Practical Test Research Articles

Determination of melon seed physical parameters and calibration of discrete element simulation parameters.

To improve the accuracy of the Hami melon discrete element model, the parameters of the Hami melon seed discrete element model were calibrated by combining practical experiments and simulation tests. The basic physical parameters of Hami melon seeds were obtained through physical experiments, including triaxial size, 100-grain mass, moisture content, density, Poisson's ratio, Young's modulus, shear modulus, angle of repose, suspension speed and various contact parameters. Taking the repose angle of seed simulation as an index, the parameters of each simulation model were significantly screened by the Plackett-Burman test. The results showed that the recovery coefficient, static friction coefficient and rolling friction coefficient of Hami melon seeds had significant effects on repose angle. Based on the steepest climbing test and quadratic regression orthogonal rotation combination test, it was determined that the significant order of the influence of various contact parameters on the angle of repose was static friction coefficient, collision recovery coefficient, and rolling friction coefficient. The optimal parameter combination was obtained through the mathematical regression model between the angle of repose and various contact parameters, namely, the collision recovery coefficient of Hami melon seeds was 0.518, the static friction coefficient of Hami melon seeds was 0.585 and the rolling friction coefficient of Hami melon seeds was 0.337. Under this condition, three static seed-dropping experiments and dynamic rolling accumulation experiments were carried out. The average simulated angle of repose was 31.93°, and the relative error with the actual value was only 1.71%. The average simulated rolling accumulation angle was 51.98°, and the relative error with the actual value was only 1.92%.

A fast modeling method of pulsed eddy current with double-coil based on model-driven BP neural network

ABSTRACT The analytical model serves as an important guiding role in practical testing. Currently, there is scarce research on the building of the analytical model for pulsed eddy current (PEC) with double-coil excited by a voltage source. This paper derives a frequency-domain model for PEC from a double-coil’s equivalent circuit. To tackle low modeling efficiency due to complex impedance changes and mutual inductance changes, a BP neural network (BPNN) maps lift-off, thickness, and frequency to impedance changes and mutual inductance changes. Training samples for the BPNN come from analytical models of impedance change and mutual inductance change. The study finds that a BPNN with four hidden layers and Bayesian regularization significantly improves prediction accuracy. Finally, the correctness of the BPNN method is verified. The PEC signals obtained by the BPNN method, the analytical model, and the Comsol finite element method are compared under different thicknesses and lift-offs, and it is found that the consistency of the PEC signals of these three methods is very well. And, the amplitudes of the residual signals between the BPNN method and the analytical model of the PEC signals are relatively small, and the relative errors at the peaks are no more than 0.4%.

Comparison of psychometric properties of the dual-task timed up-and-go test (cognitive) and the 3-m walk backward test in community-dwelling stroke patients

ABSTRACT Background There is a need for practical, easy-to-use and accurately assessing balance tools in stroke patients. Objectives This study aimed to compare the psychometric properties of the dual-task Timed Up-and-Go test (cognitive) (DTUG) and the 3-m walk backward test (3MBWT) in stroke patients. Methods This study evaluated the practicality, validity, and reliability of the DTUG and the 3MBWT. The test-retest method was used for reliability. The Modified Four Square Step Test (MFSST), the Timed Up-and-Go (TUG), and Berg Balance Scale (BBS) were administered for concurrent validity. A cutoff value was calculated to discriminate between fallers and non-fallers. Results The mean practicality times of the tests were 63.58 ± 47.32 sec for DTUG and 37.42 ± 24.036 sec for 3MBWT. Intraclass correlation coefficient of the DTUG and 3MBWT were 0.977, 0.964, respectively which showed excellent test – retest reliability. The DTUG demonstrated strong/very strong correlations with the MFSST (r = 0.724, p < 0.001), TUG (r = 0.909, p < 0.001), and BBS (r = –0.740, p < 0.001). The 3MBWT showed strong correlations with the MFSST (r = 0.835, p < 0.001), the TUG (r = 0.799, p < 0.001), and the BBS (r = –0.740, p < 0.001). The cutoff point was 36.945 s for DTUG and 14.605 s for 3MBWT. Conclusions The 3MBWT was a more practical test than the DTUG; however, the DTUG was more discriminative than the 3MBWT in identifying fallers after stroke. Clinical Trial Registration Number NCT05211349 URL https://register.clinicaltrials.gov/prs/app/action/SelectProtocol?sid=S000BRKZ&selectaction=Edit&uid=U0005GRO&ts=2&cx=z21bhg

TP53 mutations in myeloid neoplasms: implications for accurate laboratory detection, diagnosis, and treatment.

Genetic alterations that affect the function of p53 tumor suppressor have been extensively investigated in myeloid neoplasms, revealing their significant impact on disease progression, treatment response, and patient outcomes. The identification and characterization of TP53 mutations play pivotal roles in subclassifying myeloid neoplasms and guiding treatment decisions. Starting with the presentation of a typical case, this review highlights the complicated nature of genetic alterations involving TP53 and provides a comprehensive analysis of TP53 mutations and other alterations in myeloid neoplasms. Currently available methods used in clinical laboratories to identify TP53 mutations are discussed, focusing on the importance of establishing a robust testing protocol within clinical laboratories to ensure the delivery of accurate and reliable results. The treatment implications of TP53 mutations in myeloid neoplasms and clinical trial options are reviewed. Ultimately, we hope that this review provides valuable insights into the patterns of TP53 alterations in myeloid neoplasms and offers guidance to establish practical laboratory testing protocols to support the best practices of precision oncology.

Instrumen Inovatif untuk Mengembangkan Asesmen Praktik dan Produk dalam Pembelajaran Bahasa Indonesia di Era Digital

In the current era of globalization and digitalization, the teaching of the Indonesian language needs to adapt to technological advancements and innovative teaching methods. An important aspect of learning is assessment. Assessment not only measures students’ abilities but also serves as a tool to motivate and guide learning. This article describes various innovative tools that can be used to develop practical tests and learning products for the Indonesian language. Emphasis is placed on the application of digital technology, such as interactive applications and e-learning platforms, and alternative assessment methods, such as collaboration-based projects, digital portfolios, and performance-based assessments. These tools not only enhance student engagement and motivation but also provide more comprehensive and detailed feedback on students’ skills and development. With these innovative tools, it is hoped that learning the Indonesian language will become more meaningful, effective, and enjoyable for students.

Evaluating and Managing the Microbial Contamination of Eye Drops: A Two-Phase Hospital-Based Study.

The microbial contamination of eye drop tips and caps varies between 7.7% and 100%. In seeking patient protection and continuous improvement, the Pharmacy Department in the Sterile Ophthalmological and Oncological Preparations Unit at Cochin Hospital AP-HP, Paris, France, conducted a two-phase study to compare the antimicrobial efficiency and practical use of standard packaging and a marketed eye drop container incorporating a self-decontaminating antimicrobial green technology by Pylote SAS at the tip and cap sites. The first phase was conducted in situ to identify the microbial contaminants of eye drops used in the hospital and community settings. A total of 110 eye drops were included for testing. Staphylococcus species were the most prevalent bacteria. Candida parapsilosis was detected in only one residual content sample and, at the same time, on the cap and tip. The second phase was performed in vitro, according to JIS Z2801. Reductions above one log in Staphylococcus aureus and Pseudomonas aeruginosa counts were noted in Pylote SAS eye drop packaging after 24 h of contact. The practical tests showed satisfactory results. Pylote SAS antimicrobial mineral oxide technology exhibited promising effects that combined effectiveness, safety, and sustainability to protect the patient by preventing infections due to the contamination of eye drop containers.

Micro-expression recognition based on a novel GCN-transformer cooperation model for IoT-eHealth

If a person is truly healthy, his/her well-being encompasses both physical and psychological health. However, the existing IoT-eHealth system typically focus only on monitoring the user’s physical data through various sensors, neglecting their mental state. To enhance the intelligence level of IoT-eHealth system and enable it to have the psychological monitoring ability, a novel collaborative model based on Graph Convolutional Network (GCN) and Transformer is designed for Micro-Expression (ME) recognition in this paper. Firstly, facial information within each frame is transformed into a Spatial Topological Relationship Graph (STRG) by using facial landmarks detection and psychological relationship of local patches. Then, in order to automatically aggregate the key information on facial patches that contribute to ME recognition from the structured graph data, a Hierarchical Adaptive Graph Pooling (HAGP) module is designed for obtaining discriminative frame-level feature based on GCN utilizing graph structure and vertex global dependencies. Finally, in order to model the long-term dependencies among frames and capture the key frame-level features that are beneficial for ME recognition, a Temporal Sensitive Self-Attention (TSSA) mechanism is designed, and a novel Temporal Sensitive Transformer (TST) encoder is constructed based on TSSA to explore the evolution law of facial patterns and obtain discriminative video-level features that are helpful for ME recognition. In the comparative experiments of standard dataset verification and practical dataset testing, designed collaborative model is superior to other methods and can achieve the highest recognition accuracy, which almost can meet the application requirements of IoT-eHealth system.

Integration of Sustainable Development Goals in university STEM courses

The objectives of this study are: (1) to provide an overview of the validity of learning tools for STEM Method courses based on the Sustainable Development Goals Curriculum, (2) to assess the practicality of these learning tools, and (3) to evaluate their effectiveness in enhancing students' problem-solving abilities. This research employs the Research and Development (R&amp;D) methodology. Data collection methods include interviews, product validation, questionnaires, and tests. Data analysis techniques encompass qualitative analysis, product validity testing, practicality testing, and effectiveness testing. The findings indicate that: (1) learning tools for STEM Method courses based on the Sustainable Development Goals curriculum comprise a Learning Plan and teaching materials, both of which are highly valid (scores of 3.4 and 3.5 respectively), (2) the practicality test of the teaching materials scored 3.73, indicating high practicality, and (3) these tools effectively improve students' problem-solving abilities, as evidenced by the experimental class outperforming the control class. In conclusion, the study demonstrates that the designed learning tools aligned with the Sustainable Development Goals Curriculum not only show strong validity and practicality but also significantly enhance students' problem-solving skills in STEM education contexts.

Genetics of hypertrophic cardiomyopathy: established and emerging implications for clinical practice.

Pathogenic variation in genes encoding proteins of the cardiac sarcomere is responsible for 30%-40% of cases of hypertrophic cardiomyopathy. The main clinical utility of genetic testing is to provide diagnostic confirmation and facilitation of family screening. It also assists in the detection of aetiologies, which require distinct monitoring and treatment approaches. Other clinical applications, including the use of genetic information to inform risk prediction models, have been limited by the challenge of establishing robust genotype-phenotype correlations with actionable consequences, but new data on the interaction between rare and common genetic variation, as well as the emergence of therapies targeting disease-specific pathogenic mechanisms, herald a new era for genetic testing in routine practice.

Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction

Wheeled lifting robots have found widespread applications in various industrial and logistical environments. However, traditional robots are far from adequate in terms of visual perception capabilities. Additionally, their remote control methods suffer from inefficiencies, which tend to bring safety concerns. To address these issues, this work proposes an autonomous multi-sensor-enabled wheeled lifting robot system, i.e., AMSeWL-R, to facilitate remote autonomous operations. Specifically, AMSeWL-R integrates real-time simultaneous localization and mapping with object detection on a wheeled lifting robot. Additionally, a novel mobile-ROS interaction method is proposed to achieve real-time communication and control between a mobile device and a ROS host. Furthermore, a lightweight object detection algorithm based on YOLOv8, i.e., YOLOv8-R, is proposed to achieve faster detection. Experimental results validate the effectiveness of the AMSeWL-R system for accurately detecting objects and mapping its surroundings. Furthermore, TensorRT acceleration is employed during practical testing on a Jetson Nano to achieve real-time detection using the proposed YOLOv8-R, demonstrating its efficacy in real-world scenarios.

Development of a Reference Installation for Metrological Support of the Delta Value Measurements of the Ratio of Carbon and Oxygen Isotopes in Exhaled Air

Breath tests in domestic and foreign medicine are recognized as an effective and safe way to identify pathogens in the gastrointestinal tract, in particular the Helicobacter Pylori bacteria. At the end of the 20th century, a urease breath test was introduced into clinical practice to diagnose diseases of the gastrointestinal tract; it was based on the use of urea labeled with a stable isotope 13C ( 13C-urease breath test). The essence was to measure the delta value of the ratio of carbon and oxygen isotopes in exhaled air.Over decades of clinical practice of using this test, a wealth of experience related to the diagnosis of diseases caused, including by the Helicobacter Pylori bacteria has been accumulated. A significant amount of knowledge has also been accumulated in terms of metrological support of the carbon and oxygen isotope delta in exhaled air.The article describes the procedure and results of developing a development of a measurement standard of carbon and oxygen isotope delta in exhaled air, implemented within the R&amp;D a/c No. «Sistema-2020-М» at the D. I. Mendeleyev Institute for Metrology (VNIIM). The authors presented the theoretical study, test methodology, their sequence and results. The article also contains a report on the international comparisons CCQM-P204 “Isotope ratios of CO2 (δ13VPDB and δ18VPDB) in pure CO2” with the application of the measurement standard developed by VNIIM. The metrological characteristics of the measurement standard obtained during the comparisons correspond to the most accurate measurements of the ratio of carbon and oxygen isotopes, which guarantees the recognition of the measurement results at the international level. Considering the high demand for breath tests in medical practice and obvious prospects of this type of diagnostics, further research will be aimed at creating a State Primary Standard for the delta value unit of the ratio of isotopes of carbon, oxygen, hydrogen based on the created measurement standard and the development of certified reference materials of the ratio of isotopes of carbon, oxygen, hydrogen in liquid, solid, and gaseous media, including for the purpose of replacing foreign-made materials.

DEVELOPMENT OF MATHEMATICS LEARNING MEDIA CONCEPT BOARD (PAKELING BANDAR) FOR STUDENTS WITH SPECIAL NEEDS

The research aims to produce mathematics learning media that is valid, practical, and effective according to the student’s needs, particularly in learning geometry for special needs students in Junior High School (phase D). The research method used is research and development with analysis flow; design, development, implementation, and evaluation. The study was conducted in a special school with a total of 3 students, namely 1 student with Autism Spectrum Disorder (ASD) and 2 students with intellectual disabilities. The data collection techniques used were questionnaires, observations, and test questions. The instruments used were validation questionnaires, teacher response questionnaires, student observation sheets, and sheets of pretest-posttest. The research results show that Pakeling Bandar media has valid criteria by the three validators with V-Aiken scores ≥0,61 in all aspects of the assessment. The practicality test obtained an average score of 3,59. Media Pakeling Bandar was declared very effective based on the assessment results post-test with a completion percentage of 98,33%. Moreover, learning media has met the criteria of being valid, practical, and effective and has met the needs of phase D ASD and mentally retarded students in maximizing the learning of geometric elements in the material around triangles, squares, and rectangles. With Pakeling Bandar media, students can understand the concept of the perimeter of plane figures, and calculate the perimeter of plane figures, and students are actively involved in learning in class.

DEVELOPMENT OF INTERACTIVE TEACHING MATERIALS WITH RME-BASED ISPRING SUITE TO IMPROVE STUDENTS' CREATIVE THINKING SKILLS

With the use of the RME-based iSpring suite, this project creates interactive teaching resources to enhance students' capacity for creative thought. Students' low capacity for creative thought, low levels of desire, low levels of activity, and low levels of involvement in the learning process are the field's problems. In order to address these issues, interactive instructional resources were created. With the help of RME-based ispring suites, interactive teaching materials will be created that are valid, useful in the educational process, and will enhance students' capacity for creative thought. 4D (define, design, development, disseminate) is the development model that is employed. Pretest-poste instruments, student response questionnaires, and validation sheets are used in data gathering procedures. Analysis methods include the paired sample t-test, validity test, and practicality test, n-gain test. With a score of 3.41, the validity test results indicate that the product is already in the legitimate category. According to the analysis of the practicality test results, the produced product already has a score of 3.43, placing it in the extremely practical category. It is established by the paired sample t-test that capacity differences exist between pre- and post-use of interactive training resources. Moreover, improving pupils' capacity for creative thought is known to fall into the effective group based on the results of the n-gain test. In order for the RME-based iSpring suite of interactive teaching resources to be generally appropriate for use in the learning process

EXERCISES AND TESTS IN ENGLISH ACADEMIC WRITING

The aim of this paper is to disclose the critical domain of academic writing and its pivotal role in scholarly communication and professional achievement. The study seeks to evaluate the efficacy of a structured academic writing approach using a mixed-methods design, incorporating pre-test and post-test assessments alongside qualitative insights derived from the participant feedback. The participants underwent an initial pre-test covering the structural aspects of academic writing, followed by engagement in a structured undergraduate course emphasising specific nuances of academic papers and mastery of cohesive elements for academic and professional purposes. The qualitative aspect provided a nuanced understanding of the learners’ experiences and perceptions. The structured undergraduate course entailed practical activities and tests covering fundamental aspects of academic writing. These encompass understanding the intended audience and purpose, recognising conventions and standards, grasping the subtleties of formal tone and language precision, refining skills in evidence-based argumentation, analysing the organisational structure of academic texts, evaluating the effectiveness of thesis statements, and incorporating tasks related to professional writing. The research underscores the practical application of acquired academic writing skills, aligning them with professional purposes. Overall, the study aims to contribute valuable insights into the effectiveness of a structured academic writing approach in preparing individuals for success in both academic and professional contexts.

Application of Machine Learning Methods for Forecasting in the Agricultural Sector of the Economy

This article is aimed at researching machine learning methods in the context of agriculture. Studying the issues of advanced mathematical method adaptation for the agricultural sector of the economy allows us to build effective management models and predict further development. The main purpose of this article is to analyze the application of advanced machine learning methods (linear regression, gradient boosting, random forest, neural networks (Deep Learning), Support Vector Machines (SVR), One-Class SVM, KNN, K-Means, principal component analysis (PCA)) in the agricultural sector in order to optimize production, increase the efficiency of agribusiness and predict parameters. Based on the analysis of key machine learning methods, conclusions regarding their applicability in agriculturewere drawn. A special emphasis in the study is placed on the development and improvement of a model for predicting agricultural yields using machine learning methods. For practical testing, a basic model based on RandomForestRegressor was chosen, which allows predicting yield, taking into account various factors such as weather conditions, fertilizer level, soil type and other significant aspects. As part of the study, an algorithm for performing calculations using Python is presented, which allows for model training, visualization of the influence of parameters and statistical analysis. During the testing process, the model was optimized using GridSearchCV, aimed at selecting optimal hyperparameters. The analysis revealed the possibility of model improvement, yet, at low statistical significance. The obtained research results show that it is possible to apply machine learning to improve the productivity of forecasting key parameters in agricultureeffectively.

Experience in the development and use of simulators for professional training of specialists in the electric power industry

Issues of development of simulators for training and testing of electrical engineers and students of electrical engineering specialties were considered. They are based on modern knowledge in the field of electricity. The main characteristics of simulators and requirements for their computer support are given. The developments implement a new method of practical training and testing of electrical engineers and can be used at all stages of electrical engineer training. The considered simulators are based on a combination of practical and theoretical knowledge of the electric power industry. The purpose of the developments is to activate and increase the efficiency of the educational process due to the introduction of new learning technologies. The technical capabilities of the software implement both individual and team work of students and electric power personnel. It was noted that in recent years, two professional simulators, implemented in the form of two laboratory workshops, have been developed and implemented in the educational process at the department of electrical energy transmission of National technical university “Kharkiv polytechnic institute”. The first helps students study the main provisions of the Normative Documentation in the power industry, the second – the rules and sequence of operational switching in electrical networks. In addition, experts determine the high efficiency of training systems based on precedent-analytical models. The exercise program according to the plan-do-check-act cycle is considered. Its basis is the well-known concept of continuous quality improvement “Plan-Do-Check-Act” (Deming's cycle). For the electric power industry, a plan-do-check-act cycle approach can consist of cyclical planning of activities, implementation of planned actions, verification of results obtained and correction of non-conformance to expectations. The precedent-scenario method of constructing a training system based on the software platform of the training system based on the plan-do-check-act scenario is presented in detail.

Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes

Sustainable Manufacturing Practices (SMP), particularly in the selection of materials, have become essential due to environmental issues caused by the expansion of industry. Compared to conventional polymers, biodegradable Polymer Materials (BPM) are growing more commonly as an approach to reducing trash pollution. Suitable materials can be challenging due to numerous considerations, like ecological impact, expenditure, and material properties. When addressing sophisticated trade-offs, standard approaches drop. To compete with such challenges, employing Genetic Algorithms (GA) may be more successful, as they have their foundation in the basic concepts of biological development and the natural selection process. With a focus on BPM, this study provides a GA model for optimal packaging substance selection. Out of the four algorithms for computation used for practical testing—PSO, ACO, and SA—the GA model is the most effective. The findings demonstrate that GA can be used to enhance SMP and performs well in enormous search spaces that contain numerous different combinations of materials.

A novel approach using copper oxide nanocomposites-based biosensor for lactate detection in athletes

Accurate monitoring tools are necessary for lactate, a critical biomarker linked to anaerobic metabolism and muscular performance. Lactate dehydrogenase (LDH) immobilized on MXene (Ti3C2Tx)-supported CuO nanocomposites (MCNs) modified glassy carbon electrode (LDH/MCN/GCE) is the innovative electrochemical biosensor for lactate detection presented in this work. CuO nanostructure integration onto MXene nanosheets, MCN production, and LDH immobilization were all successfully accomplished, according to structural characterization performed using XRD, SEM, XPS, and FTIR. The best lactate oxidation current was shown by LDH/MCN/GCE, according to electrochemical study. With a low detection limit of 0.25 µM and a high sensitivity of 5.92107 µA/mM, the biosensor exhibited a linear response range of 0.1–121 mM. The remarkable selectivity, stability, and sensitivity were attributed to enhanced LDH immobilization made possible by MCNs and MXene. The promise of LDH/MCN/GCE for bioanalytical applications is highlighted by promising results from practical testing in human blood samples, opening the door for improvements in lactate monitoring and performance optimization techniques.

Medical students' perspective on the United States Medical Licensing Examination (USMLE) Step 1 transition to Pass/Fail

Background The transition of the United States Medical Licensing Exam: Step 1 to Pass/Fail (P/F), from scored, caused uncertainty about students’ preparedness and wellbeing related to the exam. Comparison of study behavior and results, before and after the P/F transition can provide insights for the medical curriculum and student support. The aim of this study is to evaluate students’ perceptions of their efforts in Step 1 preparation, as compared to cohort performances to determine the impact of the transition of Step 1 to P/F on medical students. Methods Data from four cohorts of second-year medical students (Class of 2022–25, N = 204) were collected from their dedicated Step 1 self-study block. Student study regiments, aggregate practice test results, Step 1 pass rates and post-block self-reported surveys were analyzed qualitatively to compare student effort and outcomes for scores and P/F cohorts. Results Analysis of practice exam averages across the four student cohorts demonstrates a potentially slower and less rigorous start to Step 1 self-studying during the dedicated preparation block for the cohorts that took the P/F Step 1 exam format as compared to the previous cohorts that prepared for a scored Step 1 exam. Similarly, self-reported study regiments decreased in the median number of hours/day and number of weeks of study for the cohorts with P/F Step 1 exam. There was also a slight shift in the type of study resources used, between the two groups, with the scored group using more traditional board preparation resources. Conclusions The P/F transition of the Step 1 exam may lead to reduced student preparedness and may require adjustments in the resources and support provided by institutions.

Research on differential data fusion processing method for dual GNSS base stations

Abstract In the differential processing of global navigation and positioning system (GNSS) trajectory data from dual base stations, a hierarchical fusion processing method is proposed based on the trajectory parameter layer for two trajectory parameters generated by the different dual base stations according to the distribution pattern. Trajectory segment that satisfies the differential baseline of a priori data accuracy are given high weight to prioritize for fusion, and the portion exceeding the maximum differential baseline distance is weighted fusion according to the sliding window plus polynomial fitting to determine the weighted coefficients. By practical testing, the statistical residuals of the weighted fusion trajectory in the horizontal and vertical directions reduced by 32.83% and 30.19% respectively compared to the unfused trajectory.