Reasoning Process Research Articles

Integrating Neural-Symbolic Reasoning With Variational Causal Inference Network for Explanatory Visual Question Answering.

Recently, a novel multimodal reasoning task named Explanatory Visual Question Answering (EVQA) has been introduced, which combines answering visual questions with multimodal explanation generation to expound upon the underlying reasoning processes. In contrast to conventional Visual Question Answering (VQA) that merely concentrates on providing answers, EVQA aims to improve the explainability and verifiability of reasoning by providing user-friendly explanations. Despite the improved explainability of inferred results, the existing EVQA models still adopt black-box neural networks to infer results, lacking the explainability of the reasoning process. Moreover, existing EVQA models commonly predict answers and explanations in isolation, overlooking the inherent causal correlation between them. To handle these challenges, we propose a Program-guided Variational Causal Inference Network (Pro-VCIN) that integrates neural-symbolic reasoning with variational causal inference and constructs causal correlations between the predicted answers and explanations. First, we utilize pretrained models to extract visual features and convert questions into the corresponding programs. Second, we propose a multimodal program Transformer to translate programs and the related visual features into coherent and rational explanations of the reasoning processes. Finally, we propose a variational causal inference to construct the target structural causal model and predict answers based on the causal correlation to explanations. Comprehensive experiments conducted on EVQA benchmark datasets reveal the superiority of Pro-VCIN in terms of both performance and explainability over state-of-the-art EVQA methods.

Predicting mortality in hospitalized influenza patients: integration of deep learning-based chest X-ray severity score (FluDeep-XR) and clinical variables.

To pioneer the first artificial intelligence system integrating radiological and objective clinical data, simulating the clinical reasoning process, for the early prediction of high-risk influenza patients. Our system was developed using a cohort from National Taiwan University Hospital in Taiwan, with external validation data from ASST Grande Ospedale Metropolitano Niguarda in Italy. Convolutional neural networks pretrained on ImageNet were regressively trained using a 5-point scale to develop the influenza chest X-ray (CXR) severity scoring model, FluDeep-XR. Early, late, and joint fusion structures, incorporating varying weights of CXR severity with clinical data, were designed to predict 30-day mortality and compared with models using only CXR or clinical data. The best-performing model was designated as FluDeep. The explainability of FluDeep-XR and FluDeep was illustrated through activation maps and SHapley Additive exPlanations (SHAP). The Xception-based model, FluDeep-XR, achieved a mean square error of 0.738 in the external validation dataset. The Random Forest-based late fusion model, FluDeep, outperformed all the other models, achieving an area under the receiver operating curve of 0.818 and a sensitivity of 0.706 in the external dataset. Activation maps highlighted clear lung fields. Shapley additive explanations identified age, C-reactive protein, hematocrit, heart rate, and respiratory rate as the top 5 important clinical features. The integration of medical imaging with objective clinical data outperformed single-modality models to predict 30-day mortality in influenza patients. We ensured the explainability of our models aligned with clinical knowledge and validated its applicability across foreign institutions. FluDeep highlights the potential of combining radiological and clinical information in late fusion design, enhancing diagnostic accuracy and offering an explainable, and generalizable decision support system.

Improving the Classification of Defect Levels in Main Electrical Equipment Through Advanced Prompting on a Large Language Model

The stable operation of the power system is closely related to the national economy and people’s livelihoods. Therefore, the timely detection, qualitative assessment, and handling of major equipment defects are crucial. The classification of defect levels in main electrical equipment is a fundamental task in this process, which is often manually completed, supplemented by knowledge bases or expert systems. However, this approach is time-consuming, labor-intensive, involves challenging human–machine interaction, and relies on expert experience. Conversational large language models, such as ChatGPT, ERNIE Bot, ChatGLM, have garnered widespread recognition in various domains. However, these models may have errors in the reasoning process, resulting in biased or even erroneous outputs, which is referred to as the “hallucinations”. The hallucinations’ problem of large language models poses challenges in specific fields. To mitigate the hallucinations in large language models, researchers often seek to incorporate domain-specific knowledge into these models through methods like fine-tuning or prompt learning. In order to enhance model performance while minimizing computational costs, this study adopts the prompt learning approach. Specifically, we propose a large language model prompt learning framework based on knowledge graphs, aiming to provide the large language model with reasoning support by leveraging specific information stored within the knowledge graph and receive explainable reasoning result. Experimental results demonstrate that our module achieved superior results on the power defect dataset compared to the non-prompt method.

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes.

Применение графов знаний для клинического мониторинга процесса лечения

Patient condition monitoring is essential at every stage of the treatment and diagnostic process. During treatment, the key tasks of monitoring include evaluating the effectiveness of treatment strategies and ensuring safety, specifically by avoiding complications and adverse effects from medications, procedures, or their combinations. To enhance doctor-patient interactions and improve treatment quality, intelligent monitoring tools are needed. These tools must use patient data and formalized knowledge of the subject area to identify which observations, at specific times, can indicate any deviation of the patient's condition from expected outcomes.This study aims to develop an ontology that formalizes the knowledge necessary for selecting and explaining monitoring parameters throughout treatment. Key concept rela-tionships that address the challenges of monitoring patient conditions during treatment are identified, leading to an on-tological graph for a specific class of medical monitoring issues. The study describes a method for building knowledge graphs applicable to various diseases and a reasoning process to identify unexpected conditions. A process of reasoning and the resulting recommendation is presented. This approach establishes a foundation for a decision support system tailored to this class of problems, where monitoring parameters can be adapted according to treatment stage, patient condition, and individual characteristics.

Enhancing intention prediction and interpretability in service robots with LLM and KG

The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process.

Breaking Down the Concept of Students' Thinking and Reasoning Skills for Implementation in the Classroom.

Various skills related to critical thinking, reasoning, and scientific reasoning are defined as essential for students in policy documents and curricula around the world as essential both in school and in everyday life. However, these concepts are often too vaguely defined and explained for a clear implementation in the classroom. In this conceptual article, the authors propose the following questions: (1) How are the concepts of thinking and reasoning as defined in policy documents reflected in curriculum descriptions across different disciplines? (2) To what extent do reasoning activities and processes overlap across different disciplines? (3) How can reasoning skills (particularly: analysis, evaluation, and creation) be described based on reasoning activities or processes and the outputs or products? Based on the literature review, it is concluded that researchers in various science disciplines have defined the aspects of reasoning that are typical for their respective disciplines, considering content, procedural knowledge, and epistemic knowledge. Meanwhile, looking from the perspective of cognitive psychology, it is concluded that reasoning processes (deductive, inductive, and analogical reasoning) are activated in the mind while students engage in reasoning activities (such as analysis, evaluation, and synthesis). Thus, similar cognitive processes occur in the mind, despite a student working in different disciplines. A conceptual framework is offered in this article showing (1) how reasoning processes and activities manifest themselves in different study domains both from a theoretical perspective and in everyday classroom work; and (2) what kind of outputs could be expected from students based on various reasoning activities. The importance of interdisciplinary collaboration is justified so that students develop their reasoning skills holistically, not fragmentarily.

Cross-Modal self-supervised vision language pre-training with multiple objectives for medical visual question answering

Medical Visual Question Answering (VQA) is a task that aims to provide answers to questions about medical images, which utilizes both visual and textual information in the reasoning process. The absence of large-scale annotated medical VQA datasets presents a formidable obstacle to training a medical VQA model from scratch in an end-to-end manner. Existing works have been using image captioning dataset in the pre-training stage and fine-tuning to downstream VQA tasks. Following the same paradigm, we use a collection of public medical image captioning datasets to pre-train multimodality models in a self-supervised setup, and fine-tune to downstream medical VQA tasks. In the work, we propose a method that featured with Cross-Modal pre-training with Multiple Objectives (CMMO), which includes masked image modelling, masked language modelling, image-text matching, and image-text contrastive learning. The proposed method is designed to associate the visual features of medical images with corresponding medical concepts in captions, for learning aligned vision and language feature representations, and multi-modal interactions. The experimental results reveal that our proposed CMMO method outperforms state-of-the-art methods on three public medical VQA datasets, showing absolute improvements of 2.6%, 0.9%, and 4.0% on the VQA-RAD, PathVQA, and SLAKE dataset, respectively. We also conduct comprehensive ablation studies to validate our method, and visualize the attention maps which show a strong interpretability. The code and pre-trained weights will be released at https://github.com/pengfeiliHEU/CMMO.

Grain Size Effects on Visible and Near-infrared (0.35–2.5 μm) Laboratory Spectra of Rare Meteorite Classes

Linking near-Earth asteroids to associated meteorites can be a challenging process for many reasons, one being grain size differences. To address this issue for rarer meteorites, we studied visible and near-infrared (0.35–2.5 μm) reflectance spectra of 11 rare meteorite classes over five different grain size bins (45–90 μm, 90–150 μm, 150–300 μm, 300–500 μm, and 500–1000 μm). We analyzed the reflectance properties, diagnostic spectral band parameters (band centers and band area ratios), spectral slope, and taxonomic classification. The spectra were analyzed using principal component analysis to detect trends in principal component (PC) space and the impact on asteroid taxonomic classification in the Bus–DeMeo system. We found that the absolute reflectance (visual albedo) at 0.55 μm (photometric V band) typically decreases with increasing grain size, although there are some variations such as sharp increases for the slabs. Our EH4 and aubrite show a trend of increasing spectral slope with decreasing grain size. Our ureilite, angrite, winonaite, acapculoite, and mesosiderite show a general trend of a decrease in Band I (∼0.9 μm) depth with increasing grain size up to 500–1000 μm. Taxonomic classification of spectra of all grain sizes shows that classification tools generally struggle to differentiate grain size effects from mineralogical variations. This research demonstrates the need for a more robust taxonomic classification system that accounts for grain size and one that accurately classifies small near-Earth asteroids with regolith-free surfaces.

Do as Sonographers Think: Contrast-Enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 94.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

Bayesian deep learning: An enhanced AI framework for legal reasoning alignment

The integration of artificial intelligence into the field of law has penetrated the underlying logic of legal operations. Currently, legal AI systems face difficulties in representing legal knowledge, exhibit insufficient legal reasoning capabilities, have poor explainability, and are inefficient in handling causal inference and uncertainty. In legal practice, various legal reasoning methods (deductive reasoning, inductive reasoning, abductive reasoning, etc.) are often intertwined and used comprehensively. However, the reasoning modes employed by current legal AI systems are inadequate. Identifying AI models that are more suitable for legal reasoning is crucial for advancing the development of legal AI systems.Distinguished from the current high-profile large language models, we believe that Bayesian reasoning is highly compatible with legal reasoning, as it can perferm abductive reasoning, excel at causal inference, and admits the "defeasibility" of reasoning conclusions, which is consistent with the cognitive development pattern of legal professionals from apriori to posteriori. AI models based on Bayesian methods can also become the main technological support for legal AI systems. Bayesian neural networks have advantages in uncertainty modeling, avoiding overfitting, and explainability. Legal AI systems based on Bayesian deep learning frameworks can combine the advantages of deep learning and probabilistic graphical models, facilitating the exchange and supplementation of information between perception tasks and reasoning tasks. In this paper, we take perpetrator prediction systems and legal judegment prediction systems as examples to discuss the construction and basic operation modes of the Bayesian deep learning framework. Bayesian deep learning can enhance reasoning ability, improve the explainability of models, and make the reasoning process more transparent and visualizable. Furthermore, Bayesian deep learning framework is well-suited for human-machine collaborative tasks, enabling the complementary strengths of humans and machines.

The role of feared possible selves in the relationship between inferential confusion and obsessive-compulsive symptoms: A replication and extension in a clinical sample.

The feared possible self refers to an imagined version of self that one is afraid of being or becoming. Previous evidence has shown that dysfunctional reasoning (i.e., inferential confusion) is associated with obsessive-compulsive disorder (OCD) symptoms, which is partially mediated by a feared self. However, the evidence is reliant on non-clinical samples and a general measure of the feared self. Using a cross-sectional design, the current study attempted to replicate and extend this literature in a sample clinically diagnosed with OCD (n = 350) to assess the pathway from inferential confusion to OCD symptoms when feared self is accounted for as a mediator, particularly the individual dimensions of feared self (i.e., corrupted, culpable, and malformed feared selves). Participants completed a structured clinical interview for DSM-5, as well as measures of inferential confusion (Dysfunctional Reasoning Processes Task), obsessive beliefs, feared self, OCD symptoms, and psychological distress. Inferential confusion directly, and indirectly through the feared corrupted self, affected OCD symptoms, even after adjusting for obsessive beliefs, psychological distress, and comorbidity. However, the feared culpable and malformed selves did not play a role in this pathway. The study underscores that the feared corrupted self links inferential confusion to OCD symptoms, translating to the need to consider both dysfunctional reasoning processes and this specific feared self in clinical settings when treating OCD. Furthermore, the study provides more support for the inference-based approach (IBA) to OCD.

Investigating the Surface Quality of Aramid Honeycomb Materials Through Longitudinal–Torsional Ultrasonic Milling

During the milling process of aramid honeycomb, residual stresses arise, which will affect the surface quality of the honeycomb. Studies have shown that reasonable processing techniques can reduce residual stresses, indicating a close relationship between residual processing stresses and the processing parameters, such as technique. By investigating the changes in residual stresses after the processing of aramid honeycomb materials, the influence of processing techniques on these changes is analyzed. Leveraging the correlation between residual stresses and surface quality, this study proposes the use of residual stress as an indicator for evaluating processing techniques. The longitudinal–torsional ultrasonic vibration milling method is applied to the processing of aramid honeycombs. A single-factor experimental approach is adopted, utilizing ABAQUS 2020 software to mimic the longitudinal–torsional ultrasonic milling process. This study explores the influence patterns of various process parameters on the residual stresses generated during the milling of honeycombs. The simulation results indicate that within the selected range, the residual stress decreases as the tool rotation speed increases, while it increases with the increase in feed rate. The influence of milling depth on residual stress can be negligible. Furthermore, experiments were conducted based on the proposed correlation between residual stress and surface quality. The experimental results show good agreement with the simulation results, indicating that under reasonable process parameters, the residual stress values decrease, thereby improving the milling surface quality of aramid honeycomb materials. Therefore, measuring residual stress can serve as an effective method for evaluating the processing technique.

Clinical Reasoning Unveiled: Demystifying Heuristics and Hermeneutics in Neurological Clinical Practice

Introduction: Medical practice, particularly in Neurology, is often influenced by cognitive biases that can compromise diagnostic and therapeutic accuracy, leading to significant patient consequences. Clinical reasoning strategies are fundamental, yet the complexity of these processes is frequently underestimated. Understanding these strategies is crucial to ensure accurate diagnoses and effective treatments, thereby reducing clinical errors and optimizing patient outcomes. Despite extensive literature on cognitive biases, there is a need for a systematic synthesis addressing the peculiarities of neurological clinical reasoning, considering heuristics (mental shortcuts or rules of thumb that simplify decision-making) and hermeneutics (the theory and methodology of interpretation, especially of textual and symbolic content). Objectives: To review and systematize the main clinical reasoning strategies in Neurology, highlighting common biases and proposing methods to improve diagnostic and therapeutic accuracy. Methodology: A comprehensive literature review focusing on familiarity biases, Dual Process Theory, and heuristics (anchoring, availability, and representativeness). Additionally, the application of hermeneutics in hypothesis formulation and the importance of understanding the patient as a complex system were analyzed. Results: Specialized neurologists often rely on heuristics, which means a cognitive shortcut. Recognizing this process can mitigate anchoring and availability biases. The hermeneutic approach, on the other hand, is essential for interpreting variables in the patient’s context, integrating historical and social aspects. Conclusion: Understanding clinical reasoning processes and systematically teaching these strategies are crucial to enhancing the efficiency and effectiveness of clinical decision-making, reducing diagnostic errors, and improving patient outcomes.

Interpretable diagnosis of breast lesions in ultrasound imaging using deep multi-stage reasoning

Objective.Ultrasound is the primary screening test for breast cancer. However, providing an interpretable auxiliary diagnosis of breast lesions is a challenging task. This study aims to develop an interpretable auxiliary diagnostic method to enhance usability in human-machine collaborative diagnosis.Approach.To address this issue, this study proposes the deep multi-stage reasoning method (DMSRM), which provides individual and overall breast imaging-reporting and data system (BI-RADS) assessment categories for breast lesions. In the first stage of the DMSRM, the individual BI-RADS assessment network (IBRANet) is designed to capture lesion features from breast ultrasound images. IBRANet performs individual BI-RADS assessments of breast lesions using ultrasound images, focusing on specific features such as margin, contour, echogenicity, calcification, and vascularity. In the second stage, evidence reasoning (ER) is employed to achieve uncertain information fusion and reach an overall BI-RADS assessment of the breast lesions.Main results.To evaluate the performance of DMSRM at each stage, two test sets are utilized: the first for individual BI-RADS assessment, containing 4322 ultrasound images; the second for overall BI-RADS assessment, containing 175 sets of ultrasound image pairs. In the individual BI-RADS assessment of margin, contour, echogenicity, calcification, and vascularity, IBRANet achieves accuracies of 0.9491, 0.9466, 0.9293, 0.9234, and 0.9625, respectively. In the overall BI-RADS assessment of lesions, the ER achieves an accuracy of 0.8502. Compared to independent diagnosis, the human-machine collaborative diagnosis results of three radiologists show increases in positive predictive value by 0.0158, 0.0427, and 0.0401, in sensitivity by 0.0400, 0.0600 and 0.0434, and in area under the curve by 0.0344, 0.0468, and 0.0255.Significance.This study proposes a DMSRM that enhances the transparency of the diagnostic reasoning process. Results indicate that DMSRM exhibits robust BI-RADS assessment capabilities and provides an interpretable reasoning process that better suits clinical needs.

A Review of the Basic Principles and Methods of Signal Sampling Technology

In modern life, communication is an essential human activity. In the process of communication, various information spreads around the world in the form of signals. In order to optimize the communication, the reasonable processing of the signal becomes an important research problem. Signal processing is an important step during signal processing. It is one of two processes that convert an analog signal into a digital equivalent signal. It can be considered that signal sampling transforms the time axis of the signal into a set of discrete time moments, that is, the process of converting a signal in a continuous time domain to a discrete time domain through a series of methods. It is used to convert continuous time signals into digital signals for relevant processing and storage of digital signals in subsequent operations. This paper will summarize the basic principles and methods of signal sampling technology, introduce the sampling theorem, sampling frequency, sampling depth, sampling method and sampling error, and finally discuss the future development of signal adoption technology.

Sistem Pakar untuk Mendiagnosa Penyakit Tanaman Cabai Menggunakan Metode Forward Chaining

Chilli farming is an important sector in Ende District, but plant disease problems are often an obstacle that hampers productivity. Farmers often have difficulty recognizing the symptoms of disease in chili plants and determining appropriate treatment steps. To overcome this problem, this research aims to design and build a web-based expert system that can help diagnose chili plant diseases using the Forward Chaining method. This expert system was developed by collecting knowledge from agricultural experts and literature related to chili plant diseases, as well as applying the Forward Chaining method for the reasoning process. Users, especially farmers, can enter the symptoms experienced by chili plants into the system, then the system will produce a disease diagnosis and appropriate treatment recommendations based on these symptoms. This research uses a case study at Maju Tani Agriculture in Ende District to ensure that the expert system developed is relevant to local conditions. This system is implemented in the form of a web-based application so that it can be accessed easily by farmers using devices connected to the internet. The test results show that this expert system can provide accurate and efficient diagnoses, as well as assist farmers in making decisions to overcome chili plant disease problems. It is hoped that this system can increase agricultural productivity and reduce the risk of losses due to plant diseases.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Social cognition in adults with neurofibromatosis type 1.

Adult patients with the genetic disease neurofibromatosis type 1 (NF1) frequently report social difficulties. To date, however, only two studies have explored whether these difficulties are caused by social cognition deficits, and these yielded contradictory data. The aim of the present study was to exhaustively assess social cognition abilities (emotion, theory of mind, moral reasoning, and social information processing) in adults with NF1, compared with a control group, and to explore links between social cognition and disease characteristics (mode of inheritance, severity, and visibility). We administered a social cognition battery to 20 adults with NF1 (mean age = 26.5 years, SD = 7.4) and 20 healthy adults matched for sociodemographic variables. Patients scored significantly lower than controls on emotion, theory of mind, moral reasoning, and social information processing tasks. No effects of disease characteristics were found. These results appear to confirm that adults with NF1 have a social cognition weaknesses that could explain, at least in part, their social difficulties, although social abilities are not all impaired to the same extent. Regarding the impact of the disease characteristics, the patient sample seemed slightly insufficient for the power analyses performed. Thus, this exploratory study should form the basis of further research, with the objective of replicating these results with larger and more appropriately matched samples.

Development of Interprofessional Competencies Through the Process of Collaborative Clinical Reasoning: A Qualitative Study.

Effective interprofessional (IP) collaboration is essential for managing complex patient cases. Collaborative clinical reasoning (CCR) is an approach where IP teams collectively analyze and address complex clinical cases, facilitating a shared understanding of patient management. While simulation and case-based learning are established methods for fostering IP collaboration, the process by which students develop a shared mental model for patient management after a simulated home visit and how this approach promotes the development of IP competencies is unknown. Understanding this process is critical for advancing IP competences that are fundamental to case management practice. The purpose of this study was to explore students' perceptions of the CCR processes used to develop an integrated care plan following a simulated home visit. Home care and community-based practice settings. A qualitative descriptive approach was employed to explore PT and nursing student teams' perceptions of the CCR processes used to develop an integrated care plan following a simulated home visit. Written responses to five reflection questions were analyzed using conventional content analysis among n =87 physical therapy and nursing students. There were three overarching themes: clarifying roles; developing a mutual approach; embracing shared values. Students' perceptions reflected skills needed to develop IP competencies including CCR. More research is needed to explore how student teams resolve disputes and use interdisciplinary knowledge in the process of CCR. Findings highlight the importance of IP collaboration in clinical case management, emphasizing the need for case managers to facilitate clear role clarification, mutual approaches, and shared values among IP teams to enhance integrated care planning and improve patient outcomes. These insights can inform training and development programs for case managers on IP teams, promoting the adoption of CCR in practice.