Generic Dynamic Model for a Range of Thermal System Components

The simulation of a thermal system consists of a simulation of its components and their interactions. The advantages of thermal system simulations have been widely recognized. They can be used to explore the performance of a newly designed system, to identify whether the design meets the design criteria, to develop and test controls, and to optimize the system by minimizing the cost or power consumption, and maximizing the energy efficiency and/or capacity. Thermal system simulations can also be applied to existing systems to explore prospective modifications and improvements. Much research has been conducted on aspects of thermal system and component simulation, especially for steady-state simulation. Recently, transient simulations for systems and components have gained attention, since dynamic modeling assists the understanding of the operation of thermal systems and their controls. This research presents the development of a generic component model that allows users to easily create and customize any thermal component with a choice of working fluids and levels of complexity for either transient or steady-state simulation. The underlying challenge here is to design the code such that a single set of governing equations can be used to accurately describe the behavior of any component of interest. The inherent benefits to this approach are that maintenance of the code is greatly facilitated as compared to competing approaches, and that the software is internally consistent. This generic model features a user-friendly description of component geometry and operating conditions, interactive data input and output, and a robust component solver. The open literature pertaining to thermal component models, especially the components of vapor compression systems, is reviewed and commented on in this research. A theoretical evaluation of the problem formulation and solution methodology is conducted and discussed. A generic structure is proposed and developed to simulate thermal components by enabling and disabling a portion of the set of governing equations. In addition, a system solver is developed to solve a system composed of these components. The component/system model is validated with experimental data, and future work is outlined

ChatGPT: promise and challenges for deployment in low- and middle-income countries

Summary: In low- and middle-income countries (LMICs), the fields of medicine and public health grapple with numerous challenges that continue to hinder patients' access to healthcare services. ChatGPT, a publicly accessible chatbot, has emerged as a potential tool in aiding public health efforts in LMICs. This viewpoint details the potential benefits of employing ChatGPT in LMICs to improve medicine and public health encompassing a broad spectrum of domains ranging from health literacy, screening, triaging, remote healthcare support, mental health support, multilingual capabilities, healthcare communication and documentation, medical training and education, and support for healthcare professionals. Additionally, we also share potential concerns and limitations associated with the use of ChatGPT and provide a balanced discussion on the opportunities and challenges of using ChatGPT in LMICs

Long-term outcomes of offspring from multiple gestations: a two-sample Mendelian randomization study on multi-system diseases using UK Biobank and FinnGen databases

Abstract Background Assisted reproductive technologies (ART) have increased the incidence of multiple births, which can have a negative impact on maternal and offspring health. The study aimed to investigate the association between genetically predicted multiple birth and the risk of 42 common diseases of the nervous, psychiatric, cardiovascular, respiratory, digestive, and endocrine systems. Methods The study utilized two-sample Mendelian randomization (MR) analysis to explore the potential causal relationship between genetically predicted multiple birth and the genetically predicted risk of diseases. The study used the FinnGen and UK Biobank datasets for analysis. Results The study found no significant causal relationship between multiple birth and psychiatric disorders. However, the lower limits of the 95% confidence intervals for bipolar affective disorder and anxiety disorders were not robust, indicating a need for further investigation. The study found that multiple birth may be a strong risk factor for infantile cerebral palsy, and caution is necessary in both natural and ART multiple births. The study revealed a potential causal relationship between multiple birth and coronary heart disease, ischemic heart disease, and deep vein thrombosis, which may be related to abnormal intrauterine environments in multiple pregnancies. Surprisingly, multiple birth appears to have a protective effect against some respiratory diseases, such as chronic obstructive pulmonary disease and asthma. Conclusions The study highlights the need for caution regarding the risk of infantile cerebral palsy, cardiovascular diseases, and psychiatric disorders in multiple birth. Our study can lead to the development of preventive strategies and improved clinical management for affected infants