Reliable Verification Research Articles

Research and Analysis of Facial Recognition Based on FaceNet, DeepFace, and OpenFace

This study provides a comprehensive review of recent advancements in face recognition technology, focusing on deep learning models such as FaceNet, DeepFace, and OpenFace. The primary evaluation criterion is these models' ability to produce accurate facial embeddings, which are essential for reliable identification and verification. The findings demonstrate that these models significantly enhance recognition performance, particularly under challenging conditions such as varying lighting and occlusions. However, the study also identifies ongoing issues, including the need for efficient processing and reliance on large, annotated datasets. Future research should address these challenges by improving the efficiency and scalability of deep learning models. Additionally, expanding datasets to include a broader range of facial features will enhance model robustness in real-world applications. Exploring the integration of advanced technologies, such as sophisticated data augmentation techniques, will further boost the accuracy and adaptability of face recognition systems. These efforts are expected to advance the development of more versatile and reliable face recognition technologies.

중등교사 일터영성 척도 구성요인 타당화

Objectives The purpose of this study is to validate a workplace spirituality scale for secondary school teachers in Korea. Methods First, preliminary questions were created by reviewing previous research and conducting FGI (Focus Group Interview) analysis. Additionally, expert evaluation of the created questions was conducted to confirm content validity. Second, for the verification of validity and reliability, data were collected from 690 secondary school teachers nationwide. Exploratory and confirmatory factor analyses were conducted using SPSS and Mplus programs. Additionally, convergent, discriminant, criterion validity were confirmed, and Cronbach’s α coefficient was calculated. Results First, the exploratory factor analysis revealed that secondary school teachers' workplace spirituality can consist of four sub-factors: (1) pursuit of the inner world (4 items), (2) self-transcendence (4 items), (3) sense of duty (4 items), and (4) sense of community (3 items), resulting in a final set of 15 items. Second, confirmatory factor analysis showed a good fit (RMSEA=.043, TLI=.998, CFI=.997). Third, the factor loadings were all statistically significant, range from 0.699 to 0.911. The average variance extracted (AVE) value was 0.69, and the composite reliability (CR) was 0.89, confirming convergent validity. Fourth, the AVE for each sub-factor was greater than the squared correlation coefficients with job satisfaction and job burnout, confirming discriminant validity. Fifth, the correlation coefficient with the existing workplace spirituality scale showed significant positive correlations (0.58 to 0.78), confirming criterion validity. Sixth, the Cronbach’s α of this scale was 0.764, indicating an appropriate level of internal consistency. Conclusions The workplace spirituality of secondary school teachers can be composed of four sub-factors: the pursuit of the inner world, self-transcendence, sense of duty, and sense of community. The scale developed based on these factors was found to consistently and validly measure the workplace spirituality of secondary school teachers.

Determination of Strength Parameters of Composite Reinforcement Consisting of Steel Member, Adhesive, and Carbon Fiber Textile.

The main aim of the study was the determination of the strength parameters of composite bonded joints consisting of galvanised steel elements, an adhesive layer, and Carbon-Fiber-Reinforced Plastic (CFRP) fabric. For this purpose, shear laboratory tests were carried out on 60 lapped specimens composed of 2 mm thick hot-dip galvanised steel plates of S350 GD. The specimens were overlapped on one side with SikaWrap 230 C carbon fibre textile (CFT) using SikaDur 330 adhesive. The tests were carried out in three series that differed in overlap length (15 mm, 25 mm, and 35 mm). A discussion on the failure mechanism in the context of the bonding capacity of the composite joint was carried out. We observed three forms of joint damage, namely, at the steel-adhesive interface, fibre rupture, and mixed damage behaviour. Moreover, an advanced numerical model using the commercial finite element (FE) program ABAQUS/Standard and the coupled cohesive zone model was developed. The material behaviour of the textile was defined as elastic-lamina and the mixed-mode Hashin damage model was implemented with bi-linear behaviour. Special attention was focused on the formulation of reliable methodologies to determine the load-bearing capacity, failure mechanisms, stress distribution, and the strength characteristics of a composite adhesive joint. In order to develop a reliable model, validation and verification were carried out and self-correlation parameters, which brought the model closer to the laboratory test, were proposed by the authors. Based on the conducted analysis, the strength characteristics including the load-bearing capacity, failure mechanisms, and stress distribution were established. The three forms of joint damage were observed as steel-adhesive interface failure, fibre rupture, and mixed-damage behaviour. Complex interactions between the materials were observed. The most dangerous adhesive failure was detected at the steel and adhesive interface. It was also found that an increase in adhesive thickness caused a decrease in joint strength. In the numerical analysis, two mechanical models were employed, namely, a sophisticated model of adhesive and fabric components. It was found that the fabric model was very sensitive to the density of the finite element mesh. It was also noticed that the numerical model referring to the adhesive layer was nonsensitive to the mesh size; thus, it was regarded as appropriate. Nevertheless, in order to increase the reliability of the numerical model, the authors proposed their own correlation coefficients α and β, which allowed for the correct mapping of adhesive damage.

Study on the reliability verification method of electronic transformer under multi-physical field coupling effect

Abstract With the development of smart grid technology, electronic transformers play an important role in substations, but their reliability is affected by a variety of environmental factors, such as temperature, humidity, and electromagnetic interference. Existing test standards fail to comprehensively simulate the operation of electronic transformers in real environments, resulting in their insufficient reliability. In this study, we propose a reliability verification method for electronic transformers that considers the effects of multiple physical fields, which mainly includes an electrical fast transient pulse group interference immunity test with temperature and humidity considerations, a conducted interference immunity test with radio frequency induction, and a surge interference immunity test. The test results show that the immunity of the collector is greatly affected when the electronic transformer is subjected to electromagnetic interference superimposed on temperature and humidity field changes and is prone to failure in extreme cases. This study provides a practical basis for the scenario application of electronic transformers.

Channel and Spatial Attention in Chest X-Ray Radiographs: Advancing Person Identification and Verification with Self-Residual Attention Network.

In contrast to traditional biometric modalities, such as facial recognition, fingerprints, and iris scans or even DNA, the research orientation towards chest X-ray recognition has been spurred by its remarkable recognition rates. Capturing the intricate anatomical nuances of an individual's skeletal structure, the ribcage of the chest, lungs, and heart, chest X-rays have emerged as a focal point for identification and verification, especially in the forensic field, even in scenarios where the human body damaged or disfigured. Discriminative feature embedding is essential for large-scale image verification, especially in applying chest X-ray radiographs for identity identification and verification. This study introduced a self-residual attention-based convolutional neural network (SRAN) aimed at effective feature embedding, capturing long-range dependencies and emphasizing critical spatial features in chest X-rays. This method offers a novel approach to person identification and verification through chest X-ray categorization, relevant for biometric applications and patient care, particularly when traditional biometric modalities are ineffective. The SRAN architecture integrated a self-channel and self-spatial attention module to minimize channel redundancy and enhance significant spatial elements. The attention modules worked by dynamically aggregating feature maps across channel and spatial dimensions to enhance feature differentiation. For the network backbone, a self-residual attention block (SRAB) was implemented within a ResNet50 framework, forming a Siamese network trained with triplet loss to improve feature embedding for identity identification and verification. By leveraging the NIH ChestX-ray14 and CheXpert datasets, our method demonstrated notable improvements in accuracy for identity verification and identification based on chest X-ray images. This approach effectively captured the detailed anatomical characteristics of individuals, including skeletal structure, ribcage, lungs, and heart, highlighting chest X-rays as a viable biometric tool even in cases of body damage or disfigurement. The proposed SRAN with self-residual attention provided a promising solution for biometric identification through chest X-ray imaging, showcasing its potential for accurate and reliable identity verification where traditional biometric approaches may fall short, especially in postmortem cases or forensic investigations. This methodology could play a transformative role in both biometric security and healthcare applications, offering a robust alternative modality for identity verification.

(Invited) Durability Analysis of Meas with Reversal Tolerant Anodes Under Repeated Cell Reversal Conditions

As regulations on greenhouse gas emissions are being strengthened, polymer electrolyte fuel cells (PEFCs) are being applied as a power source in a variety of applications. Mobility such as cars, ships, trains, airplanes, etc. have dynamic operating conditions. Because these operating conditions affect the durability of fuel cells, many studies have been reported analyzing durability of the membrane electrode assemblies (MEAs) by applying repeated operating conditions. 'Cell Reversal’, where the fuel cell voltage drops below 0V, is one of these operating conditions. This phenomenon is observed when electric energy is continuously consumed under hydrogen starvation conditions at the anode or when the required electric energy suddenly increases.In this talk, first, we attempted to find the optimal additive ratio by controlling the additive content of the electrode. Second, the durability of MEAs with three different types of electrocatalysts and additives was analyzed. Lastly, we will discuss strategies to further improve the durability of MEAs with additives in the anode. Acknowledgements This research was supported by a grant from Endowment project of “Development of Basic Technologies in Eco-friendly Ship Fuel Reliability and Safety Evaluation” funded by Korea Research Institute of Ships and Ocean Engineering (PES5100), and this research was also supported by a grant from “Development on operation and reliability verification technology of 1MW class eco-friendly ship fuel and power system under ocean environment (RS-2021-KS211507/PMS5950) funded by the Ministry of Oceans and Fisheries of Korea

LIDeepDet: Deepfake Detection via Image Decomposition and Advanced Lighting Information Analysis

The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection.

Vibration response of nanobeams subjected to random reactions

Nanobeams composed of materials exhibiting flexoelectric properties have been successfully used in advanced technological equipment, including electronic circuits and very sensitive sensors, owing to their remarkable unique effects. Therefore, it is essential to determine their mechanical behavior as a practical need. This study employs an analytical methodology to provide a precise solution to the vibration issue of nanobeams under random stationary loads. Under such circumstances, the nanobeam is influenced by both the temperature and moisture conditions simultaneously. Additionally, the beam is supported by a viscoelastic foundation that considers both the viscous resistance parameter and the elastic parameter. Analytical formulations are derived by integrating classical beam theory with nonlocal strain gradient theory in order to elucidate the impact of size effects on nanobeams. This research also demonstrates the reliability verification, which clearly validates the accuracy of the calculation formula used in this work. This study examines the impact of material parameters, viscoelastic foundation, temperature, and moisture on the displacement spectrum at the middle and entire length of the beam. The study also explores how the flexoelectric effect decreases the displacement in the nanobeam. Subsequently, we provide scientifically derived findings that have significant relevance for building practical nanobeam specifications.

Evaluating inverse modeling methods for measurement and verification of chiller energy efficiency measures

A commonly budget and time constrained yet crucial measurement and verification process for chiller energy efficiency measures often requires rarely trended chiller performance data for the pre-retrofit or post-retrofit chiller necessitating chiller modeling for year-round performance prediction. This study evaluates chiller inverse modeling methods for measurement and verification applications. Variable speed drive-controlled centrifugal chillers operating in a hot and humid climate are used as case studies. Two scenarios are explored: one where full-range metered chiller data are available and another with limited data that requires a short-term metering process. The biquadratic black-box and Gordon-Ng with a variable entropy term models perform exceptionally well when full-range chiller performance data is available, displaying a coefficient of variation of approximately 5 % for both training and test datasets. However, in situations that use short-term metered data not covering the full range, the fundamental and Foliaco reformulated Gordon-Ng models outperform other models. These models show minimal degradation in average statistical performance when trained with one-month metering data instead of four-month data, indicating their potential to capture the year-round chiller performance variation with short-term metered data. Furthermore, the optimal metering period for an accurate modeling process is identified as one containing data from at least one shoulder month like April, May, or October for a hot and humid climate. These findings provide valuable guidance for practitioners involved in chiller efficiency measure assessments, emphasizing the significance of proper model selection and an appropriate metering period for a reliable measurement and verification process.

Surface patterning for multi-scale strain analysis of in-situ SEM mechanical experiments

Scanning electron microscopy (SEM) enables microstructure characterization at multi-scale, having potential to quantify strain distribution across multiple fields of view (FOVs) and exploring coordinated relationships among the deformations of multi-scale microstructures. Optimizing distribution of multi-scale speckles is crucial for achieving high precision and efficiency in digital image correlation (DIC) computations. In this work, a multi-scale strain measurement method is proposed for in-situ SEM mechanical experiments, which relies on an uncomplicated multi-scale speckle preparation method. Multi-scale speckle preparation can be achieved by the combination of spin-coating and magnetron sputtering. By using micro- and macro-speckle, multi-scale strain calculations can be achieved during the in-situ SEM mechanical loading. The InSn macro-speckle is composed of controllable-sized InSn nanoscale speckles, ensuring that the accuracy of DIC measurements in the microscopic regions are not compromised by the coverage of macro-speckles in the testing region. In regions covered exclusively by SiO2 nano-speckles, in-situ SEM mechanical loading allows for the acquisition of high-resolution DIC (HR-DIC) and electron backscatter diffraction (EBSD) data from the same region and under the same deformation state. Experiments are conducted to illustrate the functionality and utility of the multi-scale speckles. Such experimental analyses offer an opportunity to further explore multi-scale strain heterogeneity in some advanced structural materials and provide reliable experimental verification for material mechanics simulations.

Research on the qualification of TQL-5 COTS Tools in airborne software reliability verification process based on DO-330

Abstract The rapid development of airborne software is inseparable from the help of tools, especially the TQL-5 (Tool Qualification Level 5 based on DO-330) COTS (commercial off-the-shelf tools) tool, which is the most widely used. It is worth noting that to ensure software reliability and safety, the tool also needs to be qualified. Based on the DO-330 standard, this paper introduces the qualification methods for COTS tools, TQL-5 tools, and TQL-5 COTS tools, focusing on how to qualify TQL-5 COTS tools and the objectives that tool developers and users need to meet. It plays a guiding role in the airworthiness of airborne software tool users and tool developers.

레스토랑 비건 메뉴 속성이 고객 만족과 추천 의도에 미치는 영향

Objective : This study was conducted to verify the relationship between the attributes of vegan menus perceived by consumers who have experienced vegan menus in restaurants, restaurant satisfaction, and intention to recommend. Methods : This study conducted an online survey, collected a total of 300 questionnaires, and used 277 of them for statistical analysis. This study used SPSS 24.0 and AMOS 23.0 programs for the statistical analysis and employed confirmatory factor analysis and structural equation analysis for demographic characteristics, general characteristics of the subjects, and reliability and validity verification. Results : All three attributes of vegan menu quality (menu quality, variety, and price) had a significant positive (+) effect on restaurant satisfaction. In particular, variety among the attributes of vegan menus had the most significant influence on satisfaction. In addition, restaurant satisfaction had a significantly positive (+) influence on the intention to recommend. Hence, all hypothesises were verified. Implications : Based on these research results, this study proposed an academically valuable study for vegan menus in the restaurant industry. Moreover, this study contributes to provide various marketing implications using vegan menus in restaurants.

대학생의 지각된 진로장벽이 진로결정수준에 미치는 영향에서 진로성숙도의 매개효과

Objectives The purpose of this study was to verify the structural relationship between college students' perceived career barriers, career maturity, and career decision level, and the mediating effect of career maturity on the im-pact of perceived career barriers on career decision level. Methods For this purpose, 358 college students nationwide were selected as research subjects. A survey was conducted with the independent variables of perceived career barriers, mediating variables of career maturity, and career decision level selected as dependent variables. And the collected data were subjected to reliability ver-ification, descriptive statistics, correlation analysis, confirmatory factor analysis, path analysis, and bootstrap ver-ification using SPSS 26.0 & AMOS 18.0 programs to derive research results. Results The relationship between perceived career barriers, career decision level, and career maturity was found to be appropriate, and perceived career barriers and career maturity were found to be important variables in career decision level. In addition, career maturity was found to have a direct and indirect effect in the impact of psycho-logical career barriers on the career decision level, and career maturity was found to have an indirect effect in the impact of environmental career barriers on the career decision level. Conclusions The conclusions of this study are as follows. First, it was confirmed that career maturity plays a medi-ating variable in the impact of college students' perceived career barriers on their career decision level. Second, the validity of this study was supported by the need to examine the concepts separately rather than integrating career barriers to explore career decision levels. Third,It is expected that college students experiencing perceived career barriers will be able to use it as basic data to understand the level of career decision by exploring the path of career planning and selection for each task.

성인학습자의 성장 마인드셋, 자기주도 학습능력 및 학습 성과 간의 관계

Objectives The purpose of this study is to empirically analyze the growth mindset related to learning outcomes of adult learners participating in lifelong education programs, the structural relationship between self-directed learning ability, and the partial mediating effect of self-directed learning ability in the relationship between growth mindset and learning outcomes. Methods In order to solve the research purpose and research problem, 308 adult learners who are taking lifelong education program in university located in G city of Gyeongsangbuk-do were selected as research subjects. A sur-vey was conducted by selecting growth mindset as an independent variable, self-directed learning ability as a me-diating variable, and learning outcomes as a dependent variable. The collected data were analyzed using SPSS & AMOS 29.0 program for reliability verification, correlation, multiple regression analysis, confirmatory factor analy-sis, and covariance structure analysis. Results The potential variables of growth mindset, self-directed learning ability, and learning outcome perceived by adult learners showed a significant positive relationship, and the structural relationship between growth mind-set, self-directed learning ability, and learning outcome was also significant. In addition, it was confirmed that self-directed learning ability has a significant effect as a partial mediator in the relationship between growth mind-set and learning outcomes. Conclusions Growth mindset and self-directed learning ability are core competencies that adult learners should cultivate. Therefore, in order for adult learners who have accumulated life experiences for a long period of time and whose learning tendencies are fixed to adapt to the changing times and live a successful second life, it is neces-sary to find ways to strengthen growth mindset and self-directed learning ability.

Accuracy Assessment of NOAA IMS 4 km Products on the Tibetan Plateau with Landsat-8 OLI Images

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area.

Study on Static Biomechanical Model of Whole Body Based on Virtual Human.

Material handling tasks often lead to skeletal injury of workers. The whole-body static biomechanical modeling method based on virtual humans is the theoretical basis for analyzing the human factor index in the lifting process. This paper focuses on the study of humans' body static biomechanical model for virtual human ergonomics analysis: First, the whole-body static biomechanical model is constructed, which calculates the biomechanical data such as force and moment, average strength, and maximum hand load at human joints. Secondly, the prototype model test system is developed, and the real experiment environment is set up with the inertial motion capture system. Finally, the model reliability verification experiment and application simulation experiment are designed. The comparison results with the industrial ergonomic software show that the model is consistent with the output of the industrial ergonomic software, which proves the reliability of the model. The simulation results show that under the same load, the maximum joint load and the maximum hand load are strongly related to the working posture, and the working posture should be adjusted to adapt to the load. Upright or bent legs have less influence on the maximum load capacity of the hand. Lower hand load capacity is due to forearm extension, and the upper arm extension greatly reduces the load capacity of the hand. Compared with a one-handed load, the two-handed load has a greater load capacity.

Experimental and Simulated Verification of Power Cycling Reliability for Thin and Low Warpage Power Modules

Experimental and Simulated Verification of Power Cycling Reliability for Thin and Low Warpage Power Modules

Measurement of unsteady force on rotor blade surfaces in axial flow compressor

To assess the aerodynamic performance and vibration characteristics of rotor blades during rotation, a study of unsteady blade surface forces is conducted in a low-speed axial flow compressor under a rotating coordinate system. The capture, modulation, and acquisition of unsteady blade surface forces are achieved by using pressure sensors and strain gauges attached to the rotor blades, in conjunction with a wireless telemetry system. Based on the measurement reliability verification, this approach allows for the determination of the static pressure distribution on rotor blade surfaces, enabling the quantitative description of loadability at different spanwise positions along the blade chord. Effects caused by the factors such as Tip Leakage Flow (TLF) and flow separation can be perceived and reflected in the trends of static pressure on the blade surfaces. Simultaneously, the dynamic characteristics of unsteady pressure and stress on the blade surfaces are analyzed. The results indicate that only the pressure signals measured at the mid-chord of the blade tip can distinctly detect the unsteady frequency of TLF due to the oscillation of the low-pressure spot on the pressure surface. Subsequently, with the help of one-dimensional continuous wavelet analysis method, it can be inferred that as the compressor enters stall, the sensors are capable of capturing stall cell frequency under a rotating coordinate system. Furthermore, the stress at the blade root is higher than that at the blade tip, and the frequency band of the vibration can also be measured by the pressure sensors fixed on the casing wall in a stationary frame. While the compressor stalls, the stress at the blade root can be higher, which can provide valuable guidance for monitoring the lifecycle of compressor blades.

Perancangan Sistem Keamanan Rumah Menggunakan Face Recognition (Pengenalan Wajah) Berbasis Raspberry Pi

Talking about technology, currently computer technology is developing very rapidly and is a field that has a very important role in several aspects of human life, including in the field of security. Currently, many home or room security systems have been developed with several identity verification methods using a computer system, using keys, cards, passwords, and so on. However, this method still has shortcomings such as humans' limitations in remembering number combinations which makes the door inaccessible. Therefore, techniques for reliable and accurate identification or verification can be designed using biometric technology that utilizes the special characteristics of the human individual.

The Issues of Psychological Health Tests in Chinese High Schools

At present, adolescents are facing many challenges in mental health, and there are many problems with psychological test questions, so this paper aims to strengthen mental health education and interventions. There are many problems in the current psychological test questions, including insufficient professionalism, insufficient consideration of cultural differences, insufficient privacy protection, insufficient validity and reliability, excessive reliance on self-report, oversimplification of the interpretation of results, lack of timely updating and revision, suggestiveness of the questions, insufficient verification of reliability and validity, and failure to fully cover important aspects of mental health. These issues may affect the accuracy and reliability of test results and need to be continuously improved and resolved through scientific research and professional practice. When addressing mental health issues, schools should work with professionals to ensure that psychological tests are scientific and culturally appropriate, while updating test questions regularly. In addition, it combines quantitative and qualitative assessments, provides professional training, uses information technology to improve testing convenience, and establishes a feedback mechanism to optimize test questions. Resources and support are also needed after the exam to ensure that tests are designed without bias to help students address mental health challenges. Through these measures, the effectiveness of psychological testing can be improved, and the mental health and well-being of students can be promoted.