protocol’s significant reduction in the likelihood of false positives.

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis

ABSTRACT This paper presents a finite element model for the dynamic vibration of continuous-welded rails (CWR) under different longitudinal stresses and base conditions. The model supports the physical principles of a vibration-based nondestructive testing (NDT) technique aimed at inferring the lateral and vertical strength, and the neutral temperature of CWR. The model consists of a ~ 5 meters-long rail periodically restrained by distributed translational springs that simulates the lateral and vertical resistance provided by baseplates, fasteners and spikes. Vibration is triggered by a force function equivalent to the mechanical impact of a hammer. The frequency content of the vibrations below 700 Hz and across a range of different longitudinal stress and support conditions is computed using the power spectral density, which constitutes the input matrix of a machine learning algorithm able to learn the complex relationship among frequencies, axial stress, and support conditions. Finally, the model is used to quantify any differences in excitation and detection points to optimize the setup of the proposed NDT technique.