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Artificial Intelligence (AI) technology has been widely applied to Internet of Thing (IoT) and one of key applications is intelligence data collection from billions of IoT devices. However, many AI based data collection approaches lack security considerations leading to availability restricted. In this paper, an Intelligent Trust Collaboration Network System (ITCN) is established to collect data through collaboration with mobile vehicles and Unmanned Aerial Vehicle (UAV) for IIoT. The first, a deadline-aware data collection collaboration network framework is proposed by collaboration with mobile vehicles and UAV. The second, an active and verifiable trust evaluation approach is proposed to obtain the trust of the data participants, which ensures the security and privacy of the system. The last, a trust joint AI based UAV trajectory optimization algorithm is proposed to collect as much baseline data as possible, so more trust of data participants can be accurately evaluated and the data can be collected before deadline at low cost. The simulation results show that ITCN reduces the cost by 35.08% compared with using the UAV only, saves the collection time by 58.32% and increases the accuracy by 33.34% on average compared with the previous strategies only using vehicles.

As one of promising distributed multi-robot system, Unmanned Aerial Vehicles (UAVs) can collaborate to offload complex tasks in edge networks. A Smart and Trust Multi-UAV Task Offloading (STMTO) system is established to offload tasks from Internet of Thing (IoT) devices to edge severs through UAVs with a trust style. In STMTO system, first, a group of UAVs is dispatched to relay tasks from devices to edge servers with rich computing resource. A collaborative task collection scheme is proposed to minimize energy consumption and the task processing delay by dividing working area for each UAV and designing the flight trajectory . Secondly, a many-to-many task double auction model is established for devices and edge servers to maximize the offloading utility, where devices act as buyers, edge servers as sellers, and UAVs as auctioneers. Last, to resist attack of malicious edge servers and ensure the task security, a novel trust evaluation method based on the comparison of true utility and expected utility is integrated in auction mechanism. The theoretical analysis and implementation results show that the proposed STMTO system not only achieve the best utility for devices and edge servers simultaneously, but also identify the malicious edge servers and protect task from attacks.

With the continuous development of agricultural product e-commerce platforms and the rapid growth of trading volume, conducting in-depth mining and analysis of online review data to improve consumer satisfaction is of considerable importance. This paper uses JingDong’s self-run online reviews of rice agricultural products as the research object and analyzes the logistics factors that affect consumer satisfaction through text mining technology. A multi-objective model for logistics center distribution path optimization under a soft time window was constructed. The model used the results of online review analysis, namely, packaging integrity, delivery timeliness, and logistics cost, as the goals, and the model used ant colony algorithm (ACO) and genetic algorithm (GA) to solve the optimal distribution solution to minimize the penalty cost and transportation cost. Through examples to solve the optimal distribution vehicle number and shipping routes, in addition, a comparison of the two types of algorithm performance of the model under different node number indicated that the number of nodes affects algorithm performance. With a node number below 50, the ant ACO has high precision and a better distribution path. With a node number above 50, GA has more comprehensive performance. The average efficiency of the GA is 12.28% higher than that of ACO.

Climate change would pose a direct and severe challenge to China's rice production and self-sufficiency. Here we use Global Trade Analysis Project (GTAP) model to evaluate the impacts of climate change on the self-sufficiency of rice in China due to 2 °C rise in temperature, and further analyze the impact and transmission mechanisms of climate change through market and trade. Our simulation method switches different regional mechanisms in the GTAP model by employing alternative closures and scenarios. Our results suggest that the fluctuation in rice price is much larger than that of rice production. The active regional feedback mechanism has much larger effects than the passive one in assessing climate change impacts. With the passive regional feedback mechanism (Single Regional General Equilibrium, SRGE and Multi-Regional General Equilibrium with China's shock, MRGE_C), China's rice export would decrease by more than 8%, and import would increase by over 5%, which leads to a decrease in rice self-sufficiency by more than 0.06 percentage points. With the active regional feedback mechanism (Multi-Regional General Equilibrium with global shock, MRGE_W), climate change will increase China's rice export by 2.7% as rising rice exports to South Korea overweight the export decrease to other countries, concurrently, China's rice import would decrease by 0.04%, which leads to a slight increase in rice self-sufficiency. As a whole, climate change will not severely threaten China's rice self-sufficiency. By the decomposition analysis, climate change impacts on China's rice self-sufficiency mainly depend on the changes in China's domestic rice production. Interestingly, the source of China's rice import will switch from Vietnam to Pakistan under MRGE_W, which derives from the input structure of paddy rice and rice sectors in different countries.

Background: Dual antiplatelet therapy combining aspirin with a P2Y12 adenosine diphosphate receptor inhibitor is a therapeutic mainstay for acute coronary syndrome (ACS). However, the optimal choice of P2Y12 adenosine diphosphate receptor inhibitor in elderly (aged ≥65 years) patients remains controversial. We conducted a meta-analysis to compare the efficacy and safety of ticagrelor and clopidogrel in elderly patients with ACS. Methods: We comprehensively searched in Web of Science, EMBASE, PubMed, and Cochrane databases through 29th March, 2021 for eligible randomized controlled trials (RCTs) comparing the efficacy and safety of ticagrelor or clopidogrel plus aspirin in elderly patients with ACS. Four studies were included in the final analysis. A fixed effects model or random effects model was applied to analyze risk ratios (RRs) and hazard ratios (HRs) across studies, and I2 to assess heterogeneity.Results: A total number of 4429 elderly patients with ACS were included in this analysis, of whom 2170 (49.0%) patients received aspirin plus ticagrelor and 2259 (51.0%) received aspirin plus clopidogrel. The ticagrelor group showed a significant advantage over the clopidogrel group concerning all-cause mortality (HR 0.78, 95% CI 0.63–0.96, I2 = 0%; RR 0.79, 95% CI 0.66–0.95, I2 = 0%) and cardiovascular death (HR 0.71, 95% CI 0.56–0.91, I2 = 0%; RR 0.76, 95% CI 0.62–0.94, I2 = 5%) but owned a higher risk of PLATO major or minor bleeding (HR 1.46, 95% CI 1.13–1.89, I2 = 0%; RR 1.40, 95% CI 1.11–1.76, I2 = 0%). Both the groups showed no significant difference regarding major adverse cardiovascular events (MACEs) (HR 1.06, 95% CI 0.68–1.65, I2 = 77%; RR 1.04, 95% CI 0.69–1.58, I2 = 77%).Conclusion: For elderly ACS patients, aspirin plus ticagrelor reduces cardiovascular death and all-cause mortality but increases the risk of bleeding. Herein, aspirin plus ticagrelor may extend lifetime for elderly ACS patients compared with aspirin plus clopidogrel. The optimal DAPT for elderly ACS patients may be a valuable direction for future research studies.

Shen Gui capsule (SGC) has been demonstrated to have a significant treatment effect for coronary heart disease (CHD). Nevertheless, the holistic therapeutic mechanism of SGC in vivo remain poorly interpreted. We aimed to systematically explore the preventive effect and mechanism of SGC on CHD rats using plasma metabolomics strategy. Rat CHD model was established by left anterior descending coronary artery ligation (LAD). Echocardiography, histological analyses of the myocardium and biochemical assays on serum were used to confirm the successful establishment of the CHD model and therapeutic effects of SGC. Then, UHPLC-MS/MS-based plasma metabolomics was combined with multivariate data analysis to screen potential pharmaco biomarkers associated with SGC treatment in the LAD-induced rat CHD model. After SGC treatment, 12 abnormal metabolites considered as potiential pharmaco biomarkers recovered to near normal levels. These biomarkers were involved in several metabolic pathways, including fat and protein metabolism, phenylalanine metabolism, neuroactive ligand-receptor interaction, androgen receptor signaling pathway, and estrone metabolism.These results suggested that SGC achieves therapeutic action on CHD via regulating various aspects of the body such as energy metabolism, neurological disturbances and inflammation, and thus plays a significant role in the treatment of CHD and its complications. The study is useful to systematically understand and analyze the mechanism of SGC's "multipie pathways, multiple levels, multiple targets" prevention and treatment of CHD.

Phthalates are hormonally active pollutants. In-utero exposure to phthalates has been reported to be associated with birth size parameters and pregnancy outcomes. However, previous reports were inconsistent. We examined the associations between meconium exposure to phthalates and the effects on birth size parameters, pregnancy outcomes and sex and thyroid hormones in 251 mother&ndash, infant pairs from a Shanghai hospital. We measured 10 metabolites of phthalates in meconium samples collected during the first 24h after delivery. Information on seven birth size parameters (birth weight, birth length, abdominal circumference, head circumference, femur length, biparietal diameter and anogenital distance) and three pregnancy outcomes (gestational diabetes, premature rupture of membrane, and premature birth) was available from the birth record. Concentrations of free testosterone, estradiol (E2), thyroid stimulating hormone, concentrations of total and free thyroxine and triiodothyronine were measured from cord blood. Multivariate linear regression and logistic regression were used to estimate associations between phthalate exposure and health outcomes. mono-iso-butylphthalate (MiBP), mono-n-butylphthalate (MnBP) and mono-2-ethyl-5-oxohexyl phthalate (MEOHP) were positively associated with birth length and femur length which seemed more obvious in female newborn, MiBP, MnBP and mono-2-ethylhexylphthalate (MEHP) were positively associated with gestational diabetes mellitus (GDM) only in mothers with male newborns, monomethyl phthalate (MMP), MiBP and MEOHP were positively associated with E2 in male newborns. This study indicates that meconium exposure to phthalates may adversely affect some fetal growth parameters and GDM with a potential gender effect.

In this article, a comprehensive parameter analysis for the polling-based medium access control protocol is executed. The theoretical expressions of the relationship between network parameters and performance including delay and energy consumption are given for the first time. The specific conclusions in this article are as follows: (1) awake duration is the parameter that has the greatest impact on delay and energy consumption. Increasing the duty cycle (awake duration) will effectively reduce the delay, but will also increase the energy consumption within a certain range; (2) increasing polling duration can reduce the delay, but it will also increase the energy consumption; and (3) more forwarding nodes cause a smaller delay, and it can save the energy with modest increase of delay by reducing the polling duration. An adaptive parameter optimization polling-based medium access control protocol is proposed to optimize network performance. In this protocol, the residual energy gets fully used to increase awake duration and polling duration, which makes the delay smaller, and the network maintains a long lifetime meanwhile. Based on the results of the analysis, the adaptive parameter optimization polling-based medium access control protocol proposed in this article reduces the delay by 22.40% and increases the energy efficiency by 23.25%.

With the integration of Artificial Intelligence (AI) and Internet of Things (IoT), the Federated Edge Learning (FEL), a promising computing framework is developing. However, there are still unsolved issues on communication efficiency and data security due to the huge models and unreliable transmission links. To address these issues, this paper proposes a novel federated edge learning system, called LightFed, where the edge nodes upload only vital partial local models, and successfully achieve lightweight communication and model aggregation. First, a novel model aggregation method Model Splitting and Splicing (MSS) and a Selective Parameter Transmission (SPT) scheme are proposed. By detecting the updating gradients of local parameters and filtering significant parameters, selective rotated transmission and efficient aggregation of local models are achieved. Second, a Training Filling Model (TFM) is proposed to infer the total data distribution of edge nodes, and train a filling model to mitigate the unbalanced training data without violating the data privacy of individual users. Moreover, a blockchain-powered confusion transmission mechanism is proposed for defending the attacks from external adversaries and protecting the model information. Finally, extensive experimental results demonstrate that our LightFed significantly outperforms the existing FEL systems in terms of communication efficiency and privacy security.

Doping nitrogen in TiO2 crystals is a simple and effective approach to extend the absorption spectrum of TiO2 to visible light range, but it leads to a serious decline in charge transfer and total loss of the catalytic ability for overall water splitting. Here, TiO2 was co-doped with phosphorus and nitrogen by high temperature method and found the sample of P–N–TiO2-I could realize overall water splitting under visible light while the samples of P–N–TiO2-II, P–N–TiO2-III and N–TiO2 cannot do it. The structures of the photocatalysts were described by various analytical techniques. Through XRD and HRTEM, lots of long-range defect such as twins and dislocations could be clearly observed for P–N–TiO2-II, P–N–TiO2-III and N–TiO2. But for P–N–TiO2-I, the long-range defect was decreased drastically. We proposed that because of various sizes and bonding modes of N, O, P and Ti, proper mole ratios of P to N replace Ti and O respectively can reduce the long-range defect, as that of P–N–TiO2-I. Furthermore, this co-doping effect results in a significant improvement in the separation of photo-excited electrons and holes and enables the nitrogen-doping TiO2 to catalyze the overall splitting of water into molecular hydrogen and oxygen under visible light irradiation, in the absence of sacrificial agents. These observations might arouse new strategies to mend the hybrid-atom-doping-induced structural damage in semiconductor crystals and to modulate their functional properties.

ObjectivesThis study aimed to explore the cognitive development of low-risk children during early childhood for early-term births at 37 and 38 weeks of gestation compared with full term births at 39–41 weeks of gestation.Setting and participantsWe conducted a cross-sectional study in Shanghai, one of the largest cities in China. A total of 1444 children from singleton pregnancies born at term gestation were included in the study.MeasuresThe cognitive outcomes of the subjects were measured using the cognitive subtest of Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) across three cities in China. We analysed the association between gestational age and cognitive development during infancy and toddler stages using multivariate linear modelling.ResultsThe cognitive development scores for infants born at 37 gestational weeks were significantly lower than those born at 39–41 gestational weeks (β=−2.257, 95% CI −4.280 to −0.235; p0.05). Moreover, these effects were not found in toddlers (between 17 and 48 months of age) after adjusting for the possible confounders (p>0.05).ConclusionsInfants born at 37 weeks of gestation exhibited weaker cognitive ability compared with those born at 39–41 weeks of gestation. Our findings provide evidences for the close monitoring of potential developmental problems in early-term children, especially those born at 37 gestational weeks.

Recent data suggests that abnormal maternal pre-pregnancy body mass index (BMI) or gestational weight gain (GWG) is associated with unfavorable delivery outcomes. However, limited clinical evidence is available to support this correlation in China. Participating 510 mother-infant pairs were recruited from the Shanghai First Maternity and Infant Hospital, China, between January 1st and 30th 2016. Maternal pre-pregnancy BMI was categorized according to the China’s classification and GWG according to the 2009 Institute of Medicine recommendations (IOM). Linear regression tested the associations between pre-pregnancy BMI or GWG and length of gestation, birthweight, length, and head circumference. Logistic regression assessed the associations between pre-pregnancy BMI or GWG and macrosomic, small- (SGA) and large- (LGA) for-gestational-age infants. Overweight/obese women showed increased length of gestation and birthweight, but did not have a higher risk of macrosomic and LGA infants compared with normal weight women. Women with excessive GWG showed increased length of gestation, birthweight, length, and head circumference, and were more likely to deliver macrosomic and LGA infants compared with women with adequate GWG. Although a relatively low proportion of women from Shanghai area are overweight/obese or exhibit excessive GWG, both high pre-pregnancy BMI and excessive GWG influence perinatal outcomes.

Rural financial development has played a vital role in the process of agricultural economic growth. This article explores the relationship between rural financial development and agricultural economic growth by utilizing China’s time series data from 1978 to 2011. The empirical result shows that there is a long-run equilibrium relationship between China’s rural financial development and agricultural economic growth, and the rural financial development has a negative effect on agricultural economic growth. RFD is the Granger Cause of AEG in 10% significance level. Based on these empirical results, some rural financial service policies have also been proposed so as to ensure the smooth running of the rural economy.