Rapeseed Oil Monoester of Ethylene Glycol Monomethyl Ether as a New Biodiesel

A novel biodiesel named rapeseed oil monoester of ethylene glycol monomethyl ether is developed. This fuel has one more ester group than the traditional biodiesel. The fuel was synthesized and structurally identified through FT-IR and P1PH NMR analyses. Engine test results show that when a tested diesel engine is fueled with this biodiesel in place of 0# diesel fuel, engine-out smoke emissions can be decreased by 25.0%–75.0%, CO emissions can be reduced by 50.0%, and unburned HC emissions are lessened significantly. However, NOx emissions generally do not change noticeably. In the area of combustion performance, both engine in-cylinder pressure and its changing rate with crankshaft angle are increased to some extent. Rapeseed oil monoester of ethylene glycol monomethyl ether has a much higher cetane number and shorter ignition delay, leading to autoignition 1.1°CA earlier than diesel fuel during engine operation. Because of certain amount of oxygen contained in the new biodiesel, the engine thermal efficiency is improved 13.5%–20.4% when fueled with the biodiesel compared with diesel fuel

Automatic Multitarget Detection Method Based on Distributed Through-wall Radar

Ultra-WideBand (UWB) radar exhibits strong antijamming capabilities and high penetrability, making it widely used for through-wall human-target detection. Although single-transmitter, single-receiver radar offers the advantages of a compact size and lightweight design, it cannot achieve Two-Dimensional (2D) target localization. Multiple-Input Multiple-Output (MIMO) array radar can localize targets but faces a trade-off between size and resolution and involves longer computation durations. This paper proposes an automatic multitarget detection method based on distributed through-wall radar. First, the echo signal is preprocessed in the time domain and then transformed into the time-frequency domain. Target candidate distance cells are identified using a constant false alarm rate detection method, and candidate signals are enhanced using a filtering matrix. The enhanced signals are then correlated based on vital information, such as breathing, to achieve target matching. Finally, a positioning module is employed to determine the radar’s location, enabling rapid and automatic detection of the target’s location. To mitigate the effect of occasional errors on the final positioning results, a scene segmentation method is used to achieve 2D localization of human targets in through-wall scenarios. Experimental results demonstrate that the proposed method can successfully detect and localize multiple targets in through-wall scenarios, with a computation duration of 0.95 s based on the measured data. In particular, the method is over four times faster than other methods

Cloning, identification, and functional analysis of foxl2 gene and its expression after 17β‐estradiol (E2) treatment in Dabry’s sturgeon, Acipenser dabryanus

The study focuses on the critically endangered Dabry’s sturgeon (Acipenser dabryanus), a species on the brink of extinction in the wild. This research investigates the role of the Forkhead box protein L2 (foxl2) in the gonadal development and differentiation of this species. The foxl2 gene, known for its role in various physiological processes, including sexual maturation, is hypothesized to play a significant role in the sex differentiation of Dabry’s sturgeon. This study cloned the full-length cds sequence of the foxl2 gene and analyzed its expression across various tissues, focusing on its response to estradiol treatment. Our findings indicate that foxl2 is predominantly expressed in ovaries and shows a dose-dependent response to estradiol, suggesting its potential role in ovarian differentiation. This research underscores the importance of foxl2 in understanding reproductive biology and offers a foundation for future conservation strategies

Evaluating large language models in pediatric fever management: a two-layer study

BackgroundPediatric fever is a prevalent concern, often causing parental anxiety and frequent medical consultations. While large language models (LLMs) such as ChatGPT, Perplexity, and YouChat show promise in enhancing medical communication and education, their efficacy in addressing complex pediatric fever-related questions remains underexplored, particularly from the perspectives of medical professionals and patients’ relatives.ObjectiveThis study aimed to explore the differences and similarities among four common large language models (ChatGPT3.5, ChatGPT4.0, YouChat, and Perplexity) in answering thirty pediatric fever-related questions and to examine how doctors and pediatric patients’ relatives evaluate the LLM-generated answers based on predefined criteria.MethodsThe study selected thirty fever-related pediatric questions answered by the four models. Twenty doctors rated these responses across four dimensions. To conduct the survey among pediatric patients’ relatives, we eliminated certain responses that we deemed to pose safety risks or be misleading. Based on the doctors’ questionnaire, the thirty questions were divided into six groups, each evaluated by twenty pediatric relatives. The Tukey post-hoc test was used to check for significant differences. Some of pediatric relatives was revisited for deeper insights into the results.ResultsIn the doctors’ questionnaire, ChatGPT3.5 and ChatGPT4.0 outperformed YouChat and Perplexity in all dimensions, with no significant difference between ChatGPT3.5 and ChatGPT4.0 or between YouChat and Perplexity. All models scored significantly better in accuracy than other dimensions. In the pediatric relatives’ questionnaire, no significant differences were found among the models, with revisits revealing some reasons for these results.ConclusionsInternet searches (YouChat and Perplexity) did not improve the ability of large language models to answer medical questions as expected. Patients lacked the ability to understand and analyze model responses due to a lack of professional knowledge and a lack of central points in model answers. When developing large language models for patient use, it's important to highlight the central points of the answers and ensure they are easily understandable

Association of obesity with osteoporotic fracture risk in individuals with bone metabolism-related conditions: a cross sectional analysis

IntroductionThis study aimed to investigate the individual and composite associations of different indices of obesity on osteoporotic fractures at three different sites among individuals affected by conditions influencing bone metabolism.MethodsParticipants were included from the National Health and Nutrition Examination Survey (NHANES), a national cross-sectional survey. BMI and WC were used separately and in combination to evaluate the presence of obesity. Obesity was defined as BMI ≥ 30 kg/m2, WC ≥ 88 cm in females, and WC ≥ 102 cm in males. Associations between obesity and osteoporotic fractures were assessed using multivariable logistic regression and OR curves. Associations modified by age, sex, race, and alcohol consumption were also evaluated.ResultsA total of 5377 participants were included in this study. In multivariable logistic regression analyses, we found that BMI, WC, BMI defining obesity, and WC defining obesity were negatively associated with hip fracture (all p < 0.05). However, harmful associations between WC and BMI defining obesity and spine fracture were found (all p < 0.05). OR curves revealed that BMI and WC had a linear relationship with hip and spine fractures (all P for non-linearity >0.05). Further analyses showed that the highest WC quartile was harmfully associated with a higher risk of spine fractures (p < 0.05). Obese participants diagnosed by both BMI and WC were less likely to have hip fractures but more likely to have spine fractures (all P for trend <0.05). A significant interaction between age (Ref: age < 50 years) and BMI and WC was detected for hip fractures (all P for interaction <0.05).DiscussionIn people with conditions influencing bone metabolism, obesity diagnosed by BMI and WC was associated with a lower risk of hip fracture, while obesity diagnosed by BMI and the highest WC quartile were associated with a higher risk of spine fracture

Exploring the prevalence and chest CT predictors of Long COVID in children: a comprehensive study from Shanghai and Linyi

IntroductionCOVID-19 constitutes a pandemic of significant detriment to human health. This study aimed to investigate the prevalence of Long COVID following SARS-CoV-2 infection, analyze the potential predictors of chest CT for the development of Long COVID in children.MethodsA cohort of children who visited the respiratory outpatient clinics at Shanghai Children's Medical Center or Linyi Maternal and Child Health Care Hospital from December 2022 to February 2023 and underwent chest CT scans within 1 week was followed up. Data on clinical characteristics, Long COVID symptoms, and chest CT manifestations were collected and analyzed. Multivariate logistic regression models and decision tree models were employed to identify factors associated with Long COVID.ResultsA total of 416 children were included in the study. Among 277 children who completed the follow-up, the prevalence of Long COVID was 23.1%. Chronic cough, fatigue, brain fog, and post-exertional malaise were the most commonly reported symptoms. In the decision tree model for Long COVID, the presence of increased vascular markings, the absence of normal CT findings, and younger age were identified as predictors associated with a higher likelihood of developing Long COVID in children. However, no significant correlation was found between chest CT abnormality and the occurrence of Long COVID.DiscussionLong COVID in children presents a complex challenge with a significant prevalence rate of 23.1%. Chest CT scans of children post-SARS-CoV-2 infection, identified as abnormal with increased vascular markings, indicate a higher risk of developing Long COVID