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BackgroundShortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators. ObjectiveThe goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. MethodsWe trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. ResultsWe developed a VPS called Hepius that allows students to gather clinical information from the patient’s medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P

Kowloon Walled City (KWC), formerly the world's most densely populated place, is an example of urban density and self-organization in opposition to current urban developments, such as NEOM's The Line, which illustrates the future of envisioned sustainable living. The main research question addressed in this study is: How can principles of self-organization and resilience from densely populated historical urban areas be applied to enhance the sustainability and livability of future planned urban developments? This study investigates the critical components of urban livability and governance amid severe densities by examining the underlying factors that lead to KWC's unique sort of urban equilibrium and comparing them to the ideas that motivated the creation of The Line. This multidisciplinary research employs qualitative analysis to focus on architectural styles, community interactions, and governance systems. This approach allows for an in-depth examination of the parallels and dissimilarities between natural city evolution and future city planning. Key findings reveal that KWC's functioning stems from its complex social fabric and adaptable informal governance, providing valuable lessons in resilience and community-driven urban development. The results suggest that the flexibility and community-driven efforts of KWC are essential for ensuring long-term viability, and these aspects may be integrated into contemporary projects such as The Line. The outcomes highlight the potential of combining KWC's social cohesiveness with The Line's technology developments to create urban environments that are both robust and sustainable. The results are supported by evidence obtained from historical analysis, which showcases the practical uses of integrating organic and planned urban development concepts. [ABSTRACT FROM AUTHOR]

Music has been reported to facilitate motor performance. However, there is no data on the effects of different acoustic environmental stimuli on manual dexterity. The present observational study aimed at investigating the effects of background music and noise on a manual dexterity task in young, middle-aged and elderly subjects. Sixty healthy, right-handed subjects aged between 18 and 80 years were enrolled. Twenty young (mean age: 22±2 years), 20 middle-aged (mean age: 55±8 years) and 20 elderly (mean age: 72±5 years) subjects performed the Nine Hole Peg Test (NHPT) in four different acoustic environments: silence (noise < 20dBA), classical music at 60dBA, rock music at 70 dBA, and a noise stimulus at 80dBA. Performance was recorded using an optical motion capture system and retro-reflective markers (SMART DX, 400, BTS). Outcome measures included the total test time and peg-grasp, peg-transfer, peg-in-hole, hand-return, and removing phases times. Normalized jerk, mean and peak of velocity during transfer and return phases were also computed. No differences were found for NHPT phases and total times, normalized jerk, peak of velocity and mean velocity between four acoustic conditions (p>0.05). Between-group differences were found for NHPT total time, where young subjects revealed better performance than elderly (p˂0.001) and middle-aged (p˂0.001) groups. Music and noise stimuli in the considered range of intensity had no influence on the execution of a manual dexterity task in young, middle-aged and elderly subjects. These findings may have implications for working, sportive and rehabilitative activities. [ABSTRACT FROM AUTHOR]

In recent years, floods and climate-induced cataclysms have caused significant annual damage to livelihoods worldwide, with limited research on their vulnerability, impact, resilience, and long-term adaptation strategies in developing countries. In the South Indian State of Kerala, the major flood in 2018 caused immense economic losses in the low-lying and densely populated Alappuzha District. While the region has a heightened risk of periodic flooding, the considerable destruction of buildings and infrastructure highlights the need for effective solutions for flood resilience in the existing housing stock and new construction. In this context, this study examines flood resilience in the built environment of Alappuzha, focusing on flood vulnerability, building practices, and potential approaches suitable for the region that the current literature does not address. This study employs a qualitative research approach to understand current trends in adaptation strategies and the influencing socioeconomic and cultural factors. The study employs various data collection methods, including interviews, site observations, and content analyses of existing government reports, journal articles, and popular media sources. The findings indicate that although there are three types of established flood resilience techniques (static elevated, floating, and amphibious structures), their suitability for the low-lying areas of Alappuzha depends on the geographic, climatic, socioeconomic, and cultural contexts. Stilted houses have become the most common construction method, in response to climatic and socioeconomic conditions. In addition, the findings highlight the bounce-forth resilience quality of amphibious building techniques and suggest further exploration by integrating them with local technologies and materials. The study concludes that a comprehensive approach is needed that integrates traditional and modern knowledge and practices in disaster risk reduction and management to enhance the resilience of the built environment to flooding. [ABSTRACT FROM AUTHOR]

Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58–83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient's initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment. [ABSTRACT FROM AUTHOR]