DataSheet_1_Projecting National-Level Prevalence of General Obesity and Abdominal Obesity Among Chinese Adults With Aging Effects.docx

ObjectivesTo explore the impact of population aging on the projected prevalence of obesity among Chinese adults in 2030.MethodsIn total, 71450 observations were extracted from the China Health and Nutrition Survey between 1991 and 2015.Population was projected to 2030 using a Bayesian hierarchical modeling method. Two different approaches were adopted to estimate and project the national prevalence of overweight/obesity from 1991 to 2030. One method assumed a constant population at the base year, while the other allowed the age and gender distributions vary in each year.ResultsOur projection indicated that approximately two-thirds of Chinese adults would be affected by overweight/general obesity in 2030, and more than 60% of Chinese adults will suffer from abdominal obesity in 2030. Ignoring population aging led to an underestimation of overweight, general obesity and abdominal obesity for women by 3.81, 0.06, and 3.16 percentage points (pp), and overweight and abdominal obesity among men by 1.67 and 0.53 pp, respectively; but the prevalence of general obesity among men will be overestimated by 2.11 pp. Similar underestimations were detected in the estimation from 1991 to 2015.ConclusionsEstimating and projecting the national prevalence of obesity using a constant population structure at the base line would cause significant underestimation if countries are undergoing rapid population aging.</p

Nickel-Catalyzed Cascade Reaction of 2‑Vinylanilines with <i>gem</i>-Dichloroalkenes

An efficient nickel-catalyzed cascade reaction of 2-vinylanilines with gem-dichloroalkenes has been developed to deliver diversely substituted quinolines in good to high yields. This protocol enables effective access to quinolines bearing various functional groups in the cascade process from readily available feedstock chemicals. Mechanistic studies suggest that two plausible pathways are involved in the IPr–nickel catalytic system

Video_1_Comparative Efficacy and Safety of Anterograde vs. Retrograde Iodine Staining During Esophageal Chromoendoscopy: A Single-Center, Prospective, Parallel-Group, Randomized, Controlled, Single-Blind Trial.MP4

Background and Aim: Chromoendoscopy with iodine staining is an important diagnostic method for esophageal carcinomas or precancerous lesions. Unfortunately, iodine staining can be associated with numerous adverse events (AEs). We found that the starting position of spraying iodine solution is likely the main reason of causing AEs. We conducted this work to determine whether clinical outcomes from anterograde iodine staining were superior to those achieved after retrograde iodine staining.Methods: A total of 134 subjects with a health risk appraisal flushing (HRA-F) score of >6 for esophageal cancer were randomly assigned to receive anterograde or retrograde iodine staining in the esophagus. The primary endpoints were the pain and the amount of iodine solution consumption. The secondary endpoints were iodine-staining effect, detection yield, and response to starch indicator.Results: Nine patients suffered from pain and six patients revealed positive response to starch indicator in retrograde iodine-staining group; however, no patient reported pain (0/67) and all patients revealed a negative response to starch indicator in anterograde iodine-staining group. The amount of iodine solution consumption in anterograde iodine-staining group (4.97 mL) was significantly lower than that (6.23 mL) in retrograde iodine-staining group; however, the iodine-staining effect and detection yield were comparable between the two groups.Conclusions: Anterograde iodine staining during Lugol chromoendoscopy appears to be as effective, but significantly safer than retrograde iodine staining.</p

Synthesis of Indolo[2,1‑<i>a</i>]isoquinolines by Nickel-Catalyzed Mizoroki–Heck/Amination Cascade Reaction

An efficient Mizoroki–Heck/amination cascade reaction of o-dihaloarenes with cyclic imines was realized by combining nickel and a sterically bulky N-heterocyclic carbene ligand. This protocol provides access to a variety of indole[2,1-a]isoquinolines from readily available starting materials. This cascade approach could be applied to produce straightforward synthesis of the natural product cryptowoline

Literature research focus on and risk fields.

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.</div

Data search result of WOS.

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.</div

Summary of findings for literatures.

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.</div

Yearly publications volume about this theme.

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.</div

Data search result of Scopus.

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.</div

Capillary-Driven Boiling Heat Transfer on Superwetting Microgrooves

Boiling can transfer a vast amount of heat and thereby is widely used for cooling advanced systems with high power density. However, the capillary force of most existing wicks is insufficient to surpass the liquid replenishing resistance for high-efficient boiling. Herein, we report a new microgroove wick on high-conductive copper substrates that was constructed via ultraviolet nanosecond pulsed laser milling. The phase explosion, combined with melting and resolidification effects of laser milling induces dense microcavities with sizes around several micrometers on the microgroove surface. The hierarchical microstructures significantly improve the wettability of the microgroove wicks to obtain strong capillary and meanwhile provide abundant effective nucleation sites. The boiling heat transfer in a visualized flat heat pipe shows that the new wicks enable sustainable liquid replenishing even under antigravity conditions, thus resulting in maximum 33-fold improvement of equivalent thermal conductivity when compared with the copper base. This research provides both scientific and technical bases for the design and manufacture of high-performance phase change cooling devices