Traditional Evaluation Research Articles

Experiment-driven rationalism

Philosophers debate about which logical system, if any, is the One True Logic. This involves a disagreement concerning the sufficient conditions that may single out the correct logic among various candidates. This paper discusses whether there are necessary conditions for the correct logic; that is, I discuss whether there are features such that if a logic is correct, then it has those features, although having them might not be sufficient to single out the correct logic. Traditional rationalist arguments suggest that the necessary conditions of thought are necessary and sufficient conditions singling out the correct logical and mathematical theories. In the contemporary debate, Chalmers advocates a view along this line. Jago, analogously, suggests that the necessary conditions for thought—or, as he calls them, our basic epistemic expectations—single out a family of logical and mathematical theories. Warren and Williamson, on the other hand, argue that there are no necessary conditions of thought. I argue that there are necessary conditions for thought, and these are necessary but not sufficient conditions to be the correct logic; indeed, these are features that all logics—correct or incorrect—share. No view we can understand is ruled out by the necessary conditions for thought, but we cannot understand quite any view. Human linguistic and conceptual abilities are genetically constrained, and these constraints are our best guide to the boundaries of logic. Arguing for this, I tackle two dogmas of modern rationalism: namely, the view that the biological constraints of human cognition have no bearing on the boundaries of the epistemic space, and the view that the boundaries of thought coincide with the boundaries of language.

Редукция трансцендентного как основание имагинативного поворота в неопрагматизме

<span lang="EN-US">The article is devoted to the analysis of the conceptual and methodological grounds of the imaginative turn made within the framework of neopragmatist discourse. The specific works of R. Rorty, H. Putnam, and M. Johnson are used as examples to present the features of the interpretation of ethics, moral law, and rationalist tradition. The article emphasizes the process of metaphorization of key concepts of Kant's ethics by the neopragmatists. It is argued that for Putnam Kant's ethics has value first of all for its figurative component, the presence of colorful metaphors based on the core concepts of the Judeo-Christian tradition. In adding, it is suggested to critically evaluate the position of Johnson, who deals in detail with Kant's main moral law, the meaning of the categorical imperative. It is established on the basis of the fact that neopragmatists have reduced the transcendental dimension that the crucial role begins to be taken by the sensuality of the empirical subject and imagination as a moral moderator. The author refers to this phenomenon by the term "imaginative turn". Citing the works of Russian researcher E.N. Ustyugova the author makes a conclusion that for neopragmatists the human being turns out to be an empirical natural phenomenon, and aesthetics is the only fundament for moral judgments.</span>

Genetic evaluations of dairy goats with few pedigree data: different approaches to use molecular information.

One of the limitations of implementing animal breeding programs in small-scale or extensive production systems is the lack of production records and genealogical records. In this context, molecular markers could help to gain information for the breeding program. This study addresses the inclusion of molecular data into traditional genetic evaluation models as a random effect by molecular pedigree reconstruction and as a fixed effect by Bayesian clustering. The methods were tested for lactation curve traits in 14 dairy goat herds with incomplete phenotypic data and pedigree information. The results showed an increment of 37.3% of the relationships regarding the originals with MOLCOAN and clustering into five genetic groups. Data leads to estimating additive variance, error variance, and heritability with four different models, including pedigree and molecular information. Deviance Information Criterion (DIC) values demonstrate a greater fitting of the models that include molecular information either as fixed (genetic clusters) or as random (molecular matrix) effects. The molecular information of simple markers can complement genetic improvement strategies in populations with little information.

A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.

Regional investment risk evaluation based on compound risk correlation coefficient and migration learning approach

The high profit of regional investment is often accompanied by high risks, and the prior assessment of investment risks is conducive to avoid investment risks. However, the traditional evaluation methods usually only focus on individual risk factors, and it is difficult to evaluate and manage risks on the whole. Therefore, the study introduces deep learning algorithm, first build regional investment risk evaluation index system, then according to the characteristics of risk evaluation, design based on deep learning regional investment risk evaluation model, the final use parameter based migration learning algorithm and composite correlation coefficient to improve the evaluation model, solve the problem of insufficient training samples. The test results showed that the randomly selected 50 test samples with two different risk assessment models were 0.80 and 0.86, the deep learning algorithm tested 0.84, and the transfer learning improved model tested was 0.92, with the highest accuracy. This shows that the deep learning regional investment risk evaluation model improved by transfer learning effectively solves the problem of insufficient training data and improves the accuracy of prediction evaluation. In the field of venture capital, the model can help investors to evaluate and predict investment risks more accurately and improve the effect of investment decisions.

Application of BPNN’s intelligent ETQ model in network English teaching

Teaching evaluation is a key initiative to improve the quality of education and teaching. The research significance of this study is rooted in addressing the limitations of the traditional evaluation of teaching quality (ETQ) model, which often relies on a single evaluation index, exhibits a one-sided perspective, and suffers from pronounced subjectivity. In this context, this paper delves into the application of the backpropagation neural network (BPNN) to enhance and refine the ETQ model. The intelligent ETQ model was constructed and utilized in network English teaching to enhance the effect and quality of network English teaching. By analyzing the characteristics and needs of network English teaching, the advantages of BPNN in the ETQ were explored. The intelligent evaluation model was constructed, and its application effect in network English teaching was studied and evaluated. The total number of students satisfied with the BPNN based network English ETQ model was 151, with a total satisfaction rate of 75.5%. The ETQ model on the basis of BPNN was applied to network English teaching, which helped the average final score of Class 2 improve by 5.44 points compared to the division exam. The ETQ model based on BPNN was applied to network English teaching, which can improve the rationality of teaching evaluation and help improve students’ school English proficiency.

Proactive safety assessment of urban through-roads based on GPS data

Road safety is a worldwide issue, while urban roads account for a high share of serious road injuries, especially involving vulnerable road users, such as pedestrians or cyclists. Specifically, the safety of major roads through built-up areas (through-roads) is insufficient due to mixed traffic conditions including vulnerable road users, varying driving behaviour, and many disruptions, which are combined with excessive speed.. In this context, various traffic calming measures have been implemented to improve road safety, such as gateways or pedestrian refuge islands. However, the specific safety impacts of traffic calming combined with specific characteristics of through-roads are often unknown, since most traditional evaluations have been limited by small sample sizes of crash data, as well as wide variations in physical and road characteristics. To overcome the limitations of crash-based evaluations, we used the GPS-based data from a sample of 21 Czech and 12 Polish through-roads to develop the Speed-Safety Index, which combines speed, speed variance, and traffic volume. Our study has three novelty features: (1) To assess safety, we used speed and speed variance simultaneously. (2) To complete the missing link between specific traffic calming measures and safety, we validated the statistical relationship between the developed Speed-Safety Index and crash history. (3) To prove the usefulness of the developed index, we also showed its practical interpretation by proving the effect of spacing between traffic calming measures on safety. The index proved to be well correlated to crash frequency and it also proved the effect of spacing between traffic calming measures: the longer spacing, the smaller speed-reducing effect. The paper concludes with a discussion on the limitations, which we plan to address in further research, by moving from the current macro-perspective (Speed-Safety Index on the level of through-roads) to the micro-perspective (focusing on individual directions, locations, and traffic calming measures). We also plan to investigate the method’s applicability in different contexts. If the approach proves feasible, with reliable and valid results, it may become an alternative for a proactive network-wide road assessment, as called for by the European Road Infrastructure Safety Management Directive.

Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation

Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. However, standard evaluation tools merely possess the capacity to provide a general overview of a system’s performance; they lack consistency and effectiveness in their use, as evidenced by most recent studies on the topic. Furthermore, traditional evaluation techniques fail to detect potentially harmful data on small subsets. Moreover, they generally lack explainable features to interpret how such minor variations could affect the system’s performance. This proposal focuses on data clustering for recommender evaluation and applies a cluster assessment technique to locate such performance issues. Our new approach, named group validation , aids in spotting critical performance variability in compact subsets of the system’s data and unravels hidden weaknesses in predictions where such unfavorable variations generally go unnoticed with typical assessment methods. Group validation for recommenders is a modular evaluation layer that complements regular evaluation and includes a new unique perspective to the evaluation process. Additionally, it allows several applications to the recommender ecosystem, such as model evolution tests, fraud/attack detection, and the capacity for hosting a hybrid model setup.

Theory-driven neural network for total organic carbon content estimation from well logs

Abstract Total organic carbon content (TOC) is a crucial geochemical indicator for assessing the hydrocarbon generation potential of source rock formations. Traditional TOC evaluation methods employing well logs do not match well with measured data in complex formations. While intelligent algorithms have the potential to improve TOC estimation accuracy, they have poor petrophysical significance. In this study, we develop the theory-driven neural networks (TdNN) to extend TOC prediction accuracy via combining logging mechanism models into the input of neural network. TdNN have two ways for merging petrophysical models with intelligent model, specifically TdNN based on series connection (STdNN) and based on embedded way (ETdNN). STdNN processes log responses through logging mechanism models and subsequently inputs them to the neural network for TOC evaluation. ETdNN inputs log responses and calculation results from logging mechanism models to the neural network to estimate TOC. Two ways achieve more accurate prediction results than ordinary neural network via improving the quality of input data, analogous to applying data augmentation methods on intelligent models. This study utilizes the Schmoker method, 𝛿logR method, and multivariate regression method as logging mechanism models of TdNN. The prediction performance of the proposed models is tested using data from two shale reservoirs in the Longmaxi Formation and the Shahejie Formation. Results show that the proposed ETdNN achieves the highest prediction accuracy, meaning that the ETdNN is suitable for predicting TOC of shale formations.

A development Evaluation Index System of Public Sports Football University Course Based on OBE Concept in Guangdong Province

Background and Aim: The teaching evaluation index system is an essential part of teaching activities, which is related to the improvement of teaching quality and teaching effect, the stimulation of students 'learning motivation, and the improvement of teachers' teaching behavior. Thus, this research aimed to develop and articulate a development evaluation index system of public sports football university courses based on the OBE concept in Guangdong Province. Materials and Methods: The scope of this research covers all 66 undergraduate universities in Guangdong Province (including five independent colleges) using stratified random sampling. The first layer is the geographical distribution. The second layer is the school type, and the third is the target group, including teachers, students, and administrators. The calculated optimal sample size is 385 people. The research used a combination of interviews, questionnaires, and the specialist software SPSS. The research process included data collection and first draft indicators, index system confirmation using the Delphi method, and focus groups to examine the suitability and feasibility of the index system. Results: The results showed that a new evaluation index system based on the OBE concept was proposed. Five first-level indexes, 12 second-level evaluation indexes, and 29 three-level evaluation indexes are established. This index system focuses on students 'knowledge and skills, attitudes, values, and other non-cognitive factors and establishes an accurate and objective teaching evaluation index system that comprehensively reflects the public sports and football courses in Guangdong Province. Conclusions: Compared with the traditional evaluation index system, the original evaluation index system was reconfirmed and added the first-level indicators of "continuous teaching improvement and learning effect improvement", two second-level indicators, and six three-level indicators, which proved the uniqueness and innovation of this study. The new indicators encourage teachers to constantly reflect on and improve their teaching methods to achieve better results. They also enrich the evaluation index system of the OBE concept and provide a clear direction for the subsequent teaching evaluation index system of other sports projects.

Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods

Abstract This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts. Significance Statement As the general Navy meteorological and oceanographic (METOC) community engages in collaboration with the broader scientific community, our goal is to harness community tools like MET for the systematic evaluation of weather forecasts, with a specific focus on variables crucial to the Navy. Clouds, given their significant impact on visibility, hold particular importance in our investigations. Cloud forecasts pose unique challenges, primarily attributable to the intricate physics governing cloud development and the complexity of representing these processes within numerical models. Cloud observations are also constrained by limitations, arising from both top-down satellite measurements and bottom-up ground-based measurements. This study illustrates that, with a comprehensive understanding of community tools, cloud forecasts can be consistently verified. This verification encompasses traditional evaluation methods, measuring general qualities such as bias and root-mean-squared error, as well as newer techniques like spatial and object-based methods designed to account for displacement errors.

Qingxiangxing Baijiu sensory quality grade classification by 1H NMR and GC combined with multivariate statistical analysis

The traditional uniform artificial sensory evaluation makes it difficult to standardize the classification of different sensory quality grades of Baijiu. In this study, a total of 92 authentic Qingxiangxing Baijiu samples with 3 sensory quality grades were carefully collected. Gas chromatography (GC) was used to determine 46 main flavor components and proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to obtain hydrogen atom characteristic information of organic compounds. The principal component analysis (PCA), k-nearest neighbor (KNN) and linear discriminant analysis (LDA) models were conducted and fully validated by internal leave-one-out cross validation (LOOCV) and external repeated double random cross validation (RDRCV). The sensory quality grades of Qingxiangxing Baijiu were effectively classified by using GC and 1H NMR techniques coupled with PCA/KNN analysis with the averaged accuracy higher than 80%. In addition, synthetic minority oversampling technique (SMOTE) algorithm was successfully used to address the model overfitting problem caused by an unbalanced sample composition. This study demonstrated that 1H NMR and GC combined with multivariate statistical analysis were effective for sensory quality classification of Qingxiangxing Baijiu.

Rigorous Reliability Testing of an Encapsulated Thermal Pyrolytic Graphite (TPG) Heat Spreader for Passive Thermal Management Applications

Continued microelectronic miniaturization has resulted in higher power density, increasing the need for high performance thermal management. One possible solution for board-level thermal management is to fully encapsulate a high thermal conductivity material, such as Thermal Pyrolytic Graphite (TPG; ~1700 W/mK) in a metal (such as 6061 aluminum), producing an efficient passive heat spreader for thermal dissipation. The metal shell provides sufficient mechanical strength at a small detriment to the overall thermal conductivity. Reliability and consistent performance over time are critical for thermal management systems, especially in Defense and Aerospace applications. MIL-STD-883H provides test parameters for reliability involving exposure to mechanical shock (Method 2002.5), mechanical vibration (Method 2005.2), and thermal shock (Method 1011.9), with traditional evaluation criteria involving microelectronic device performance and/or a visual examination. For this work, the reliability of aluminum encapsulated TPG test coupons was evaluated with more demanding criteria: a visual examination, X-ray inspection, acoustic microscopy, and thermal conductivity performance testing, both before and after the previously discussed MIL-STD-883H exposures. The encapsulated coupons showed excellent robustness; the thermal conductivity properties remained constant with an average value around 965 W/mK, and the non-destructive imaging revealed excellent bond quality for the metal encapsulation, with no evidence of delamination. This more stringent evaluation criteria after MIL-STD-883H testing provides a better way to evaluate the reliability of passive thermal management systems. The study results also demonstrate the metal encapsulated TPG heat spreader as a superior thermal management solution with high performance and high reliability for demanding applications.

Bayesian Averaging Evaluation Method of Accelerated Degradation Testing Considering Model Uncertainty Based on Relative Entropy.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty.

Ceļš uz savu valsti Annas Brigaderes romānā “Kvēlošā lokā”

Anna Brigadere’s (1861–1933) novel “The Glowing Circle” (Kvēlošā lokā, 1928) is the writer’s only novel. The article examines how Brigadere recreated the path to the state’s founding in the novel and how she interpreted the events of that time. The aim of the article is to show in which aspects Brigadere creates the novel as the historical memory and cultural basis of the nation and in which aspects she expresses the views of her social group. The article uses materials in Latvian and German from the collections of the Museum of Literature and Music. The article uses case analysis – primary research case analysis and explanatory case analysis – to look at museum materials and their relationship to the text of the novel, as well as discourse analysis. Brigadere emphasises in the novel that the Republic of Latvia was established at a time when two countries (Russia and Germany) were trying to include it in their circle of interests, when people were involved in the war, and when it gradually became clear that only their own country could save the nation. In the novel, Brigadere opposes social-democratic and communist views. With the final solution of the novel, she joins the positivist tradition of Latvian literature.

Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance; during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results.

Research Progress in Traditional Applications, Phytochemistry, Pharmacology, and Safety Evaluation of Cynomorium songaricum.

Cynomorium songaricum Rupr. (CSR) belongs to the family Cynomoriaceae. It is a perennial succulent parasitic herb with a reddish-brown coloration, predominantly submerged in sand and lacking chlorophyll. Traditionally, it has been used in ethnic medicine to treat various diseases, such as gastric ulcers, indigestion, bowel movements, and improving sexual function. To comprehensively collect CSR data, extensive literature searches were conducted using medical, ecological, and scientific databases such as Google Scholar, PubMed, Science Direct, Web of Science, and China National Knowledge Infrastructure (CNKI). This article summarizes and categorizes research on the uses, phytochemical characteristics, pharmacological activities, and toxicity of ethnic medicine, with the aim of establishing a solid foundation and proposing new avenues for exploring and developing potential applications of CSR. So far, a total of 98 compounds have been isolated and identified from CSR, including flavonoids, terpenes, steroids, and other compounds. It is worth noting that flavonoids and polysaccharides have significant antioxidant and anti-inflammatory properties. In addition, these compounds also show good application prospects in anti-tumor, antioxidant, anti-aging, anti-fatigue, anti-diabetes, and other aspects. Although extensive progress has been made in the basic research of CSR, further research is still needed to enhance the understanding of its mechanism of action and explore more unknown compounds. Our review indicates that CSR has broad prospects and deserves further research.

A decentralized and security-enhanced professional title evaluation system in universities under mobile Internet of Things

Digital online application is a commonly used method for professional title evaluation in colleges and universities. With the rapid development of the mobile Internet of Things, the fastest way for users to access the evaluation system is through mobile devices. However, this also poses security challenges to the traditional professional title evaluation system. Therefore, this paper proposes a decentralized and security-enhanced digital title evaluation system in mobile IoT. Since the system uses blockchain to implement a decentralized review process, the transparency and fairness of the review process are guaranteed. At the same time, the review data and reviewer information in the system are encrypted and stored in the distributed network to ensure the security and non-tamperability of the data. All review records are recorded on the blockchain, and anyone can view and verify the legitimacy of the review results. Additionally, the system incorporates security enhancement mechanisms such as identity verification, smart contracts, and auditing functions to improve the credibility of the evaluation process and prevent cheating under mobile IoT. Experiments have shown that this system has important application value in the field of professional title review in universities, which can improve review efficiency, reduce human intervention, and provide strong support for the credibility of review results.

Dynamic Assessment of Photovoltaic-Storage Integrated Energy Stations Health Incorporating Subjective and Objective Characteristics

Photovoltaic-storage integrated systems, which combine distributed photovoltaics with energy storage, play a crucial role in distributed energy systems. Evaluating the health status of photovoltaic-storage integrated energy stations in a reasonable manner is essential for enhancing their safety and stability. To achieve an accurate and continuous assessment of the health status of photovoltaic-storage integrated energy stations, a dynamic evaluation method is proposed in this study. This method integrates both subjective and objective characteristics. Initially, considering the evaluation needs of low-carbon operation and health status for photovoltaic-storage integrated energy stations, a comprehensive health status evaluation system is developed. The significance of each indicator is subjectively analyzed, while also considering objective characteristics and sensitivity of indicators. The integration of subjective and objective characteristics is achieved using principles from game theory. Subsequently, through the establishment of the Grey-TOPSIS evaluation model, both positive and negative correlations of the health status of photovoltaic-storage integrated energy stations are determined, resulting in the derivation of a health status vector. Furthermore, the introduction of time-weight vectors and the incorporation of a time dimension enable dynamic evaluation and the comprehensive observation of health status. Finally, the scientific validity and effectiveness of the proposed evaluation method are demonstrated through practical examples, with comparisons made to traditional evaluation methods. The results clearly indicate that this method offers higher sensitivity when evaluating the health of photovoltaic-storage integrated energy stations.

Risk assessment for the liquefied petroleum gas filling industry using fuzzy logic and hazard and operability

The object of this study is to integrate Hazard and Operability (HAZOP) analysis and Fuzzy Logic to improve risk assessment in the Liquefied Petroleum Gas (LPG) filling industry. the research introduces a new approach to resolve the uncertainties and imprecise information inherent in traditional risk evaluation methods. Using HAZOP, a qualitative analysis conducted by the experts of industry identifies potential process deviations and hazards, categorized by impact type such as fire, explosion, and environmental damage. Fuzzy Logic, quantifies these risks by evaluating the likelihood and consequences of accidents using linguistic variables like "high," "medium," and "low." The simulation of different parameters considers various scenarios, including the interplay of consequence severity, event frequency, and detection capability on overall risk using model fuzzy-Hazop. The results provide more precision and actionable risk assessments, showing the potential of the combination of Hazop and fuzzy logic for safety measurement in the LPG filling process. The study concludes HAZOP with fuzzy logic evaluates risks in uncertain conditions, offering an alternative for clearer, decision-making in risk-based design processes.