Expert Scoring Research Articles

Adaptation and Validation of the Supportive and Palliative Care Indicators Tool (SPICT): A Quantitative Methodological Study.

To translate the Supportive and Palliative Care Indicators Tool (SPICT) into Chinese and conduct preliminarily tests of its performance in hospitalized patients with cancer. A cross-sectional validation study conducted from January to March 2024. SPICT 2022 was translated in both directions, following the Brislin translation model, and the Chinese version culturally debugged through expert consultation and pre-testing. Content validity was evaluated by expert scoring. Tool internal consistency was evaluated using KR-20 coefficient, and retest reliability was evaluated using kappa coefficient. The screening performance was evaluated by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Of 388 hospitalized cancer patients included, approximately one-quarter had potential palliative care needs. Content validity of the Chinese version of SPICT was good, as were internal consistency and test-retest reliability. Accuracy (0.905), sensitivity (0.806), specificity (0.943), PPV (0.845), and NPV (0.926) for the Chinese version of SPICT indicated that it is an acceptable instrument. The Chinese version of SPICT can be applied for screening of palliative care needs in hospitalized patients with cancer in China. The Chinese version of SPICT had been adapted to assist clinicians or nurses in quickly identifying hospitalized patients with cancer who may have palliative care needs. This is conducive to help clinical team to start palliative care consultation, care goal discussion and (or) referral for patients in clinical practice. And it probably helps to advance integration between palliative care assessment and routine oncology care assessment. This study provided a screening tool for palliative care, with good validity and reliability, as well as excellent screening performance to facilitate palliative care need screening in clinical practice, promote palliative care referrals and improve patient quality of life. This study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. No patient or public contribution.

Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis

The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images. The framework involves four stages: image preprocessing, hand segmentation with UNet, joint identification via YOLOv7, and OSS prediction utilizing a custom Vision Transformer (ViT). Evaluation metrics included Intersection over Union (IoU), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Huber loss, and Intraclass Correlation Coefficient (ICC). The model was trained using stratified group 3-fold cross-validation on a dataset of 679 patients and tested externally on 291 subjects. The joint identification model achieved 99% accuracy. The ViT model achieved the best OSS prediction for patients with Sharp scores < 50. It achieved a Huber loss of 4.9, an RMSE of 9.73, and an MAE of 5.35, demonstrating a strong correlation with expert scores (ICC = 0.702, P < 0.001). This study is the first to apply a ViT for OSS prediction in RA. It presents an efficient and automated alternative for overall damage assessment. This approach may reduce reliance on manual scoring.

Psychometric Properties of the Critical Appraisal Tool for Anatomical Meta-Analysis.

The hallmark of evidence-based anatomy (EBA) is the anatomical meta-analysis (AMA). The Critical Appraisal Tool for Anatomical Meta-Analysis (CATAM) was recently published to enable users to appraise AMAs quickly and effectively. The tool is valuable for students and clinicians who need to judge the quality of AMAs, which informs clinical decision making and results in better patient care. Subjective measures of the tool's face and content validity have been established, but establishing its reliability provides a more objective measure of the instrument's dependability. This study investigated the interrater reliability (IRR) of the CATAM between novice and expert raters. Three graduate students and three professors (two anatomists and one pharmacist) read the original CATAM paper, and then had a post hoc meeting to discuss scoring with the tool. Three recent AMAs (published between 2017 and 2022) were randomly chosen from PubMed, and all six raters scored the papers blindly. The intraclass correlation coefficient (ICC) statistic was used to calculate the interrater reliability (IRR) between all scores, and then the ICCs between novice and expert scores were compared. Cronbach's alpha (internal consistency) of the CATAM was also calculated (SPSS 25, Armonk, NY). ICC for AMA-1 was 0.999 (95% CI, 0.997-0.999), p = 0.000, and alpha was 0.999. ICC for AMA-2 was 0.994 (95% CI, 0.988-0.998), p = 0.000, and alpha was 0.994. ICC for AMA-3 was 0.998 (95% CI, 0.995-0.999), p = 0.000, and alpha was 0.998. ANOVA showed no significant differences (p > 0.05) in mean ICCs between raters. The CATAM is a robust tool with excellent IRR (ICC > 0.990) and internal consistency (alpha > 0.990). No significant difference in ICC scores between novices and experts suggests the tool does not require prior expert knowledge to be effective. Now that the reliability of the CATAM is established, it can be more widely adopted by students and physicians worldwide to evaluate the quality of AMAs. The CATAM offers widespread applicability, and can be adopted in medical education, journal clubs, and clinical seminars to critically evaluate AMAs.

Innovation in Agricultural Water Pricing Systems in China Based on Irrigation Benefits

The comprehensive reform of agricultural water prices is an important component of China’s agricultural water conservation strategy and is highly important for ensuring national water security and sustainable agricultural development. Given the difficulty in raising water prices due to the limited carrying capacity of farmers in the reform, there is a pressing need to consider the implementation of agricultural water price sharing as a potentially viable strategy. Based on the grain production data from 2000 to 2018, the proportion of agricultural water prices borne by farmers and governments in different regions were calculated via the C-D production function method and the “Mitchell scoring + Expert scoring” method in the study. The results revealed that the average sharing coefficient of irrigation benefits for grain crops in China is 0.245. The sharing proportion of agricultural water prices for farmers in seven major geographical regions are ranked as follows: Northwest China (0.467) &gt; Central China (0.427) &gt; Southwest China (0.389) &gt; Northeast China (0.358) &gt; North China (0.319) &gt; East China (0.312) &gt; South China (0.163), while the sharing proportion of the government is 0.533, 0.573, 0.611 0.642, 0.681, 0.688, and 0.837. We proposed a systematic approach that directly ties cost distribution to the benefits received, and determined the proportion of agricultural water prices shared by farmers and governments, which is in line with the farmers’ economic interests and psychological demands. Furthermore, suggestions were proposed regarding the implementation of a rational agricultural water price-sharing policy.

Evaluation of the effectiveness of GASMAN anesthesia simulation software combined with case-based learning versus traditional lecture-based learning in inhalation anesthesia education.

To evaluate the effectiveness of integrating GASMAN anesthesia simulation software with case-based learning (IGC) compared to traditional lecture-based learning (LBL) in teaching inhalation anesthesia to undergraduate anesthesiology students. Fourth-year students from two academic years (2022, n = 110; 2023, n = 131) enrolled in a five-year anesthesiology program were assigned to either traditional lecture-based learning (LBL) or IGC groups. The LBL group received traditional lectures using PowerPoint slides, while the IGC group engaged with GASMAN anesthesia simulation software (a tool designed for anesthesia simulation and gas monitoring) combined with case-based learning. The cases used in the IGC group were structured around realistic clinical scenarios, simulating real-world challenges in inhalation anesthesia. These scenarios were integrated with the GASMAN software to provide interactive simulations, enhancing students' understanding of pharmacokinetics and pharmacodynamics. Teaching effectiveness was evaluated through expert assessments and student feedback, with learning outcomes compared via post-course assessments. The IGC group scored significantly higher in student evaluations in areas such as comprehending and mastering theoretical knowledge, resolving clinical challenges, nurturing clinical reasoning, increasing learning interest, enhancing learning efficiency, consolidating memory, improving analytical skills, and refining application proficiency (adjusted P < 0.001), however, there were no significant differences between the two groups in the improvement of practical skills. Post-course test scores were also higher in the IGC group for both total post-course test and subjective questions scores (adjusted P < 0.001), though no difference was found for objective question scores. After applying false discovery rate (FDR) correction, expert evaluation scores showed no significant differences between the two groups. The integration of GASMAN software with case-based learning significantly enhances the quality of inhalation anesthesia education by improving student engagement, critical thinking, and conceptual understanding. This approach demonstrates promise for advancing clinical education, although further research is needed to evaluate its long-term impact.

Assessing hard and loose "endpoints": comparison of patient and expert Bristol Stool Scale scoring of 2280 fecal samples.

Stool consistency is an important outcome measure to evaluate in the investigation of several gastrointestinal diseases. The Bristol Stool Scale (BSS) is one of the most commonly used tools for evaluation of stool consistency. BSS ranges from 1-7 and each score is assigned to a given consistency of the feces. Self-reported characterizations can differ from an expert evaluation, and the reliability of BSS is unclear. We aimed to evaluate the reliability of BSS by comparing patient scores with expert scores. Patients with inflammatory bowel disease collected stool samples throughout a 3-year follow-up. The stool´s consistency was evaluated with BSS by the patients and matched with an expert score. Agreement between patient and expert scores was assessed using Cohen's kappa. BSS scores from 2280 fecal samples collected from 992 patients at up to five time points were included. When all samples were compared, there was good to substantial agreement between patient and expert scores (Cohen's weighted kappa: 0.66-0.72). When the BSS scores were simplified and categorized as 1 (scores 1-2), 2 (scores 3-5) or 3 (scores 6-7), the agreement improved slightly (Cohen's weighted kappa: 0.73-0.77). When the scores from the first sample per patient were compared, the experts were more likely to assign higher scores compared to the patient. The proportion of the lowest assigned scores (1-2) was 12.1% for patients and 8.1% for experts. The agreement between patient and expert BSS scores is good to substantial, especially when the BSS scores are simplified into three categories.

Using Rubrics to Enhance Accuracy of Peer Assessment and Self-Judgment in Biology Learning

Abstract This study investigated the potential of rubrics to enhance science learning outcomes and the accuracy of peer and self-assessments. We randomly divided 230 upper-secondary students from nine classrooms in public schools into two groups: rubric and non-rubric groups. The research concentrated on five open-ended items pertaining to cell biology and applied a pretest-posttest design. The students participated in online peer assessment activities, including independently scoring and providing feedback for five unique responses per question. The findings revealed that both groups displayed (1) a notable correlation between peer and expert scores and (2) learning improvement, with the rubric group outperforming the non-rubric group in terms of effect size, and that (3) male students in the rubric group demonstrated a significant reduction in overconfidence, although the regression results suggested that gender did not influence the learning outcomes. This research supports the claim that rubrics can elevate formative assessment by enhancing learning outcomes and facilitating more reliable judgments.

Wine ratings and commercial reality

Abstract Is the quality of a 91-point wine significantly different from that of an 89-point wine? Which wines are underpriced relative to their evaluation of quality? This paper addresses these questions by constructing a novel wine rating system based on scores assigned by a panel of wine experts to a set of wines. Wines are classified in ranked disjoint quality equivalence classes using measures of statistically significant and commercially relevant score differences. The rating system is applied to the “Judgment of Paris” wine competition, to data of Bordeaux en-primeur expert scores and prices, and to expert scores and price categories of a large database of Italian wines. The proposed wine rating system provides an informative assessment of wine quality for producers and consumers and a flexible rating methodology for commercial applications.

Based on AHP, The Ecological Environment of Fenhe River Basin Is Evaluated

he evaluation of the ecological quality of the Fenhe River Basin is of great significance for the high-quality development of the Yellow River Basin and Shanxi Province. In this paper, the analytic hierarchy process (AHP) is used to evaluate the ecological environment of Fenhe River Basin. Firstly, based on the relevant research literature, the ecological environment evaluation index is determined, and the influencing factors of the evaluation index are determined. Secondly, the weight of different influencing factors is determined based on AHP method, and the weight of evaluation index is obtained. Finally, the ecological environment of Fenhe River Basin in different years was analyzed by weight analysis results and expert scoring method. The results show that the ecological environment evaluation indexes of Fenhe River Basin are : water loss, soil loss, water pollution and vegetation reduction; the influencing factors of the evaluation index are : rainfall factors, soil and water conservation measures, reservoir construction, industrial water use, coal mining, engineering construction, solid waste pollution, urban development, industrial structure change, population change; from 2000 to 2020, the ecological quality of the Fenhe River Basin continued to improve, indicating that the policies and measures adopted for ecological management were effective. The ecological deterioration near the main stream and tributaries of the middle and lower reaches of the Fenhe River is related to the pollution of mine environment and industrial sewage.

The research on dance motion quality evaluation based on spatiotemporal convolutional neural networks

With the rapid development of artificial intelligence and computer vision technology, dance action recognition and quality assessment have become a challenging and important research field. Traditional dance movement recognition methods often rely on manual feature extraction and expert scoring, which have problems such as strong subjectivity, low efficiency, and difficulty in handling complex movements. To address these issues, this study proposes a dance action recognition and quality assessment method based on Spatio Temporal Convolutional Neural Network (ST-CNN). Specifically, it includes a feature extraction network based on 3D spatiotemporal description operators and a multi-scale aggregated long short memory network for extracting spatial features of single frame images and fusing video sequence features composed of all frames. This approach can effectively capture dynamic changes in dance movements by combining information from spatial and temporal dimensions, thereby achieving more accurate recognition and evaluation. In addition, to address the high storage and computational requirements of convolutional neural networks during training, we integrated dance video sequences into an IoT architecture to address parallel recognition and evaluation of multiple videos. Finally, we validated the proposed method on the public datasets NTU-RGB 60 and NTU-RGB 120, and the experimental results demonstrated the effectiveness of the method.

Application of big data and artificial intelligence in visual communication art design.

In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications' reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.

Validation of automated detection of REM sleep without atonia using in-laboratory and in-home recordings.

To evaluate the concordance between visual scoring and automated detection of REM sleep without atonia (RSWA) and the validity and reliability of in-home automated-RSWA detection in REM sleep behavior disorder (RBD) patients and a control group (CG). Sleep Profiler signals were acquired during simultaneous in-laboratory polysomnography in 24 isolated RBD patients. Chin and arm RSWA measures visually scored by an expert sleep technologist were compared to algorithms designed to automate RSWA detection. In a second cohort, the accuracy of automated-RSWA detection for discriminating between RBD and CG (n = 21 and 42, respectively) was assessed in multi-night in-home recordings. For the in-laboratory studies, agreement between visual and auto-scored RSWA from the chin and arm were excellent, with intra-class correlations of 0.89 and 0.95, respectively, and substantial, based on Kappa scores of 0.68 and 0.74, respectively. For classification of iRBD patients versus controls, specificities derived from auto-detected RSWA densities obtained from in-home recordings were 0.88 for the chin, 0.93 for the arm, and 0.90 for the chin or arm, while the sensitivities were 0.81, 0.81 and 0.86, respectively. The night-to-night consistencies of the respective auto-detected RSWA densities were good based on intra-class correlations of 0.81, 0.79 and 0.84, however some night-to-night disagreements in abnormal RSWA detection were observed. When compared to expert visual RSWA scoring, automated RSWA detection demonstrates promise for detection of RBD. The night-to-night reliability of chin- and arm-RSWA densities acquired in-home were equivalent.

Suitability matching evaluation of the low-carbon regional integrated energy system based on the “resource-demand” balance

The integrated energy system (IES) stands out due to its advantages of multi-energy complementarity, green and efficiency. This paper aims to provide a practical model for suitable region site selection of low-carbon regional IES based on the “resource-demand” balance. A “resource-demand” suitability matching analysis indicator system is established. By using prospect theory and variable precision rough set theory, the decision-making preferences and the cognitive fuzziness influence of expert scoring were corrected respectively, and reasonable subjective weights were obtained. Then, the objective weights are obtained using the entropy method, and the optimal combined weights are obtained through the game theory weighting method. Finally, this paper establishes a “resource-demand” suitability matching model consisting of three sub models: similarity matching degree, numerical matching degree, and suitability matching degree. An empirical study is conducted on 16 regions in Beijing, and the results show that the numerical matching degree of “resource-demand” gradually decrease from the central urban regions to the surrounding regions. The similarity matching degree in most regions reaches the highest range (0.776,1.00). There are 4 regions with a suitability matching rating of perfect, 7 regions with a rating of great, 1 region with a rating of good, and 4 regions with a rating of poor. The suitability matching model established in this paper can guide energy project decision-makers to select regions with greater potential for the development of IES. In addition, adjusting the similarity matching coefficient and numerical matching coefficient can meet the diversified needs of decision-makers for the similarity matching degree and numerical matching degree of the “resource-demand” balance, providing support for energy project planning decisions.

Quantification of biosecurity measures in Mediterranean European seabass and gilthead sea bream farms

In the Mediterranean region, aquaculture drives economic growth and food supply through the production of seabass and sea bream. However, the region faces disease risks attributable to aquaculture intensification. Biosecurity is crucial for maintaining fish health and sustaining the aquaculture industry. This study adopted a novel risk-based scoring tool, originally developed for terrestrial animals, to benchmark biosecurity levels in seabass and sea bream. The tool considers all relevant aspects of aquaculture biosecurity, subdivided into external and internal biosecurity, with an accompanying questionnaire subjected to expert scoring. Land-based hatcheries and pre-growing facilities exhibited higher preventative measures, achieving overall biosecurity scores of 73.0 % and 75.0 %, respectively. These scores stem from stringent external and internal preventative measures, contrasting with lower overall biosecurity scores in land-based on-growing (50.0 %) and sea-based on-growing (59.0 %) facilities. Maintaining biosecurity during on-growing phases seems demanding due to uncontrolled interactions with the seawater environment and potential water quality issues. This benchmarking tool allows farmers to audit their biosecurity programs and pinpoint areas for improvement. These include vaccination, establishing biosecurity programs and keeping records, given the low scores across farm types. Improving vaccination practices, enhancing record keeping for traceability, and implementing robust biosecurity programs are crucial for improvement. Ultimately, the study emphasizes the need for tailored measures that foster synergy between external and internal components and enhance overall biosecurity in Mediterranean aquaculture.

Multi-objective optimization design of steel cross section of integrated supports and hangers based on NSGA-Ⅱ and MDOS

The stability and safety of building Mechanical, Electrical, and Plumbing (MEP) systems are significantly influenced by the steel section design of integrated supports and hangers. Traditional design methods, reliant on empirical steel section selection, are often inefficient and can result in unnecessary material usage. This paper introduces a novel multi-objective optimization approach that enhances the section design process by minimizing material costs and maximizing bending performance of supports and hangers. Firstly, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify an array of optimal cross-sectional designs that reconcile economic considerations with structural mechanical performance. Subsequently, in order to pinpoint the most suitable design from the resulting Pareto-optimal scheme, the Method of Determining the Optimal Scheme (MDOS) is proposed. MDOS harnesses expert scoring, the Continuous ordered weighted averaging (COWA) operator, game theory, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to objectively evaluate and prioritize design schemes according to stakeholder preferences and project-specific criteria. Finally, the optimization results are analyzed and the influence of different spacing of supports and hangers and different subjective weight factors on the selection of cross-section is studied. The findings reveal that the method can precisely determine all cross-section designs that adhere to the established constraints, achieving a 4.09-fold enhancement in design efficiency. It was observed that the two outlined objectives are predominantly affected by the height of the web. The distribution trend of the optimal solution value of the independent variable is almost the same under the condition of different values of the supports and hangers spacing. With the change of subjective weight, the cross section selection focuses on three optimal schemes. Therefore, the proposed method can be used as reference for similar steel design optimization.

The interaction of blockchain technology, audit process, and the International Financial Reporting Standards

Purpose This study aims to provide a framework for the interaction of blockchain technology, the audit process and International Financial Reporting Standards (IFRS). Design/methodology/approach This study was conducted in three phases. In the first phase, a meta-synthesis method was used until 2020. Out of 87 papers, a total number of 15 were selected, and 72 were excluded after an initial screening. In the second phase, 11 interviews were held with experts with sufficient experience and expertise in blockchain technology, accounting and auditing. These interviews were held between 2020 and 2021. The sampling was purposive (targeted) in type, along with snowball sampling. In the third phase, a combination of questionnaire-based method and the fuzzy Delphi method was used. The research population for this phase consisted of accounting professors, members of the Iranian Association of Certified Public Accountants and accounting and technology professionals. Following the selection of the Delphi panel, 35 questionnaires were collected between 2021 and 2022 for analysis. Findings The results showed that in line with the interaction of blockchain technology, audit process and IFRS, 52 indicators were confirmed in eight components. Moreover, “Verifiability,” “Timeliness” and “Predictive value” were the most influential factors, respectively, according to the conceptual model. In addition, higher average scores of experts’ views were related to “Transparency audit reports” and “Increasing the quality of the auditor’s judgment”, respectively. Originality/value Blockchain is a complex subject, and there has been very little published research on the intersection of IFRS and blockchain technology. It is crucial for the audit profession and technology professionals to focus on the interaction of blockchain technology with the qualitative characteristics of information. This is an area that has not been thoroughly researched yet. The interaction of blockchain technology, improvements in the audit process and adherence to IFRS is a significant development in accounting and auditing.

Analysis of landscape vegetation ecology and cellular structure in nature reserves based on an improved analytic hierarchy process

With the increasing severity of global environmental issues, natural reserves have become crucial areas for maintaining biodiversity and protecting natural ecosystems. The ecological status of landscape vegetation and the characteristics of cellular structures within these reserves have become focal points of research. However, traditional evaluation methods have limitations when dealing with the complex ecosystems of natural reserves, making it difficult to assess the relationship comprehensively and accurately between vegetation ecology and cellular structures. Therefore, this paper proposes an analysis of landscape vegetation ecology and cellular structure in natural reserves based on an improved Analytic Hierarchy Process (AHP). By incorporating fuzzy set theory and expert scoring mechanisms, the evaluation process is enhanced in terms of objectivity and precision. An evaluation index system for landscape vegetation ecology and cellular structure in natural reserves is constructed, covering aspects such as vegetation species richness, community structural complexity, and ecological functional stability. This systematic approach reflects the ecological status of vegetation and cellular structure characteristics, providing scientific support for targeted protection and management measures.

A novel FMECA method for CNC machine tools based on D-GRA and data envelopment analysis

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement.

Development of Virtual Reality Hydroelectric (VIRRIC) for teaching students about renewable energy

Understanding abstract physics theories, such as energy, can be challenging for students. Teachers also face the difficulty of meeting learning objectives within limited classroom time. The purpose of this research is to develop physics learning media to teach high school students about renewable energy. The learning media is presented as virtual reality, focusing on the energy conversion process in hydroelectric power plants. The goal is for students to apply their understanding of energy conversion concepts in hydroelectric power plants as one type of renewable energy source. The research process follows the ADDIE model, focusing on the analysis, design, and development stages. Data is collected through validation sheets completed by media experts, which provide valuable suggestions for revisions. The data was analyzed using descriptive statistical methods. It is known that at this level of education, the cognitive level required for understanding renewable energy material is C4 according to the revised Bloom's taxonomy. At the design stage, storyboards are used to frame the media development process, comprising a problem orientation scene and an exploration scene. After development, the VIRRIC media was validated based on visual aspects, interest, usefulness, accuracy, legitimacy, and structure. The final media expert validation score for the VIRRIC media is 0.87, categorized as highly valid. The lowest score was in the visual aspect, which was 0.77. This was due to some text information in the media being difficult to read. The text is observable when not using a gyro device, but becomes difficult to read when observed using a gyro device. Based on these results, the VIRRIC media is deemed suitable for testing in learning after making improvements.

Accident causation analysis of metal processing plants based on questionnaire and Bayesian network

Metal dust and metal waste from the process of metal processing are prone to fire and explosion, resulting in personal injury and property losses. It is necessary to explore the causes of fire and explosion in metal processing plants for the prevention and management of safety accidents. In this paper, the relevant information of fire and explosion accidents in metal processing plants is collected by questionnaire investigation. Then, three experts with authority in this field were chosen, and the experts’ weights were calculated. Secondly, Bayesian networks of metal processing plants were established according to accident information. In addition, questionnaire data and the expert score after fuzzing were applied to serve as probabilistic data for Bayesian networks. Finally, a safety management model was constructed based on the sensitivity analysis in the Bayesian network. It is shown that the probability of human and management errors is the largest in a plant. Compared to other factors, the probability of occurrence of an unsafe state of material is the smallest. Human errors and the ventilation system is not activated are the two factors that have the greatest influence on the accidents. This study provides assistance in improving safety in metal processing plants.