Test Code Research Articles

Incidence of peripheral vestibular disorders based on population data of South Korea.

There have been no comprehensive studies on the incidence of peripheral vestibular disorders based on population-based data for a long-term period. We investigated the incidence of peripheral vestibular disorders using population-based data representing the whole population of South Korea. This study used the National Health Insurance Service data in Korea from 2008 to 2020. Peripheral vestibular disorders such as benign paroxysmal positional vertigo (BPPV), vestibular neuritis (VN), and Meniere's disease (MD) were defined with diagnostic, treatment, or audiovestibular test codes. The annual incidence in total and according to sex, age, and residence was analyzed. The annual incidence of BPPV, VN, and MD per 100,000 was 51.4, 22.7, and 12.4 in 2008 and 181.1, 62.9, and 50.5 in 2020, respectively. The incidence of each was significantly different by sex (p < 0.001), age (p < 0.001), and residence (p < 0.001), with the highest value in female, people aged 60 years or older, and people who resided in metropolitan cities. The annual incidence of BPPV, VN, and MD had increasing trends from 2008 through 2020 in South Korea, and all were significantly higher in female, people aged 60 years or older, and people who resided in metropolitan cities.

A case study of interdisciplinary thematic learning curriculum to cultivate "4C skills".

Critical thinking, communication, collaboration, and creativity are four fundamental skills for students in the 21st century, indicating the way for nurturing talents required for future social development. Interdisciplinary thematic learning has become an important educational carrier for "4C Skills" training, with its connotation coinciding with the training requirements of "4C Skills." Few academics, however, have looked into interdisciplinary thematic learning activities based on real-world problems. In this study, using a middle school in Xiamen, Fujian Province as an example, 32 s-year students in middle school were given several problem-solving tasks relevant to "visual disaster weather." Based on test coding and questionnaire evaluation, class notes, course videos, student solutions, and interview texts, we examined the development of students' 4C skills through real-world problem-based interdisciplinary thematic learning activities. This study discovered that an interdisciplinary thematic learning environment centered on real-world challenges fosters students' creative thinking in open practice while also encouraging group communication and collaboration. Students also gain critical thinking skills through questioning and critique.

Refactoring Test Smells With JUnit 5: Why Should Developers Keep Up-to-Date?

Test smells are symptoms in the test code that indicate possible design or implementation problems. Previous research demonstrated their harmfulness and the developers’ acknowledgment of test smells’ effects, prevention, and refactoring strategies. Test automation frameworks are constantly evolving, and the JUnit, one of the most used ones for Java projects, has its version 5 available since late 2017. However, we do not know the extent to which developers use the newly introduced features and whether such features indeed help refactor existing test code to remove test smells. This article conducts a mixed-method study investigation to minimize these knowledge gaps. Our study consists of three parts. First, we evaluate the usage of this framework and its features by analyzing the source code of 485 popular Java open-source projects on GitHub that use JUnit. We found that 15.9% of these projects use the JUnit 5 library. We also found that, from 17 new features detected in use, only 3 (i.e., 17.6%) are responsible for more than 70% of usages, limiting optimized propositions to test code creation and maintenance. Second, after identifying features in the JUnit 5 framework that could be considered to test smells removal and prevention, we use these features to propose novel refactorings. In particular, we present refactorings based on 7 introduced JUnit 5 features that help to remove 13 test smells, such as Assertion Roulette, Test Code Duplication, and Conditional Test Logic. Third, to evaluate our refactorings with the opinions of experienced developers, we (i) survey 212 developers for their preferences and comments about our refactorings, corroborating the benefits of our proposals and raising community feedback on JUnit 5 features, and (ii) we refactor actual test code from popular GitHub Java projects and submit 38 Pull Requests, reaching a 94% acceptance rate among respondents. As implications of our study, we alert the software testing community (i.e., practitioners and researchers) to the need to study the JUnit 5 features to effectively remove and prevent test smells. To better assist this process, we give directions on how test smells can be refactored using such features.

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Testing Longitudinal Measurement Invariance in Latent Transition Analysis: An Illustrative Analysis of Change in the Teacher−Student Relationship

본 연구의 목적은 최근 사회과학 분야에서 활발하게 적용되고 있는 잠재전이분석(Latent Transition Analysis: LTA)에서 그동안 소홀하게 다뤄진 종단적 측정동일성(Longitudinal Measurement Invariance: LMI) 검증 절차에 대한 가이드라인을 소개하는 것이다. 이를 위해 LTA 모형을 적용한 국내외 경험연구를 탐색했으며, 중고등학교 전환기 교사-학생 관계를 측정한 종단자료를 대상으로 LTA 모형을 구성하고 LMI 검증과정을 제시했다. 구체적인 연구결과는 다음과 같다. 첫째, 선행연구를 고찰한 결과, LTA 모형을 위한 LMI 검증의 필요성과 구체적 방안에 대한 인식 수준은 국외 연구에 비하여 국내 연구에서 상대적으로 낮은 경향을 보였다. 둘째, LTA 모형에서 검증할 수 있는 LMI 유형을 소개하고, 이와 관련된 구체적 검증 방안이 제시됐다. 셋째, LTA 모형에서의 LMI가 제공하는 통계적이고 개념적 유용성을 논의했다. 마지막으로 본 연구를 논의하고 추후 연구를 위한 제언을 추가했다.Latent transition analysis (LTA) is known to be a powerful tool for exploring change in latent classes of individuals over time. For this reason, applications of LTA have been actively published in the social and behavioral sciences, including education. Although the LTA model does not necessitate longitudinal measurement invariance (LMI), LMI is desirable for model parsimony and interpretation of LTA results. However, the issue of LMI has received relatively less attention in the context of the LTA model. To fill this gap in the literature, we have reviewed previous studies and present guidelines for testing LMI in an LTA framework. We also illustrate the LMI testing procedures for the LTA model with panel data from a sample of 5,439 students ranging from the 9th through 11th grades in South Korea. In addition, we provide the Mplus codes for LMI testing. Finally, practical implications of the results are discussed.

Prevalence and Clinical Burden of Idiopathic Dilated Cardiomyopathy in the United States

BackgroundDilated cardiomyopathy (DCM) contributes significantly to heart failure prevalence, yet supporting epidemiologic data is sparse. This study sought to estimate the period prevalence of DCM and the proportion of idiopathic DCM in the United States using a large, diverse electronic health records (EHR) database. MethodsThis retrospective, observational study included 56,812,806 deidentified patients in Optum EHR with visits between 2017 and 2019. Suspected DCM cases were identified using ICD-10 coding. Deidentified clinical notes from 1000 randomly selected cases were manually reviewed to determine the diagnosis of DCM and estimate the proportion of idiopathic DCM. The period prevalence and clinical burden of DCM and idiopathic DCM were estimated. ResultsManual clinical review demonstrated that our definition had a positive predictive value of 92.5% for DCM, with 46.3% estimated as the idiopathic DCM proportion. The estimated period prevalence of DCM between 2017 and 2019 was 118.33 per 100,000. Prevalence increased for adults ≥65 years of age, males, and African Americans. Extrapolation to the 2019 US population led to an overall estimated burden of roughly 388,350 patients. Adjusting for the proportion of cases with idiopathic DCM yielded an idiopathic DCM prevalence of 59.23 per 100,000 and a burden of 194,385 patients. Evidence of clinical genetic testing in this population was scarce, with less than 0.43% of DCM cases reporting a testing code. ConclusionsThis study establishes a conservative period prevalence for DCM and idiopathic DCM and demonstrates very low molecular genetic testing for DCM. These findings suggest that the clinical burden of genetic DCM may be underestimated.

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction

The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar.

A Large-Scale Empirical Study of Real-Life Performance Issues in Open Source Projects

Software performance is a critical quality attribute that determines the success of a software system. However, practitioners lack comprehensive and holistic understanding of how real-life performance issues are caused and resolved in practice from the technical, engineering, and economic perspectives. This paper presents a large-scale empirical study of 570 real-life performance issues from 13 open source projects from various problem domains, and implemented in three popular programming languages, Java (192 issues), C/C++ (162 issues), and Python (216 issues). From the technical perspective, we summarize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eight</i> general types of performance issues with corresponding root causes and resolutions that apply for all three languages. We also identify available tools for detecting and resolving different types of issues from the literature. In addition, we found that 27% of the 570 issues are resolved by design-level optimization—coordinated revision of a group of related source files and their design structure. We reveal four typical design-level optimization patterns, including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">classic design patterns</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">change propagation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimization clone</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parallel optimization</i> that practitioners should be aware of in resolving performance issues. From the engineering perspective, this study analyzes how test code changes in performance optimization. We found that only 15% of the 570 performance issues involve revision of test code. In most cases, the revised test cases focus on the functional logic of the performance optimization, rather than directly evaluate the performance improvement. This finding points to the potential lack of engineering standard for formally verifying performance optimization in regression testing. Finally, from the economic perspective, we analyze the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Return On Investment”</i> of performance optimization. We found that design-level optimization usually requires more investment, but not always yields to higher performance improvement. However, developers tend to use design-level optimization when they concern about other quality attributes, such as maintainability and readability.

Simplification of ISO 3744 - Making the Most Important Standard for Determination of Sound Power Levels Easier to Use

Sound power level (L_W) is the key quantity to describe the noise emission of products and is needed to reduce noise at work, at home, and in the environment, e.g. for promoting and selecting low noise products (Sell and Buy Quiet). ISO 3744:2010 is the main standard referred to by most product test codes and regulatory requirements for determining L_W. It is used by laboratories, noise control engineers, and manufacturers. Due to its structure and length, the usability of the current document is limited. The main body of ISO 3744:2010 contains so much information that the basic concept of sound power is obscured. To improve usability of ISO 3744:2010 three projects were launched: 1. A revision and restructuring of ISO 3744 with a simplified main body and a series of annexes for special cases; 2. A new standard: ISO/CD 5114-1 on the uncertainty of L_W determined from sound pressure levels; 3. A new standard: ISO/CD 26101-2 to determine the environmental correction used in determining L_W. These new standards contain specific information needed by some users while ISO/CD 3744 focusses on determining L_W. We present key aspects of all three committee drafts and explain how and why ISO 3744 should be revised.

A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

Most software companies have extensive test suites and re-run parts of them continuously to ensure that recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This article analyses two decades of TCP research and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to system under test code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes.

STS U 밴드 적용 연도의 기밀성 실험 및 누기량 예측

Chimney is a device that discharges harmful exhaust gases produced during combustion of fossil fuel into the atmosphere. In this study, binding force analysis and air-tightness test of existing V-band and STS spring U-band were performed. U-band uses a STS spring to exert binding force at the connection of chimney pipe parts. The binding force of V-band type is zero since it relies only on sealant which ultimately gets hardened, resulting in cracks after a long-time use. As a result of the pressure drop test for 40 minutes, the pressure of V-band descended largely, while the U-band showed relatively little pressure drop. Pressure drop was about 79% lower than that of the V-band. As a result of the calculation of the leakage amount and the leakage rate, the V-band leaked mass was 2.23 g and the leakage rate was 23.3%, while the U-band leaked mass was 0.33 g and the leakage rate was 3.5%. Leakage rates of V-band and U-band were measured to be 0.000232 and 0.000034 liter/s⋅㎡, respectively. Leakage of U-band was 85% lower than that of V-band. Both types satisfied the criteria of 0.12 liter/s⋅㎡ of SPS-KARSE B 0042-204 Test code for pre-fabricated chimney for exhausting combustion gas. This study shows that U-band is better than conventional V-band for connecting chimney parts in terms of binding force and air-tightness.

On the diffusion of test smells and their relationship with test code quality of Java projects

AbstractTest smells are considered bad practices that can reduce the test code quality, thus harming software testing goals and maintenance activities. Prior studies have investigated the diffusion of test smells and their impact on test code maintainability. However, we cannot directly compare the outcomes of the studies as most of them use customized datasets. In response, we introduced the TSSM (Test Smells and Structural Metrics) dataset, containing test smells detected using the JNose Test tool and structural metrics (test code and production code) calculated with the CK metrics tool of 13,703 open‐source Java systems from GitHub. In addition, we perform an empirical study to investigate the relationship between test smells and structural metrics of test code and the relationship between test smells on a large‐scale dataset. We split the projects into three clusters to analyze the distribution of test smells, the co‐occurrences among test smells, and the correlation of test smells and structural metrics of test code. The ratio of smelly test classes with a specific test smell is similar among the clusters, but we could observe a significant difference in the number of test smells among them. The test smells Sleepy Test, Mystery Guest, and Resource Optimism rarely occur in the three clusters, and the last two are strongly correlated, indicating that those test smells are more severe than others. Our results point out that most test smells have a moderate correlation with high complexity, large size, and coupling of the test code, indicating that they can also negatively affect its quality. To support further studies, we made our dataset publicly available.

Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital.

Validated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies. We aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital. This study used administrative claims data and electronic medical records at Juntendo University Hospital from April 2017 to March 2019. We developed two candidate claims-based algorithms to detect bone metastases, one based on diagnosis codes alone (Algorithm 1) and the other based on the combination of diagnosis and imaging test codes (Algorithm 2). Of the patients identified by Algorithm 1, 100 patients were randomly sampled. Among these 100 patients, 88 patients met the conditions of Algorithm 2; further, 12 additional patients were randomly sampled from those identified by Algorithm 2, thus obtaining a total of 100 patients for Algorithm 2. They were evaluated for their true diagnosis using the patient chart review as the gold standard. The positive predictive value (PPV) was calculated to assess the accuracy of each algorithm. For Algorithm 1, 82 patients were analyzed after excluding 18 patients without diagnostic imaging reports. Of these, 69 patients were true positive by chart review, resulting in a PPV of 84.1% (95% confidence interval (CI) 74.5-90.6). For Algorithm 2, 92 patients were analyzed after excluding eight patients whose diagnoses were not judged by chart review. Of these, 76 patients were confirmed positive by chart review, yielding a PPV of 82.6% (95% CI 73.4-89.1). Both claims-based algorithms yielded high PPVs of approximately 85%, with no improvement in PPV by adding imaging test conditions. The diagnosis code-based algorithm is sufficient and valid for identifying bone metastases in this Japanese hospital.

Testing feature‐rich blockchains

Blockchain implementations have become more and more advanced, combining many different features in the same framework (e.g., oracles, names, and state channels). Since the cost of errors in reputation and represented value is high in the blockchain world, software quality is of the utmost importance. One of the main methods used to assure such high software quality is careful testing. However, the number of tests needed to achieve a high level of assurance grows quadratic with the pairs of features of the blockchain, and when testing triples features the growth is cubic. To manually craft the required large number of tests is an almost impossible undertaking in practice. In this article, we describe how property-based testing (PBT) techniques have been used to automate testing of the core part of the Aeternity blockchain, ensuring the high software quality of the blockchain. Even though PBT is a powerful testing technique, applying it to the task of testing a complex system such as a blockchain, is far from trivial. The structure of the Aeternity property-based test model follows the structure of the blockchain, that is, it cleanly separates different blockchain features (e.g., oracles, smart contracts) into different model parts, and moreover, reduces the amount of boilerplate test model code by focusing on the identification of valid blockchain transactions. The test model is evaluated through a careful instrumentation of test code which permits observations of which combinations of features have been tested during a test run, and with which frequency. This article documents the details of how these issues were addressed in the development of the Aeternity test model, providing insights into both the testing of other blockchains as well as the testing of other complex feature based systems.

Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression.

Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussian-scale flow for prediction, which cannot support fine-grained adaptation to diverse motion content. Towards more content-adaptive prediction, we propose a novel cross-scale prediction module that achieves more effective motion compensation. Specifically, on the one hand, we produce a reference feature pyramid as prediction sources and then transmit cross-scale flows that leverage the feature scale to control the precision of prediction. On the other hand, for the first time, a weighted prediction mechanism is introduced even if only a single reference frame is available, which can help synthesize a fine prediction result by transmitting cross-scale weight maps. In addition to the cross-scale prediction module, we further propose a multi-stage quantization strategy, which improves the rate-distortion performance with no extra computational penalty during inference. We show the encouraging performance of our efficient neural video codec (ENVC) on several benchmark datasets. In particular, the proposed ENVC can compete with the latest coding standard H.266/VVC in terms of sRGB PSNR on UVG dataset for the low-latency mode. We also analyze in detail the effectiveness of the cross-scale prediction module in handling various video content, and provide a comprehensive ablation study to analyze those important components. Test code is available at https://github.com/USTC-IMCL/ENVC.

An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos

Vehicle speed estimation is one of the most critical issues in intelligent transportation system (ITS) research, while defining distance and identifying direction have become an inseparable part of vehicle speed estimation. Despite the success of traditional and deep learning approaches in estimating vehicle speed, the high cost of deploying hardware devices to get all related sensor data, such as infrared/ultrasonic devices, Global Positioning Systems (GPS), Light Detection and Ranging (LiDAR systems), and magnetic devices, has become the key barrier to improvement in previous studies. In this paper, our proposed model consists of two main components: 1) a vehicle detection and tracking component – this module is designed for creating reliable detection and tracking every specific object without doing calibration; 2) homography transformation regression network – this module has a function to solve occlusion issues and estimate vehicle speed accurately and efficiently. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art methods by reducing the mean square error (MSE) metric from 14.02 to 6.56 based on deep learning approaches. We have announced our test code and model on GitHub with <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ervinyo/Speed-Estimation-Using-Homography-Transformation-and-Regression-Network</uri> .

An Implementation of Code Validation Function in C Programming Learning Assistant System

In many universities around the world, C programming is offered as the first programming course. To help novice students learn on their own, we have developed the C Programming Learning Assistant System (CPLAS). Currently, CPLAS offers simple practice questions at the elementary level, where any question requires a word, a sentence, or a number as the answer and the student answer is checked against the correct answer by string matching. However, CPLAS does not cover the problems of writing source codes completely from scratch such that the correctness of each code must be validated automatically. In this paper, we present the implementation of the code validation function for validating the answer source code through 1) compiling test, 2) execution test, and 3) output test. Here, the software test approach using test codes is not adopted, because it might be too difficult for novice students. For evaluations, we applied the proposal to 2, 045 source codes from 43 first-year students at the C programming course in Nihon University, Japan. We analyzed the answer results and confirmed the effectiveness of the proposal. In future works, we will introduce this function in the CPLAS platform, which will be used to help students with self-studies.

Experimental investigation of the effect of the use of nanoparticle additional biodiesel on fuel consumption and exhaust emissions in tractor using a coated engine

This study focuses on reducing the fuel consumption and exhaust gas emission values of the tractor used in the agricultural field. With the additive added to the fuel and the coating of the tractor engine, the fuel consumption values were reduced, and agricultural production costs were tried to be reduced. On the other hand, exhaust emission values were also tried to be reduced and thus a more environmentally friendly production method was aimed to be adopted. For this reason, the cylinders of the tractor engine were coated with Al2O3 + 13%TiO2 metal powders mixed by mass using the plasma spray method. All experiments were repeated by attaching these coated pistons to the engine. The fuel used in the experiments was purchased from a commercial company and the nanoparticle (molybdenum) additive was added to the biodiesel at the rates of 25 ppm, 50 ppm, 100 ppm, and 200 ppm by mass. The fuel mixtures obtained in the coated and uncoated engine, when the engine is in the full throttle position, using the PTO load test unit at 1000 rpm, 1200 rpm, 1400 rpm, 1600 rpm, 1800 rpm, and 2000 rpm. It has been tested by loading at 2200 rpm, 2400 rpm, and 2600 rpm engine speeds. Code for the Official Testing of Agricultural and Forestry Tractor Performance standards were adhered to during all engine tests. The results showed that coating the cylinders and adding nanoparticles into biodiesel reduces the fuel consumption value, increases the exhaust gas temperature, decreases HC, CO, and PM emissions, and increases NOx emissions.

Development of an acoustic emission technology package for copvs inspection and recertification

Composite overwrapped pressure vessels (COPVs) are used in many applications from 10,000 ft below sea levels to outer space. COPVs are designed to sustain high pressure, carry a large volume of a pressurized gas, and offer excellent weight reduction. These vessels can be subjected to complex modes of failure, some of which are dominated by fiber-bundle breakage (FBB). Due to the safety implications of COPV failure, they are subjected to stringent certification requirements by DOT, ASME and ISO. In this document, we present results of a test campaign designed to collect and characterize acoustic signatures of fiber bundle breakage (FBB), as well as deriving experimentally the “single energy event criteria” (SEEC) required by relevant testing codes and procedures. Testing was performed on type 4 carbon fiber COPVs. Three vessels were tested, aged by pressure cycling to simulate refilling operations for 10 years, and were damaged by high velocity impacts. Hydrostatic tests following DOT requalification test procedures were performed at different stages of the ageing process, and the vessels were loaded to failure. The data set provided signals of FBB on damaged vessels.

Structural and performance radiation protection the phosphate glasses contain: Te, K, Al, Nb-doped with rare earth

Because of the increased use of ionizing radiation, radiation management and security procedures are now regarded a standard part of many therapeutic and specialist fields. The focus of this work is on the radiation security features of Novel Oxide Glass (PZBKTANEr). The unique glass assembly is 40P2O5-30ZnO- 20BaF2-3.8K2TeO3- 1.2Al2O3-5Nb2O5-3Er2O3 in mol percent (test code PZBKTANEr). For the suggested oxide glass, several radiation shielding characteristics have been investigated for a specific energy range of ionizing radiation. The linear and mass attenuation coefficients, mean free path, half-value layer, total nuclear and electronic cross-sections, and fast neutron expulsion cross-section are among the radiation shielding properties. Furthermore, the unique fabricated glass (PZBKTANEr) was compared to commonly used radiation protection compositions, such as RS-253 G18, RS-360, RS-520, Chromite, Ferrite, Magnetite, and Barite glass, as well as RS-253 G18, RS-360, RS-520, Chromite, Ferrite, Magnetite, and Barite glass. Also, we studied the structure of fabrication by using Raman spectra. The findings suggest that the new oxide glass might be used in a broad variety of ionizing radiation applications for protection in both therapeutic and industrial applications.