Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients’ data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used.

International audienceThe strength and geometry of the Atlantic meridional overturning circulation is tightly coupled to climate on glacial-interglacial and millennial timescales(1), but has proved difficult to reconstruct, particularly for the Last Glacial Maximum(2). Today, the return flow from the northern North Atlantic to lower latitudes associated with the Atlantic meridional overturning circulation reaches down to approximately 4,000 m. In contrast, during the Last Glacial Maximum this return flow is thought to have occurred primarily at shallower depths. Measurements of sedimentary Pa-231/Th-230 have been used to reconstruct the strength of circulation in the North Atlantic Ocean(3,4), but the effects of biogenic silica on Pa-231/Th-230-based estimates remain controversial(5). Here we use measurements of Pa-231/Th-230 ratios and biogenic silica in Holocene-aged Atlantic sediments and simulations with a two-dimensional scavenging model to demonstrate that the geometry and strength of the Atlantic meridional overturning circulation are the primary controls of Pa-231/Th-230 ratios in modern Atlantic sediments. For the glacial maximum, a simulation of Atlantic overturning with a shallow, but vigorous circulation and bulk water transport at around 2,000 m depth best matched observed glacial Atlantic Pa-231/Th-230 values. We estimate that the transport of intermediate water during the Last Glacial Maximum was at least as strong as deep water transport today

Abstract.A two dimensional scavenging model is used to investigate the patterns of sediment 231 Pa/ 230 Th generated by the Atlantic Meridional Overturning Circulation (AMOC) and further advance the application of this proxy for ocean paleocirculation studies. The scavenging parameters and the geometry of the overturning circulation cell have been chosen so that the model generates meridional sections of dissolved 230 Th and 231 Pa consistent with published water column profiles and an additional 12 previously unpublished profiles measured in the North and Equatorial Atlantic. The processes that generate the meridional sections of dissolved and particulate 230 Th, dissolved and particulate 231 Pa, dissolved and particulate 231 Pa/ 230 Th, and sediment 231 Pa/ 230 Th are discussed in detail. The results indicate that the relationship between sediment 231 Pa/ 230 Th at any given site and the overturning circulation is very complex. They clearly show that constraining past changes in the strength and geometry of the AMOC requires an extensive data set and they suggest strategies to maximize information from a limited number of samples.

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Photocatalytic oxygen reduction reaction (ORR) offers a promising hydrogen peroxide (H2O2) synthetic strategy, especially the one‐step two‐electron (2e−) ORR route holds great potential in achieving highly efficient and selectivity. However, efficient one‐step 2e− ORR is rarely harvested and the underlying mechanism for regulating the ORR pathways remains greatly obscure. Here, by loading sulfone units into covalent organic frameworks (FS‐COFs), we present an efficient photocatalyst for H2O2 generation via one‐step 2e− ORR from pure water and air. Under visible light irradiation, FS‐COFs exert a superb H2O2 yield of 3904.2 μmol h−1 g−1, outperforming most reported metal‐free catalysts under similar conditions. Experimental and theoretical investigation reveals that the sulfone units accelerate the separation of photoinduced electron‐hole (e−‐h+) pairs, enhance the protonation of COFs, and promote O2 adsorption in the Yeager‐type, which jointly alters the reaction process from two‐step 2e− ORR to the one‐step one, thereby achieving efficient H2O2 generation with high selectivity.

Despite the scientific consensus, some people still remain skeptical about climate change. In fact, there is a growing partisan divide over the last decade within the United States in the support for climate policies. Given the same climate evidence, why do some people become concerned while others remain unconvinced? Here we propose a motivated attention framework where socio-political motivations shape visual attention to climate evidence, altering perceptions of the evidence and subsequent actions to mitigate climate change. To seek support for this framework, we conducted three experiments. Participants viewed a graph of annual global temperature change while they were eyetracked and estimated the average change. We found that political orientation may bias attention to climate change evidence, altering the perception of the same evidence (Experiment 1). We further examined how attentional biases influence subsequent actions to mitigate climate change. We found that liberals were more likely to sign a climate petition or more willing to donate to an environmental organization than conservatives, and attention guides climate actions in different ways for liberals and conservatives (Experiment 2). To seek causal evidence, we biased attention to different parts of the temperature curve by coloring stronger climate evidence in red or weak climate evidence in red. We found that liberals were more likely to sign the petition or more willing to donate when stronger evidence was in red, but conservatives were less likely to act when stronger evidence was in red (Experiment 3). This suggests that drawing attention to motivationally consistent information increases actions in liberals, but discouraged conservatives. The findings provide initial preliminary evidence for the motivated attention framework, suggesting an attentional divide between liberals and conservatives in the perception of climate evidence. This divide might further reinforce prior beliefs about climate change, creating further polarization. The current study raises a possible attentional mechanism for ideologically motivated reasoning and its impact on basic perceptual processes. It also provides implications for the communication of climate science to different socio-political groups with the goal of mobilizing actions on climate change.

We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

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