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Critical thinking is a common and important learning outcome in college curricula. Case-based and problem-based learning can be used to assess and foster critical thinking skills. HNF 250–Contemporary Issues in Human Nutrition is a critical thinking course developed during the redesign of a nutritional sciences major program. Course assignments were designed to assess the course and nutritional sciences major learning outcomes. The nutrition and health claim assignment is scaffolded across the academic semester as three assignments: 1) bibliography assignment; 2) poster presentation; and 3) paper. Course lectures and materials have been designed to prepare students for completion of each assignment. The assignments have been modified over time based on classroom observations and student performance. In 2021, the course learning outcomes were examined by assessing several assignments including the nutrition and health claim poster and paper. Course learning outcome benchmarks using these assessments generally included 80% of students achieving an 80% for each criterion. Results revealed that students were not meeting most of these assessment benchmarks during the 2021 iteration, although benchmark data from other course assessments were more satisfactory. It is possible that the transition from a virtual to an in-person format negatively influenced student performance on these course learning outcomes. This course and the nutrition and health claim assignment example can provide a course design and learning outcome assessment framework for other higher education critical thinking courses. NEW & NOTEWORTHY: This paper describes how a scaffolded nutrition and health claim assignment is used to teach critical thinking skills among nutritional sciences students and examine the program's learning outcomes. Further, this course example is to serve as an model for STEM majors on how to incorporate case-based and problem-based learning strategies into an undergraduate course. Listen to this article's corresponding podcast at https://apspublicationspodcast.podbean.com/e/teaching-critical-thinking-in-nutritional-sciences/. [ABSTRACT FROM AUTHOR]

Vernacular architecture offers valuable technological and typological solutions rooted in knowledge systems that connect environmental, social, economic, and cultural contexts. This paper introduces the Heritage for People Platform, a collaborative digital tool developed under the VerSus+ project (Creative Europe Program), aimed at organising and disseminating vernacular architectural knowledge with a focus on sustainable practices that are both culturally and environmentally conscious. The platform builds upon lessons learned from similar projects, offering key improvements such as the adoption of a Case-Based Reasoning methodology, which organises examples by geographic location, materials, and intervention types. A structured classification system based on sustainability principles and strategies enables cross-disciplinary research and comparison. The design process was highly participatory, incorporating user feedback at every stage to ensure the platform is accessible to a wide range of users, including designers, scholars, craftspeople, and the general public. A cross-device, user-centered approach further broadens its accessibility. The platform provides a public, web-based geospatial repository where users can create, update, and explore a dynamic collection of artefacts, design models, people, and institutions from across the globe. This tool marks a significant advance in promoting and preserving both physical and intangible heritage, with strong educational and practical applications for sustainable architecture. The paper details the theoretical framework and real-world potential of the platform as a critical resource for promoting sustainability in the built environment. [ABSTRACT FROM AUTHOR]

Those with intellectual and developmental disabilities (I/DD), especially autism, represent an often misunderstood and underserved population. In order to address health disparities faced by those with I/DD in Washington state, the Extension for Community Healthcare Outcomes (Project ECHO) model was implemented to build capacity to serve these individuals and their families in their home communities. Through this year-long telementorship and learning community, expert teams lead clinics based on interdisciplinary case-based discussion and knowledge sharing with medical, behavioral, and mental healthcare providers. A cornerstone of these expert teams are autistic self-advocates, who provide insight into lived experience. In this paper, we will discuss how the inclusion of six autistic self-advocates across four different ECHO programs has allowed ECHO participants to broaden their horizons and gain new insight into supports for their I/DD clients across multiple aspects of care. In addition to the unique knowledge provided by self-advocates, their participation illustrates the potential for all people with I/DD to live rich and fulfilling lives. The initial implementation and on-going success of including lived experience within the ECHO model can be used as an example of how to create partnerships that meaningfully inform decisions and improve equitable outcomes in service provision. [ABSTRACT FROM AUTHOR]

This perspective paper addresses the current landscape of predoctoral orthodontic education in the United States. A history of American Dental Education Association (ADEA) senior dental student exit surveys consistently reveals that graduates lack confidence in space management and malocclusion. The authors, with over 50 years of combined experience in dental education, present variations in predoctoral orthodontics programs, common challenges, and underscore the need for aligning curricula with the distinct characteristics of the current Gen Z student cohort. Variations include instructional clock hours, timing and sequence, and instructor qualification. An examination of the topics covered in predoctoral orthodontics demonstrates stability from the 1993 ADEA guidelines to a 2021 consensus panel's recommendations. Specific examples stemming from the direct experiences of the authors demonstrate the variety of approaches used in predoctoral orthodontic education. Challenges in predoctoral education stem from not distinguishing curricular goals from Advanced Education in Orthodontics, or clearly defining parameters for competence. Training dental students to treat malocclusion is not practical or possible within the present dental curriculum. The authors stress the importance of shifting educational goals towards competency training in recognizing, diagnosing, and managing malocclusion, to increase relevance to general dental clinical practice. The paper highlights innovative teaching strategies to engage students in competency-based learning: Problem Based Learning, Gamification, Case-based learning, Cold calling, Test enhanced Learning, Spiral Curriculum. The paper also presents educational technological applications used in traditional classrooms or remote environments for teaching and assessment, well suited for Gen Z's preference for technologically driven interactive learning methods. Despite its challenges, predoctoral orthodontics education in the United States is filled with opportunities for growth, which will only lead to elevating the standard of dental care for all patients. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence (AI). In this article, the development of CBR is reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were designed to solve problems by CBR are summarized. Then, the application fields of CBR are summarized. Finally, according to the idea of deep learning and interpretable AI, the main challenges for the future development of CBR are proposed. [ABSTRACT FROM AUTHOR]

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, "fire" and "no fire", were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting. [ABSTRACT FROM AUTHOR]

Systems thinking is crucial for understanding and solving complex problems and is considered an important thinking skill in engineering. Active learning is considered an effective approach for fostering STEM students' systems thinking. However, viable methods for teaching and assessing systems thinking with active learning across STEM disciplines, particularly in first-year undergraduate education, are still under-researched. In this paper, we introduce a research-based framework named System Architecture-Function-Outcome to help first-year STEM instructors both foster and assess students' introductory systems thinking. To conduct an initial evaluation of the framework's suitability in active learning settings, we designed a directed case-based learning assignment with an adapted article and a rubric for assessing 'introductory systems thinking', as defined in the framework. We deployed the assignment among 84 first-year STEM students and successfully tested its inter-rater reliability, with 75–100% inter-rater agreement across all assessment criteria. We discuss the implications of our results on fostering and assessing first-year STEM students' systems thinking, and outline examples for potential applications of the framework, pending further validation, in case-based learning settings of varying degrees of learner autonomy, from lecture-based to problem-based learning. [ABSTRACT FROM AUTHOR]

The grinding process of transmission gear with worm grinding wheel usually relies on the manual parameter setting by experienced engineers, but the reliability and validity are quite hard to control due to many grinding factors that affect the surface quality (e.g. structure, size, and materials of gear, heat treatment process, surface accuracy requirements). Therefore, it is necessary to set up a decision system for assisting engineers with designing the process plan efficiently and reasonably. A modified model of the revision method based on the radial basis function (RBF) neural network and case-based reasoning (CBR) considering evaluation index was proposed in this paper. A similar case retrieval mainly relies on the soft likelihood function considering a compound evaluation index model, where the score value of each new case execution test is converted into the corresponding index to achieve effective case storage. Process solutions for similar cases that perform better will be recommended from the case database by means of CBR. These candidates can be revised systematically based on a self-learning model. A modified RBF neural network trained by the existing cases will apply the learned experience to revise solution in part or in whole, which utilizes a self-filtering attribute method based on the correlation coefficient matrix so as to gain the primary attributes. In the case of the grinding gear with worm grinding wheel, the applicability of this technology was demonstrated. A decision-making system applying this method was developed by using.Net framework4.0 and SQL Server. Consequently, the technique can quickly generate a feasible process plan for specific gear. [ABSTRACT FROM AUTHOR]

Nowadays, in a turbulent environment, enterprise must be proactive and even predictive to survive in front of the plenty natures of dynamics. Achieving the desired proactivity and predictability, supposes that the enterprise works in advance on its internal transformation plans, but also develops an awareness to its context, to intercept, analyze and simulate any potential trigger of change that could affect its stability or more precisely its state of equilibrium. In this work, the concept of enterprise architecture is considered as a tool for establishing alignment between business, strategy, and IT. This paper is a continuation to previous works seeking for a dynamic model of enterprise architecture, able to respond in an autonomic manner to the turbulent environment. The self-adaptiveness was highlighted basically using the well-known loop, monitor, analyze, plan, execute and Knowledge. The use of Case-Based Reasoning was suggested to store a collection of problems and their resolutions, then using algorithms of similarity and adaptation to suggest the problem's best fit solution. To make the reasoning mechanism and similarity process as reliable as possible, it was important to maximize experiences and expand the knowledge base. For thus, the proposition was to share knowledge bases. In this paper, the context awareness is considered as second source to enrich the knowledge base and to stay vigilant and sensitive to triggers of change. [ABSTRACT FROM AUTHOR]

With the rapid development of the hydrogen energy industry and the vigorous promotion of the application of hydrogen fuel cell vehicles, hydrogen refueling station (HRS) has gradually penetrated into cities, mostly built in densely populated areas. Once leakage, fire, and/or explosion accident occurs, it is highly likely to cause casualties, property damage, and environmental pollution. In order to avoid HRS risks, this paper establishes a risk decision support model based on Cloud-Analytic Hierarchy Process for HRS. Firstly, the static and dynamic features of HRS emergency plan selection are analyzed, and the weight coefficient of each feature is determined by using the cloud analysis hierarchy process. Then, the case-based reasoning theory and the weighted Euclidean distance based on the cloud model are used to establish a decision support model based on emergency plan optimization. Finally, taking the HRS hydrogen storage tank fire as an example, the risk decision support model based on the cloud analysis hierarchical process is applied to form the optimal scheme of the HRS hydrogen storage tank fire emergency plan. The results show that the weighted Euclidean distance scheme selection method based on cloud model can be effectively combined with the weight acquisition method based on cloud analysis hierarchy process to provide decision support for HRS emergency risk management and control. • Decision support model is proposed based on Cloud-Analytic Hierarchy Process. • A scheme selection model with static and dynamic dual features is established. • The Weighted Distance-Based Approximation method assists in scheme selection based on dynamic features. • Case study confirms the superiority and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper Michael Vazquez and Dustin Webster consider the practice of deliberating about ethical case studies as a means to contribute to the professional development of educators. An ongoing debate is whether or not the study of ethical theory should be included in this practice. Vazquez and Webster argue that a popular strategy, known as the Phronetic Approach, is vulnerable to what they call "epistemic blinders" that arise in the absence of the scaffolding provided by theory. They then sketch an alternative approach to case‐based reasoning inspired by Barbara Herman's notion of middle theory. The middle theory approach naturally suggests a balanced method for using case studies in teacher professional development: expose educators to some theory, but only to the degree and at the level appropriate for the specific audience and in light of how frequent interactions with that audience will be. [ABSTRACT FROM AUTHOR]

Phishing attacks are increasing every year, both in terms of number and technique. Using only human weaknesses, an attacker can easily obtain the victim's credentials or access their network. The problem persists despite many approaches offered by researchers, due to its dynamic nature, in which new phishing tactics are created every time. We, therefore, need more robust and effective methods to detect phishing emails. In this paper, we aim to detect phishing emails using the body text of the email with the hybrid approach combining case-based reasoning (CBR) and a deep learning model. Our proposed model, called DL-CBR, consists of a Bidirectional Long Short-Term Memory (Bi-LSTM) + Temporal Convolutional Network (TCN) network with an attention mechanism followed by a CBR classifier. The deep learning model is used for email representation, where it is trained using the N -pair loss function. To demonstrate the performance of DL-CBR, evaluation metrics, such as precision, accuracy, recall, and F-measure, were used, where we obtained an accuracy of 98.28%. The results show that our model outperformed other CBRs that utilize classical text representations like TF-IDF and Bag-of-Words. Additionally, while our model's performance is slightly below that of the state-of-the-art models, it offers several advantages inherent to CBR. For instance, it can learn from new cases and update their database accordingly. [ABSTRACT FROM AUTHOR]

With the rapid development of metro construction in China, construction safety accidents occur frequently due to the lack of effective safety risk controls. Continuously improving the level of safety risk pre-control has become a significant factor in the safety managements of metro construction. This paper presents a reasoning model for safety risk pre-control measures in metro construction using case-based reasoning. To establish the case database, a frame-based knowledge representation method is adopted based on the characteristics of metro construction safety accident cases. Additionally, a two-level retrieval strategy combining hierarchical retrieval and nearest-neighbor algorithms is used for case retrieval. Furthermore, a new approach, the cloud model, is proposed to determine the attribute weight of metro construction safety accidents. This approach addresses quantitative attributes with fuzziness and randomness. Finally, a specific metro station is taken as a case to validate the feasibility of the approach. The results showed that the case-based reasoning method enables engineers to quickly and effectively develop risk pre-control measures based on previous cases. In addition, the use of case-based reasoning in decision-making enhances knowledge sharing and reuse in the metro construction industry. This paper presents an effective reasoning approach for pre-control measure analysis in metro construction, allowing engineers to quickly and efficiently carry out scientific and reasonable pre-control measures from past cases.

Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners' decision support. We have also developed a dedicated application for this project to visualize the results obtained. [ABSTRACT FROM AUTHOR]

Context: Software development effort estimation (SDEE) is one of the most challenging aspects in project management. The presence of missing data (MD) in software attributes makes SDEE even more complex. K‐nearest neighbors imputation (KNNI) has been widely used in SDEE to deal with the MD issue. However, KNNI, in its classical process, has low tolerance to imprecision and uncertainty especially when dealing with categorical features. When dealing with categorical attributes, KNNI uses a classical approach, employing mainly numbers or classical intervals to represent software attributes and similarity measures originally designed for numerical attributes. Objectives: This paper evaluates the use of an optimized fuzzy clustering‐based KNNI (FC‐KNNI) and compares it with classical KNN when dealing with mixed data in the context of SDEE. Methods: We investigate the effect of two imputation techniques (FC‐KNNI and KNNI) on five SDEE techniques: case‐based reasoning, fuzzy case‐based reasoning, support vector regression, multilayer perceptron, and reduced‐error pruning tree. The evaluation is carried out using six publicly available datasets for SDEE using two performance measures, standardized accuracy (SA), and Pred (0.25). The Wilcoxon statistical test is also performed to assess the significance of results. Results: The results are promising in the sense that using an imputation technique designed for mixed data is better than reusing methods originally designed for numerical data. We found that FC‐KNNI significantly outperforms KNNI regardless of the SDEE technique and dataset used. Another important finding is that F‐CBR improved the analogy process compared to CBR. Conclusion: The introduction of fuzzy sets and fuzzy clustering in the analogy process improves its performances in terms of SA and Pred (0.25). [ABSTRACT FROM AUTHOR]

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a "context-awareness" that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model. [ABSTRACT FROM AUTHOR]

To tackle the global water imbalance problem, a multitude of inter-basin water transfer projects have been built worldwide in recent decades. Nevertheless, given the complexity and safety challenges associated with project operation, effective emergency decision-making is crucial for addressing unforeseen incidents. Hence, this research has developed a two-stage emergency decision-making framework to tackle the uncertainty in the development trends of emergencies in inter-basin water transfer projects. (1) The first stage mainly utilizes case-based reasoning techniques to extract historical case information and disposal plans for inter-basin water transfer projects. Subsequently, a holistic similarity model is built by employing structural similarity and local attribute similarity algorithms to identify highly similar historical cases. (2) The second stage involves the optimization and adjustment of decision-making plans based on the dynamic evolution characteristics of emergencies. It utilizes the theory of decision-makers regret psychology and combines it with practical case studies to verify the scientific rationality of the method. This enables it to achieve effective multidimensional expression and rapid matching of scenarios, satisfying the decision-making requirements of "scenario response". Finally, this study compares the results obtained from this method with those computed using the traditional TOPSIS method and fuzzy comprehensive evaluation method, further validating its feasibility and effectiveness. In practice, this method can provide effective support for decision-makers work. [ABSTRACT FROM AUTHOR]

In emergency situations, accurate demand forecasting for relief materials such as food, water, and medicine is crucial for effective disaster response. This research is presented a novel algorithm to demand forecasting for relief materials using extended Case-Based Reasoning (CBR) with the best-worst method (BWM) and Hidden Markov Models (HMMs). The proposed algorithm involves training an HMM on historical data to obtain a set of state sequences representing the temporal fluctuations in demand for different relief materials. When a new disaster occurs, the algorithm first determines the current state sequence using the available data and searches the case library for past disasters with similar state sequences. The effectiveness of the proposed algorithm is demonstrated through experiments on real-world disaster data of Iran. Based on the results, the forecasting error index for four relief materials is less than 10%; therefore, the proposed CBR-BWM-HMM is a strong and robust algorithm. [ABSTRACT FROM AUTHOR]

Predicting landslide hazards benefits geological disaster prevention and control. A novel cellular automaton (CA) integrating spatial case-based reasoning (SCBR), namely SCBR-CA, is proposed in this paper to predict landslide hazards at a local scale. The proposed model not only extracts spatial scene features for computations but also achieves dynamic prediction, which means that only one input is needed to obtain continuous predictions. Experiments were performed in Lushan, Sichuan, China. After using a convolutional neural network (CNN) to obtain the initial static landslide hazard zoning results, the landslide hazard zoning results for 2016–2025 were predicted with the SCBR-CA model. For comparison, a CA combined with a CNN (CNN-CA), was introduced. The area under the curve (AUC) of the receiver operating characteristic curve and Moran's I index were used to assess the performance of the model. The experimental results showed that SCBR-CA yields slightly better AUC and Moran's I index values than CNN-CA, and the dynamically predicted landslide hazard zoning results are equivalent or superior to those of static zoning, which indicates that the SCBR-CA model effectively predict local landslide hazards. [ABSTRACT FROM AUTHOR]

The road use load should essentially conform to the plan load as stated in the design guidelines. But in reality, the load received by the road is more than standard, so it has the potential to accelerate road damage. This study aims to predict the increase in the value of the International Roughness Index (IRI) and model the increase in value (IRI) due to overload. The approach method used in the analysis is the prediction of an increase in the IRI value due to excess load. The data collection method is carried out on road sections which include IRI values, vehicle load testing, CBR values and environmental conditions. The location of data collection is carried out on BTS Provincial Road. Surabaya City – Legundi. The analysis method for the prediction of the IRI value is the HDM-4 method, while to model the prediction of the increase in the IRI value is used exponential method with various parameters related to the results. The prediction results show that overload affects the increase in the IRI value. From the initial IRI value of 2.37, if the road is traversed by vehicles with standard loads for 20 years, the IRI value increases to 10.42. While the road traveled by overloaded vehicles within 20 years from an IRI value of 2.37 will increase to 16.88. With overload, the IRI value increases very sharply with the exponential model y = 2.3583e0.0982x . While the standard load by generating an exponential model y = 2.3825e0.0739x . From the results of the exponential model, load overload resulted in a significant increase in the IRI value with a growth rate of 0.0982. When compared to normal expenses which only experienced a growth rate of 0.0739. The modeling results show that overload will increase the growth of very high IRI values with the exponential model increase graph. [ABSTRACT FROM AUTHOR]

Jiajin Mountain, where the giant pandas reside, is an essential nature reserve in China. To comprehend the land use classification of the habitat, this article proposes a remote sensing interpretation algorithm based on spatial case reasoning, known as spatial case-based reasoning (SCBR). The algorithm incorporates specific spatial factors into its framework and does not require an extensive amount of domain knowledge and eliminates the need for a complex model training process, making it capable of completing land use classification in the study area. SCBR comprises a spatial case expression model and a spatial case similarity reasoning model. The paper conducted comparative experiments between the proposed algorithm and support vector machine (SVM), U-Net, vision transformer (ViT), and Trans-Unet, and the results demonstrate that spatial case-based reasoning produces superior classification outcomes. The land use classification experiment based on spatial case-based reasoning at the Jiajinshan giant panda habitat produced satisfactory experimental results. In the comparative experiments, the overall accuracy of SCBR classification reached 95%, and the Kappa coefficient reached 90%. The paper further analyzed the changes in land use classification from 2018 to 2022, and the average accuracy consistently exceeds 80%. We discovered that the ecological environment in the region where the giant pandas reside has experienced significant improvement, particularly in forest protection and restoration. This study provides a theoretical basis for the ecological environment protection of the area.

In this paper, for the common live streaming with cargo dispute cases in the context of the information technology era, the live streaming with cargo industry dispute case retrieval algorithm based on case-based reasoning calculates the attribute similarity measure and weights of the dispute cases in order to realize the live streaming with cargo industry dispute case retrieval. Three thousand judicial documents with live streaming with cargo disputes as the cause of the case were crawled from the China Judicial Documents Network as the initial data for the study and the method of BIOES was used to annotate the initial data and formulate eight basic legal elements of live streaming with cargo dispute cases. The form of extracting legal elements of live streaming with goods industry is transformed into a multi-label text classification task, and the neural network modeling method combined with the attention mechanism is used to design the extraction model of legal elements of the live streaming with goods dispute cases, and then the live streaming with goods dispute cases on the Internet are analyzed by examples. The results show that the Acc value of this paper’s model is 0.973, the Loss value is 0.051, and the F1 value is 0.981, and on the whole, this paper’s model performs better compared with the other five models, i.e., it shows that this paper’s model can be more intelligent and accurate to collect the case element information under the field of live streaming with goods disputes. This study aims to contribute to the development of a new sales model of live streaming with goods benignly.

Explainable Artificial Intelligence (XAI) is an emerging field within Artificial Intelligence (AI) that has provided many methods that enable humans to understand and interpret the outcomes of AI systems. However, deciding on the best explanation approach for a given AI problem is currently a challenging decision-making task. This paper presents the iSee project, which aims to address some of the XAI challenges by providing a unifying platform where personalized explanation experiences are generated using Case-Based Reasoning. An explanation experience includes the proposed solution to a particular explainability problem and its corresponding evaluation, provided by the end user. The ultimate goal is to provide an open catalog of explanation experiences that can be transferred to other scenarios where trustworthy AI is required. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: The integration of Virtual Reality (VR) with Case-Based Learning (CBL) has the potential to revolutionise undergraduate medical education, particularly in complex subjects such as the anatomy and rehabilitation of the shoulder joint. This study aimed to explore the effectiveness of this innovative approach in enhancing learning outcomes and knowledge retention. Methods: This study employed a parallel-group, assessor-blinded randomised controlled trial (RCT) design. A comprehensive five-week educational programme was developed, combining traditional lecture-based learning with VR-enhanced CBL. The study involved 82 undergraduate students from China Medical University, who were divided into groups receiving different combinations of VR and CBL. Student performance was evaluated through tests and questionnaires. Results: In the anatomy-related courses, the integration of VR technology with CBL yielded significantly higher results (87.71 ± 5.60) compared to traditional methods (82.59 ± 6.64), with a statistically significant difference (P < 0.05). This provides compelling evidence of VR's potential to enhance student engagement and knowledge retention. In the context of physiotherapy-related courses, however, while the test scores of the VR-combined CBL group (81.85 ± 5.99) were marginally higher than those of the traditional CBL group (79.02 ± 7.57), this difference was not statistically significant (P > 0.05). Conclusion: The present study provides preliminary evidence for the benefits of incorporating VR into medical education, particularly in anatomy. While the results are promising, further research is needed to explore the optimal integration of VR and CBL in rehabilitation studies and to assess their long-term impact on student learning and clinical performance. Trial registration: The study was registered with Chinese Clinical Trials Registry (Registration Number: ChiCTR2400089295) on 05/09/2024. [ABSTRACT FROM AUTHOR]

A rapidly changing world with a high degree of uncertainty in the context of climate change requires sustainable and resilient infrastructures, for which engineers are jointly responsible. For this purpose, engineering students need to acquire competencies such as dealing with complexity and uncertainty, systems thinking and anticipatory thinking, which are linked to resilience. However, research has shown that engineering education falls short of providing future professionals with these competencies. This case study aims to give a holistic view of a teaching approach to address these shortcomings in the context of problem- and case-based learning. Based on a pre-post survey and reflective diaries, students' perception of their competency development and their learning progress are analyzed and discussed, following a mixed-methods approach. Findings indicate that both perception of competencies related to systems resilience, such as analyzing scenarios and evaluating crisis approaches, and interpersonal competencies, such as communication and resolving conflicts have significantly improved during the course. At the same time, the former were less pronounced before the course and developed the most during the course. The results underline the need for fostering engineering students' ability to dealing with resilience issues in order to design resilient infrastructure. [ABSTRACT FROM AUTHOR]

Maintaining logical consistency in structured explanations is critical for understanding and troubleshooting the reasoning behind a system's decisions. However, existing methods for entailment tree generation often struggle with logical consistency, resulting in erroneous intermediate conclusions and reducing the overall accuracy of the explanations. To address this issue, we propose case-based deduction (CBD), a novel approach that retrieves cases with similar logical structures from a case base and uses them as demonstrations for logical deduction. This method guides the model toward logically sound conclusions without the need for manually constructing logical rule bases. By leveraging a prototypical network for case retrieval and reranking them using information entropy, CBD introduces diversity to improve in-context learning. Our experimental results on the EntailmentBank dataset show that CBD significantly improves entailment tree generation, achieving performance improvements of 1.7% in Task 1, 0.6% in Task 2, and 0.8% in Task 3 under the strictest Overall AllCorrect metric. These findings confirm that CBD enhances the logical consistency and overall accuracy of AI systems in structured explanation tasks. [ABSTRACT FROM AUTHOR]

Background: Case-based learning (CBL) utilizing three-dimensional (3D) printed hip joint models is a problem-solving teaching method that combines the tactile and visual advantages of 3D-printed models with CBL. This study aims to investigate the impact of integrating 3D printing with CBL on learning developmental dysplasia of the hip (DDH). Methods: We conducted a prospective study from 2022 to 2023, including 120 fourth-year clinical medical students at Xuzhou Medical University. Students were randomly allocated into two groups of 60 participants each. The CBL group received conventional CBL teaching methods, while the 3D + CBL group utilized 3D-printed models in conjunction with CBL. Post-teaching, we analyzed and compared the theoretical and practical achievements of both groups. A questionnaire was designed to assess the impact of the educational approach on orthopedic surgery learning. Results: The theory scores of the CBL group (62.88 ± 7.98) and 3D + CBL group (66.35 ± 8.85) were significantly different (t = 2.254, P = 0.026); the practical skills scores of the CBL group (57.40 ± 8.80) and 3D + CBL group (63.42 ± 11.14) were significantly different (t = 3.283, P = 0.001). The questionnaire results showed that the 3D + CBL group was greater than the CBL group in terms of hip fundamentals, ability to diagnose cases and plan treatments, interesting teaching content, willingness to communicate with the instructor and satisfaction. Conclusions: The integration of 3D printing with case-based learning has yielded positive outcomes in teaching DDH, providing valuable insights into the use of 3D-printed orthopedic models in clinical education. [ABSTRACT FROM AUTHOR]

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis. Then compute the similarity of requirements through case-based reasoning, which predicted the requirements for reuse and restricted to error-based requirements. Afterward, the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate. Furthermore, the proposed model was evaluated by conducting experiments. The results showed that requirement redundancy and irrelevancy improved due to semantic analysis, which correctly predicted the requirements, increasing the fault detection rate and resulting in high user satisfaction. The predicted requirements are mapped into test cases, increasing the fault detection rate after changes to achieve higher user satisfaction. Therefore, the model improves the redundancy and irrelevancy of requirements by more than 90% compared to other clustering methods and the analytical hierarchical process, achieving an 80% fault detection rate at an earlier stage. Hence, it provides guidelines for practitioners and researchers in the modern era. In the future, we will provide the working prototype of this model for proof of concept. [ABSTRACT FROM AUTHOR]

Background: Although clinical substance use disorder (SUD) care is multidisciplinary there are few opportunities to collaborate for quality improvement or systems change. In Oregon, the Project ECHO (Extension for Community Healthcare Outcomes) model was adapted to create a novel multidisciplinary SUD Leadership ECHO. The objective of this study was to understand the unique effects of the adapted ECHO model, determine if the SUD Leadership ECHO could promote systems change, and identify elements that enabled participant-leaders to make changes. Methods: Four focus groups were conducted between August and September of 2022 with a purposive sample of participants from the second cohort of the Oregon ECHO Network's SUD Leadership ECHO that ran January to June 2022. Focus group domains addressed the benefits of the adapted ECHO model, whether and why participants were able to make systems change following participation in the ECHO, and recommendations for improvement. Thematic analysis developed emergent themes. Results: 16 of the 53 ECHO participants participated in the focus groups. We found that the SUD Leadership ECHO built a multi-disciplinary community of practice among leaders and reduced isolation and burnout. Three participants reported making organizational changes following participation in the ECHO. Those who successfully made changes heard best practices and how other organizations approached problems. Barriers to initiating practice and policy changes included lack of formal leadership authority, time constraints, and higher-level systemic issues. Participants desired for future iterations of the ECHO more focused presentations on a singular topic, and asked for a greater focus on solutions, advocacy, and next steps. Conclusions: The adapted ECHO model was well received by focus group participants, with mixed reports on whether participation equipped them to initiate organizational or policy changes. Our findings suggest that the SUD Leadership ECHO model, with fine-tuning, is a promising avenue to support SUD leaders in promoting systems change and reducing isolation among SUD leaders. [ABSTRACT FROM AUTHOR]

Background: The transition from nursing students to working as new nurses can be a challenging process. This study aimed to assess the efficacy of a pedagogical approach amalgamating the think-aloud approach and case-based learning in the instructional rounds for new nurses. Methods: Utilizing convenience sampling, new nurses were selected between 2020 and 2021 in China cancer hospital. A total of 98 participants agreed to participate, with 50 enrolled in 2020 as the control group and 48 in 2021 as the observation group. Across a span of weeks 1, 3, 5, 7, 9, and 11, each clinical department conducted six teaching rounds. The observation group engaged in teaching rounds combining the think-aloud approach with case-based learning, whereas the control group solely utilized case-based learning. Disparities in case analysis scores and critical thinking ability between the two groups were scrutinized, alongside an analysis of learning strategies and the observation group feedback. Results: The observation group exhibited superior case analysis scores (91.92 ± 6.33) and overall critical thinking ability scores (308.39 ± 35.88) in comparison to the control group, which scored (85.27 ± 5.39) and (275.11 ± 31.32) respectively, reflecting statistically significant variances (t = 1.868 ~ 6.361, P < 0.05). Predominant learning strategies employed in the observation group ranged from cognitive to meta-cognitive, followed by psychosocial strategies. During interviews focused on nurses' feedback on the learning process, themes emerged surrounding the enhancement of learning proficiency, invigoration of learning enthusiasm, and bolstering psychological well-being. Conclusion: The combination of think-aloud approach and case-based learning in nursing teaching rounds greatly improves the efficiency of training and the critical thinking acuity of new nurses. Concurrently, it facilitated an evaluation of learning strategies, thereby offering valuable insights for the nursing teaching rounds of new nurse. [ABSTRACT FROM AUTHOR]

As medical education evolves, Problem-Based Learning (PBL) and Case-Based Learning (CBL) methodologies have emerged as vital pedagogical tools. This study aims to delve into the application and effectiveness of a hybrid PBL-CBL approach in clinical teaching for vascular surgery. By conducting a comparative analysis through concrete teaching practices, this research evaluates the impact on students' clinical knowledge retention, advancement in clinical reasoning skills, and proficiency in resolving real-world clinical challenges. The findings reveal that the integrated PBL-CBL methodology substantially enhances learning outcomes in vascular surgery clinical teaching, ultimately fostering significant development in students' comprehensive clinical abilities. [ABSTRACT FROM AUTHOR]

Assessing healthy cities is a crucial strategy for realizing the concept of "health in all policies". However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model's adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy. [ABSTRACT FROM AUTHOR]

Sustainable development has become a worldwide consensus in recent years. It has become increasingly important to study how to make effective management decisions in rural areas to achieve sustainable development. In this paper, we develop a new approach to strategic responses for sustainable rural development by using case-based reasoning (CBR), the bi-direction Choquet integral (BDCI), and the Shapley index (SI). First, to deal with the complexity of sustainable rural development, we develop an ontology-based sustainable rural development domain model. Second, we propose a case retrieval algorithm based on BDCI to improve the case retrieval method used in CBR to solve the problem of the relationship between the features. Third, we propose a case reuse method that is inspired by SI, in which each solution is evaluated relative to its value, and the implementation step for the response strategy is determined based on its priority. The proposed strategy response method for sustainable rural development is demonstrated through a case study and its effectiveness is demonstrated empirically. • A CBR-based approach for sustainable rural development strategy response is innovatively developed. • Using ontology method to build a sustainable rural development domain model. • A BDCI-based case retrieval algorithm is proposed to solve the relationship between attributes. • A Shapley-based case reuse method is proposed to provide order for solutions. [ABSTRACT FROM AUTHOR]

This paper describes the development, content, structure, and implementation of a case-based collaborative learning, flipped classroom, integrated preclinical neurology, neuroanatomy, and neuroscience course for first year medical students at Harvard Medical School. We report the methods for pre-class preparation, in-class instruction, and evaluation; student feedback with respect to content, teaching method, and learning environment; and several lessons learned regarding how to optimize preparatory and in-class learning in a case-based flipped classroom course. • Active learning models include problem-based learning and team-based learning. • In the flipped classroom approach, students study material prior to class, and classroom time is used to apply what they have learned. • We describe a case-based, team-based, flipped classroom course integrating neuroanatomy/neuroscience and clinical neurology. [ABSTRACT FROM AUTHOR]

The decision-making on green retrofitting of existing buildings relies on both explicit and implicit knowledge, and its efficiency and reliability need improvement. Intelligent approaches that can sufficiently utilize the text information of existing projects are required to provide more suitable strategies for green retrofitting. This paper describes a decision-making approach combining Case-Based Reasoning (CBR) and Random Forest (RF), which can identify similar cases from the database containing 109 green retrofit projects and revise outdated measures. A practical project case study shows that the revised retrofit measures can reduce Energy Use Intensity (EUI) by 37%. The proposed approach optimizes and standardizes CBR processes and provides guidance for coping with semi-structured green retrofit decision-making problems, thereby promoting sustainable development and intelligent management in the construction field. The system prototype will be developed and promoted after the case database is expanded. • A CBR-RF approach is designed to select more energy-efficient strategies for building green retrofit. • RF is proven to optimize the case revision stage of CBR to avoid outdated retrofit measures. • The information on existing green retrofit projects is standardized and cross-platform used. • This approach provides intelligent guidance for retrofit decision-making, improving efficiency and reliability. [ABSTRACT FROM AUTHOR]

Background: The use of psychoactive substances to increase cognitive performance while studying has been termed 'pharmacological cognitive enhancement' (PCE). In previous years, several large-scale national surveys have focused on their use by students at university, including drug types, prevalence rates, and predictive factors. The recent coronavirus pandemic brought about widespread structural changes for UK universities, as students were forced to adapt to home-based learning and in many cases reduced academic support. No study has yet focused primarily on the impact of pandemic social restrictions on PCE in students and academic staff, and whether personality and demographic factors reveal user profiles that predict use during the pandemic period. Method: A convenience sample of 736 UK students and staff aged 18-54 (M = 22.2, SD = 5.2) completed a cross-sectional survey assessing PCE prevalence rates, polydrug use, perceived effects, academic self-efficacy and personality during the first year of social restrictions (March 2020 - February 2021) compared with the previous year (March 2019 - February 2020). Results: There was a significant self-reported rise in the use of all drug types (all ps < 0.001) during social restrictions, particularly with Modafinil (+42%), nutraceuticals (+30.2%) and microdose LSD (+22.2%). Respondents also indicated stronger PCE effects for all substances, except alcohol, in comparison to the previous year. Polydrug use with modafinil and other prescription stimulants increased the most during social restrictions. Personality factors and gender identity reliably predicted PCE use and lower agreeableness was often the strongest predictor, followed by identifying as male and lower conscientiousness. Academic self-efficacy and student/academic staff status were not consistent predictors. Conclusion: This is the first survey of UK students to investigate PCE during coronavirus social restrictions and to assess predictive factors. Findings reveal a rise in PCE use and polydrug use which we suggest is because of increased pressures on students created by the lockdown and reduced access to university resources. [ABSTRACT FROM AUTHOR]

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a "one-size-fits-all" approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra—a state-of-the-art PIAC method—and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method's performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra's methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra's inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR. [ABSTRACT FROM AUTHOR]

3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud's aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic "block model" of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds. [ABSTRACT FROM AUTHOR]

Higher perspective taking skills are associated with better social functioning and improved social relationships. Generally, teachers are willing to take the perspective of their students, but it is unclear whether the same is true for the perspective of parents. As communication and conflicts with parents are pervasive, the motivation and willingness to adopt the perspective of parents in counseling situations should be promoted during university teacher training. Therefore, we investigated the promotion of perspective taking among teachers in training and focused mainly on perspective taking toward parents. We developed a case-based learning task in which teachers in training from Freie Universität Berlin (N  =  515) prepared for a fictitious upcoming consultation with a mother about her son. Because it is unclear if direct instruction for perspective taking is necessary in order to promote it, we also used indirect instruction to investigate whether preparing for the consultation under these instructions fostered the willingness to adopt the perspective of students and parents. In the direct instruction participants were directly told to take the perspective of the fictitious mother when evaluating and developing formulations for the consultation. The indirect instruction did not mention the concept of perspective taking but asked participants to focus on the comprehensibility of the formulations. We obtained three measures: the willingness to take a perspective, the attitude toward another person, and the emotional and empathic language used in written texts. With our main result we demonstrated that the willingness to adopt the perspective of both students and parents could be significantly promoted by both instructions. We further demonstrated that a higher willingness to take another’s perspective is associated with a more positive attitude toward the mother, as well as increased positive emotions and empathic concern. Additionally, we replicated results of a previous study showing a generally higher willingness to take the student perspective prior to the intervention. Results are discussed regarding the benefits of promoting perspective taking, especially toward parents, in teacher education. [ABSTRACT FROM AUTHOR]

Spatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies. [ABSTRACT FROM AUTHOR]

Purpose: This study developed and implemented case-based flipped learning using illness script worksheets and investigated the responses of preclinical students and professors to the intervention in terms of its effectiveness, design, and implementation. Methods: The study was conducted at a medical school in Korea, where the “clinical reasoning method” course, originally a lecture-oriented course, was redesigned into a flipped learning. In total, 42 second-year medical students and 15 professors participated in this course. After the class, online surveys were conducted, and a focus group interview was held with seven students to explore the students’ experiences in more detail. Results: In total, 37 students and seven professors participated in the survey. The mean score for all items is 3.12/4 for the student survey and 3.43/4 for the professor survey. The focus group interview results were categorized as the beneficial aspects and challenges for the development of clinical reasoning. Conclusion: The findings indicated that their responses to the intervention were generally positive, and it is thought to be an effective instructional method for fostering clinical reasoning skills in preclinical medical students. [ABSTRACT FROM AUTHOR]