Expert Scoring Research Articles

Relationship of adaptive expertise of health professions educators with age, experience, academic rank, and their work performance during an altered academic environment

BackgroundHealth professionals and health professions educators (HPEs) worldwide were confronted by the COVID-19 pandemic, which disrupted standard practice and forced HPEs to develop creative, alternative modes of training and education. The ability of people to work successfully and efficiently in non-standard situations can be called adaptive expertise in which people quickly overcome changes in work requirements using their expert knowledge in novel ways. The objectives of the current study were to investigate how the adaptive expertise of a group of HPEs influenced perceived work performance in a non-standard situation and to see whether there were relationships between the level of adaptive expertise and academic ranking and work experience of HPEs.MethodsA descriptive, cross-sectional, single-site study was conducted using a self-reported study tool about adaptive expertise developed by Carbonell et al. (2016), and three questions were asked about participants’ perceptions of work performance, amount of work done, and teaching quality. The sample consisted of HPEs from the University of Twente, Netherlands.ResultsAmong 123 eligible participants, 40 individuals completed the survey. Kaiser-Meyer-Olkin and Bartlett’s Test of Sphericity indicated the adequacy of the sample size (KMO = 0.633, P < 0.0001). Participants were lecturers, senior lecturers, assistant professors, associate professors and full professors. The average adaptive expertise score of the sample was 4.18 ± 0.57 on a scale from 1 (low) to 5 (high). The domain and innovative skills are the principal distinct dimensions of adaptive expertise among HPEs. Professors showed higher adaptive expertise scores than the other ranks. Statistically significant correlations were found between scores of adaptive expertise and perceived work performance (r = 0.41, p < 0.05 and academic ranking (r = 0.42, p < 0.05). Adaptive expertise scores were not associated with work experience or HPEs’ age.ConclusionsOur finding of a lack of relationships between self-reported level of adaptive expertise and experience and age but significant relationships with work performance and academic ranking of HPEs suggests that adaptive expertise is not auto-generated or acquired with seniority and experience but is a ‘mastery’ that should be developed deliberately.

Uncertainty analysis of impact factors for a comprehensive assessment of dam failure consequence under earthquake effects

ABSTRACT Utilizing cloud models to analyze the uncertainty and consequences of dam failure factors. Enhanced Analytic Hierarchy Process (AHP) with a scale criterion based on index scale and an expert score constraint mechanism. Focus on the downstream area of the Zipingpu Dam for assessing the uncertainty of dam comprehensive evaluation factors. The final outcome is represented by three numerical eigenvalues of the cloud model, determining the weights of each factor in the evaluation index system. This approach offers a novel method for the comprehensive evaluation of dam failure consequences.

Role preferences for ethnic autonomous region government in emergency management in China

China's ethnic autonomous regions, inhabited by diverse ethnic groups and located near national borders or at provincial intersections, confront complex natural, relatively underdeveloped economic, and ethnic harmony challenges. Governments balance central government-assigned tasks and ethnic harmony, showing varied responses: some prioritize stability, others focus on management, and some are indifferent to local emergencies. Given this phenomenon, why do governments in these regions vary in their responses to emergencies? This research, based on government role theory, examines how role preferences influence government behavior in emergency management, identifying two key roles: economic man and social man, and establishes a four-quadrant role analysis framework: : local guardian, political competitor, moral paragon, and indifferent bystander. Grounded theory explains these roles and evaluation indicators, with evaluations from 44 scholars gathered via questionnaire, and the indicator weights determined using the Analytic Hierarchy Process. The weight matrix then calculates economic man (X) and social man (Y) values for 272 cases based on expert scores. The research results reveal that (1) these governments exhibit an overall role preference of “local guardian > political competitor > indifferent bystander > moral paragon” in emergency management. (2) This ranking also reflects their preference for the economic man role. (3) Factors contributing to this ranking include cultural differences, administrative contracting, and performance tournaments within the Chinese system and administrative ethics. These findings provide theoretical insights into emergency management role preferences in multi-ethnic, multi-racial, and even multicultural regions, ultimately aiming to enhance local government emergency management efficiency, optimize policy formulation, and ensure community resilience.

DEVELOPMENT OF FLAT-BUILT DIGITAL COMIC MEDIA BASED ON ETHNOPEDAGOGY TO IMPROVE THE MATHEMATICAL CONNECTIONS OF LOWER CLASS PRIMARY SCHOOL STUDENTS

Meaningful and enjoyable learning is a strategy developed by teachers to optimize learning objectives. This strategy encompasses various aspects, including the approach used, material delivery, engaging instructional media, and creating a pleasant learning environment (Tisza &amp; Markopoulos, 2021). The research was conducted using the Research and Development (R&amp;D) approach, commonly known as development research that results in a product. The purpose of this study was to develop a digital comic-based flat geometry learning media grounded in ethno-pedagogy to enhance elementary school students’ mathematical connections in lower grades. The ADDIE model, developed by Dick and Carey in 1996, was employed as the framework for designing the instructional system (Mulyanitiningsih, 2016).The research outcomes, based on expert validation and field testing, indicate that the digital comic-based flat geometry learning media grounded in ethno-pedagogy aligns well with usability, appropriateness, accuracy, and suitability. The expert validation resulted in an 86% score, with an average material expert score of 96%, categorized as ‘Highly Suitable.’ Additionally, the language experts provided an average score of 82.5%, categorized as ‘Suitable.’ Overall, the combined scores from the experts yielded an 88.16% rating of ‘Highly Suitable’ for use with elementary school students. Based on the effectiveness test, the digital comic-based flat geometry learning media grounded in ethno-pedagogy demonstrated a positive impact on mathematical connections. The N-Gain calculation resulted in an average N-Gain score of 0.7061, categorized as "High.”

Toward 3D facial analysis for recognizing Mendelian causes of autism spectrum disorder.

Recognizing Mendelian causes is crucial in molecular diagnostics and counseling for patients with autism spectrum disorder (ASD). We explored facial dysmorphism and facial asymmetry in relation to genetic causes in ASD patients and studied the potential of objective facial phenotyping in discriminating between Mendelian and multifactorial ASD. In a cohort of 152 ASD patients, 3D facial images were used to calculate three metrics: a computational dysmorphism score, a computational asymmetry score, and an expert dysmorphism score. High scores for each of the three metrics were associated with Mendelian causes of ASD. The computational dysmorphism score showed a significant correlation with the average expert dysmorphism score. However, in some patients, different dysmorphism aspects were captured making the metrics potentially complementary. The computational dysmorphism and asymmetry scores both enhanced the individual expert dysmorphism scores in differentiating Mendelian from non-Mendelian cases. Furthermore, the computational asymmetry score enhanced the average expert opinion in predicting a Mendelian cause. By design, our study does not allow to draw conclusions on the actual point-of-care use of 3D facial analysis. Nevertheless, 3D morphometric analysis is promising for developing clinical dysmorphology applications in diagnostics and training.

Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.

Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury – a comparison of an image extraction and classification system to expert scoring

BackgroundWalking impairment is a common disability post acquired brain injury (ABI), with visually evident arm movement abnormality identified as negatively impacting a multitude of psychological factors. The International Classification of Functioning, Disability and Health (ICF) qualifiers scale has been used to subjectively assess arm movement abnormality, showing strong intra-rater and test-retest reliability, however, only moderate inter-rater reliability. This impacts clinical utility, limiting its use as a measurement tool. To both automate the analysis and overcome these errors, the primary aim of this study was to evaluate the ability of a novel two-level machine learning model to assess arm movement abnormality during walking in people with ABI.MethodsFrontal plane gait videos were used to train four networks with 50%, 75%, 90%, and 100% of participants (ABI: n = 42, healthy controls: n = 34) to automatically identify anatomical landmarks using DeepLabCut™ and calculate two-dimensional kinematic joint angles. Assessment scores from three experienced neurorehabilitation clinicians were used with these joint angles to train random forest networks with nested cross-validation to predict assessor scores for all videos. Agreement between unseen participant (i.e. test group participants that were not used to train the model) predictions and each individual assessor’s scores were compared using quadratic weighted kappa. One sample t-tests (to determine over/underprediction against clinician ratings) and one-way ANOVA (to determine differences between networks) were applied to the four networks.ResultsThe machine learning predictions have similar agreement to experienced human assessors, with no statistically significant (p < 0.05) difference for any match contingency. There was no statistically significant difference between the predictions from the four networks (F = 0.119; p = 0.949). The four networks did however under-predict scores with small effect sizes (p range = 0.007 to 0.040; Cohen’s d range = 0.156 to 0.217).ConclusionsThis study demonstrated that machine learning can perform similarly to experienced clinicians when subjectively assessing arm movement abnormality in people with ABI. The relatively small sample size may have resulted in under-prediction of some scores, albeit with small effect sizes. Studies with larger sample sizes that objectively and automatically assess dynamic movement in both local and telerehabilitation assessments, for example using smartphones and edge-based machine learning, to reduce measurement error and healthcare access inequality are needed.

Improving Concepts Understanding Of Class V Students With The Utilization Of Geogebra Learning Media

The purpose of this research is to create a learning media for the concept of geogebra to improve students' mathematical understanding of the material of spatial structure. The things tested in this study were validity, practicality, and effectiveness. This research method is RD with a 4-D model, namely defining, designing, developing and distributing. However, in this study only up to the development stage. Based on the research that has been done, the result is that the average score of media experts is 3.6, material experts are 3.6, which means the results are very good. Furthermore, the practicality score obtained from the teacher's response questionnaire is 89,93% and the student's is 78,67%. Furthermore, the results of the effectiveness of learning media get 90% of students who get complete learning using geogebra learning media.

Evaluating outlier probabilities: assessing sharpness, refinement, and calibration using stratified and weighted measures

An outlier probability is the probability that an observation is an outlier. Typically, outlier detection algorithms calculate real-valued outlier scores to identify outliers. Converting outlier scores into outlier probabilities increases the interpretability of outlier scores for domain experts and makes outlier scores from different outlier detection algorithms comparable. Although several transformations to convert outlier scores to outlier probabilities have been proposed in the literature, there is no common understanding of good outlier probabilities and no standard approach to evaluate outlier probabilities. We require that good outlier probabilities be sharp, refined, and calibrated. To evaluate these properties, we adapt and propose novel measures that use ground-truth labels indicating which observation is an outlier or an inlier. The refinement and calibration measures partition the outlier probabilities into bins or use kernel smoothing. Compared to the evaluation of probability in supervised learning, several aspects are relevant when evaluating outlier probabilities, mainly due to the imbalanced and often unsupervised nature of outlier detection. First, stratified and weighted measures are necessary to evaluate the probabilities of outliers well. Second, the joint use of the sharpness, refinement, and calibration errors makes it possible to independently measure the corresponding characteristics of outlier probabilities. Third, equiareal bins, where the product of observations per bin times bin length is constant, balance the number of observations per bin and bin length, allowing accurate evaluation of different outlier probability ranges. Finally, we show that good outlier probabilities, according to the proposed measures, improve the performance of the follow-up task of converting outlier probabilities into labels for outliers and inliers.

Aerial low‐altitude remote sensing and deep learning for in‐field disease incidence scoring of virus yellows in sugar beet

AbstractThis study investigates the potential of high‐resolution (&lt;0.5 cm/pixel) aerial imagery and convolutional neural networks (CNNs) for disease incidence scoring in sugar beet, focusing on two important aphid‐transmitted viruses, beet mild yellowing virus (BMYV) and beet chlorosis virus (BChV). The development of tolerant sugar beet cultivars is imperative in the context of increased disease management concerns due to the ban on neonicotinoids in the European Union. However, traditional methods of disease phenotyping, which rely on visual assessment by human experts, are both time‐consuming and subjective. Therefore, this study assessed whether aerial multispectral and RGB images could be harnessed to perform automated disease ratings comparable to those performed by trained experts. To this end, two variety trials were conducted in 2021 and 2022. The 2021 dataset was used to train and validate a CNN model on five cultivars, while the 2022 dataset was used to test the model on two cultivars different from those used in 2021. Additionally, this study tests the use of transformed features instead of raw spectral bands to improve the generalization of CNN models. The results showed that the best CNN model was the one trained for BMYV on RGB images using transformed features instead of conventional raw bands. This model achieved a root mean square error score of 11.45% between the model and expert scores. These results indicate that while high‐resolution aerial imagery and CNNs hold great promise, a complete replacement of human expertise is not yet possible. This research contributes to an innovative approach to disease phenotyping, driving advances in sustainable agriculture and crop breeding.

A climate vulnerability assessment of the fish community in the Western Baltic Sea

Marine fisheries are increasingly impacted by climate change, affecting species distribution and productivity, and necessitating urgent adaptation efforts. Climate vulnerability assessments (CVA), integrating expert knowledge, are vital for identifying species that could thrive or suffer under changing environmental conditions. This study presents a first CVA for the Western Baltic Sea's fish community, a crucial fishing area for Denmark and Germany. Characterized by a unique mix of marine, brackish, and freshwater species, this coastal ecosystem faces significant changes due to the combined effects of overfishing, eutrophication and climate change. Our CVA involved a qualitative expert scoring of 22 fish species, assessing their sensitivity and exposure to climate change. Our study revealed a dichotomy in climate change vulnerability within the fish community of the Western Baltic Sea because traditional fishing targets cod and herring as well as other species with complex life histories are considered to face increased risks, whereas invasive or better adaptable species might thrive under changing conditions. Our findings hence demonstrate the complex interplay between life-history traits and climate change vulnerability in marine fish communities. Eventually, our study provides critical knowledge for the urgent development of tailored adaptation efforts addressing existing but especially future effects of climate change on fish and fisheries in the Western Baltic Sea, to navigate this endangered fisheries systems into a sustainable future.

MEREC-MABAC method based on cumulative prospect theory for picture fuzzy sets: Applications to wearable health technology devices

Since there is a causal connection between the performance of the alternative with criteria removed and the assessment of the criteria weights, Method based on the Removal Effects of Criteria (MEREC) can reflect the objectivity of statistical data from this new perspective. Multi-Attributive Border Approximation area Comparison (MABAC) approach considers the subjective psychological characteristics of decision makers (DMs), particularly personal preferences and cognitive limitations, through expert scoring. Cumulative prospect theory (CPT) takes into account both potential gains and losses. We propose applying the MEREC method to picture fuzzy sets (PFS) for the first time to determine criteria weights. We suggest an alternate ranking approach for the CPT-MABAC algorithm based on the Hamming distance of the picture rejection membership degree that corresponds to four behavioral states: acceptance, opposition, abstention, and rejection. We develop the PFS-MEREC-CPT-MABAC evaluation framework to tackle the multiple attribute decision-making (MADM) problems while evaluating the performance of wearable health technology devices (WHTDs). The model achieves a compromise between objectivity and subjectivity as well as capturing the psychological behavior of the experts. Finally, in order to determine the soundness and effectiveness of this integrated research approach, sensitivity and comparison analysis are done to compare the results of its operation with those of existing models.

Development of Website based Multiple Representation on Buffer Solution Topic in Chemistry Learning

Buffer solution is one of chemistry difficult topics. It contains theory, calculation, and experimentation, so it requires good analysis and understanding of concepts. The results of interviews with students in Bintan Regency showed that the sub material of determining buffer solution components and analyzing the working principles of buffer solutions was the most difficult part because it was abstract. Multiple representations on the website media will visualize abstract things from the topic of buffer solution so as to increase the understanding of concepts from students. The research aims to produce multiple representation website on buffer solution topics and to determine its feasibility, practicality, and readability. The research method used is research and development (RD) following the 4D development model by Thiagrajan (define, design, develop, and disseminate). The sample research used 21 students and five chemistry teachers in Bintan Regency to collect user responses related to practicality and readability. The data analysis calculates the average score of the validation and response values and classifies them into the available categories. The results of the validation show that the average score of material experts is 63.5 (learning, materials, multiple representations, and evaluation aspects), while the average score of media experts is 71.5 (graphic design, software engineering, and media selection criteria aspects), which are classified as very good. The results of practicality and readability responses are 119 (learning, materials, multiple representations, evaluation, graphic design, software engineering, and media selection criteria) and 70.5 (learning, graphic design, and software engineering), which are very good categories

Development of Factory-Based Learning Modules in Clothing Production Units

The aim of research and development is to develop a factory-based learning module in fashion production units in vocational schools that can be used in the classroom or for independent learning. This study was developed based on an analysis of the needs of class XI fashion students at SMK Negeri 1 Beringin. Development research using the 4D method by Thiagarajan. Has the Define, Design, Develop, and Disseminate research stages. The development process involves input from material experts, media experts, and instructional experts, as well as acceptability testing from students. The results of the research show that (1) the Teaching Factory-Based Learning Module in the Clothing Production Unit received a material expert score in the content feasibility aspect of 3.43, or 85.83% (very feasible). In the feasibility aspect of the presentation, it was 3.23, or 80.68% (feasible). Language assessment aspect: 3.54, or 88.46% (very decent). Instructional expert validation aspect: 3.32, or 82.93% (feasible). Media expert aspects of graphic feasibility are 3.68, or 91.94% (very feasible). Each validation shows that the product is suitable for use in learning. (2) The Teaching Factory Based Learning Module in the Clothing Production Unit is tested using the N-Gain Score to see the effectiveness of media use on learning outcomes. The research results showed a figure of 0.57 (medium effectiveness). Research conclusions: The Teaching Factory-Based Learning Module in the Clothing Production Unit at SMK Negeri 1 Beringin is suitable for use and is effective in improving learning outcomes.

Risk assessment of maritime autonomous surface ships collisions using an FTA-FBN model

Maritime autonomous surface ships (MASS), presenting the future of maritime transport, are attracting increasing attention from the international maritime community. The collision risk analysis of MASS reveals unsolved challenges, which without appropriate solutions, will result in the error prone development of the relevant risk control measures and policies. Among the challenges, two significant ones in the existing literature are the lack of historical failure data to realise quantitative risk assessment, and 2) the complex causal relationship among the relevant risk factors. This paper aims to develop a new Fault Tree Analysis-Fuzzy Bayesian Network (FTA-FBN) model to conduct the collision risk assessment of MASS with uncertainty in data. First, it establishes a causal relationship among the risk factors through an FTA. Secondly, mapping the obtained FTA diagram into a BN allows fault diagnosis and the identification of the most important factors influencing MASS collisions. In this process, a survey is conducted to collect the primary data for configuring the conditional probabilities of the relevant influential factors and quantifying the developed BN for risk diagnosis and prediction. Finally, the new model is verified by using sensitivity analysis and three axioms and then applied to conduct scenario-based risk prediction and diagnosis to generate insightful findings to guide MASS navigation safety. The results demonstrate that the FTA-FBN model realizes the simplification of the expert scoring process, reduces computational complexity, and addresses the challenge of constructing causal relationships between MASS collisions and their risk factors due to the scarcity of historical accident data. Additionally, the BN backward reasoning identifies key collision risks, including external physical attacks, inadequate training of shore-based operators, insufficient maintenance of ship equipment and systems, and cyber-security threats. The new model when being adapted, can provide a reference for the formulation of safe navigation policies and provide important insights for shipping companies to ensure the safe navigation of their ships and shipbuilders to optimise ship design.

Pengembangan Modifikasi Bola Pompa Angin Dalam Permainan Bola Kasti Pada Kelas V SDN Glagahsari 1 Kabupaten Pasuruan

The lack of interest among students in the game of kasti is the background problem addressed in this research. The researcher utilized a modified air pump ball as a substitute for a tennisball. The air pump ball is a type of ball made from synthetic rubber, thus eliminating the potential for pain upon impact with the body. The modification of the air pump ball is intended to increase students' interest in playing kasti in the fifth-grade class at SDN Glagahsari 1 in Pasuruan Regency. This research employs the ADDIE model, a development method consisting of five stages: Analyze, Design, Development, Implementation, and Evaluation. Data collection involves distributing questionnaires to content experts, media experts, small-scale trials, and large-scale trials. Based on the validation results from content and media experts, with each expert scoring 88% and 94%, respectively, it is indicated that the modified air pump ball is valid and suitable for use without revisions. Before conducting the large-scale trial, the researcher distributed before trial questionnaires to assess students' knowledge of softball, revealing an average percentage of 50%, reinforcing the lack of interest among students in the game. After the large-scale trial, after trials questionnaires were distributed, indicating an average percentage score of 90% among students. This demonstrates that the modified air pump ball is highly effective in enhancing students' interest in kasti game in the fifth-grade class at SDN Glagahsari 1, Pasuruan Regency.

EXPLAIN THE CONTENT OF DEVELOPING AUTHENTIC ASSESSMENT INSTRUMENTS IN INDONESIAN LANGUAGE LEARNING VIII SMP NEGERI 2 BINJAI

This study aims to, (1) develop the form of authentic assessment instruments in the learning news text of SMP Negeri 2 Binjai, (2) describe the process of developing authentic assessment instruments in the learning news text of grade VIII SMP Negeri 2 Binjai, (3) describe the feasibility of authentic assessment instruments in the learning news text of grade VIII SMP Negeri 2 Binjai. The type of research used in this research is research and development (R&amp;D). The research instruments used in this study were material expert validation questionnaires, languages, and teacher response tests using the Likert scale. The results of this study proved that authentic assessment instruments in class VIII SMP Negeri 2 news text learning were included in the "excellent" and "suitable for use" categories with material expert scores of 77.5%, linguists 75%, and 95% teacher response test. Therefore, authentic assessment instruments in learning class VIII news texts of SMP Negeri 2 Binjai are worth using.

Establishment of in-hospital nutrition support program for middle-aged and elderly patients with acute decompendated heart failure

ObjectiveTo construct a nutrition support program for middle-aged and elderly patients with acute decompensated heart failure (ADHF) during hospitalization.MethodsBased on the JBI Evidence-Based Health Care Model as the theoretical framework, the best evidence was extracted through literature analysis and a preliminary nutrition support plan for middle-aged and elderly ADHF patients during hospitalization was formed. Two rounds of expert opinion consultation were conducted using the Delphi method. The indicators were modified, supplemented and reduced according to the expert’s scoring and feedback, and the expert scoring was calculated.ResultsThe response rates of the experts in the two rounds of consultation were 86.7% and 100%, respectively, and the coefficient of variation (CV) for each round was between 0.00% and 29.67% (all < 0.25). In the first round of expert consultation, 4 items were modified, 3 items were deleted, and 3 items were added. In the second round of the expert consultation, one item was deleted and one item was modified. Through two rounds of expert consultation, expert consensus was reached and a nutrition support plan for ADHF patients was finally formed, including 4 first-level indicators, 7 s-level indicators, and 24 third-level indicators.ConclusionThe nutrition support program constructed in this study for middle-aged and elderly ADHF patients during hospitalization is authoritative, scientific and practical, and provides a theoretical basis for clinical development of nutrition support program for middle-aged and elderly ADHF patients during hospitalization.

Well Selection for CO2 Huff-n-Puff in Unconventional Oil Reservoirs Based on Improved Fuzzy Method

The implementation of CO2 huff-n-puff in unconventional oil reservoirs represents a green development technology that integrates oil recovery and carbon storage, emphasizing both efficiency and environmental protection. A rational well selection method is crucial for the success of CO2 huff-n-puff development. This paper initially identifies eight parameters that influence the effectiveness of CO2 huff-n-puff development and conducts a systematic analysis of the impact of each factor on development effectiveness. A set of factors for well selection decisions is established with seven successful CO2 huff-n-puff cases. Subsequently, the influencing factors are classified into positive, inverse, and moderate indicators. By using an exponential formulation, a method for calculating membership degrees is calculated to accurately represent the nonlinearity of each parameter’s influence on development, resulting in a dimensionless fuzzy matrix. Furthermore, with the oil exchange ratio serving as a pivotal parameter reflecting development effectiveness, recalibration of weighting factors is performed in conjunction with the dimensionless fuzzy matrix. The hierarchical order of weighting factors, from primary to secondary, is as follows: porosity, reservoir temperature, water saturation, formation pressure, reservoir thickness, crude oil density, crude oil viscosity, and permeability. The comprehensive decision factor and oil exchange ratio exhibit a positive correlation, affirming the reliability of the weighting factors. Finally, utilizing parameters of the Ordos Basin as a case study, the comprehensive decision factor is calculated, with a value of 0.617, and the oil exchange ratio is predicted as 0.354 t/t, which falls between the Chattanooga and Eagle Ford reservoirs. This approach, which incorporates exponential membership degrees and recalibrated weighting factors derived from actual cases, breaks the limitations of linear membership calculation methods and human factors in expert scoring methods utilized in existing decision-making methodologies. It furnishes oilfield decision-makers with a swifter and more precise well selection method.

Assessment of child-friendly spatial environment in urban communities based on fuzzy Analytic Hierarchy Process

Urban development is progressively integrating the concept of child-friendly cities to foster inclusive and sustainable environments prioritizing children's needs and rights. This study contributes to the discourse on child-friendly urban spaces by leveraging the fuzzy Analytic Hierarchy Process (AHP) to assess these environments systematically. The methodology begins by compiling key factors influencing child-friendly environments. Using the AHP and expert scoring, judgment matrices are created to determine the weight of these factors, forming a structured assessment index system. The system is further refined through the development of specific rating items for each second-level factor, employing the fuzzy comprehensive assessment method integrated with membership functions. This approach enables a detailed, quantifiable analysis of factors contributing to child-friendly environments. To illustrate the practical application of this methodology, it was applied to Zengjiayan Community in Yuzhong District, a densely populated area of Chongqing, China, known for its mountainous terrain, thus navigating the distinctive challenges posed by this unique urban landscape. Extensive field research and interviews aligned second-level rating items with specific community features. Results yield a 'Good' rating for the community’s child-friendly environment, highlighting achievements and spatial constraints. These insights underscore the impact of urban design and policies on child-friendliness in challenging settings. The study provides a vital tool for urban planners and policymakers, presenting a model to assess and improve child-friendly spaces in complex urban settings.