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Abstract This article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts.

The cortisol in human body is a crucial biomarker in terms of wellness management, mental state monitoring and stress-related disorder diagnosis. Therefore, the rapid, reliable and facile measurement of cortisol concentration has attracted extensive research interest. However, traditional cortisol detection such as immunosensing requires demanding laboratory layout, lengthy procedures and high costs, which means, consequently, it is incompatible with the current goal of cortisol sensing. Given the contradiction, an electrochemical sensor based on molecularly imprinted polymer (MIP) for simple, efficient, non-invasive cortisol detection was proposed. The two-step approach employed is simple enough and allows for the mass production of devices. And the embedding of Prussian Blue (PB) within the MIP layer eliminates the need for complex external probes, thereby making the resultant sensors more suitable for integration into wearable devices. We firstly demonstrated the feasibility of the proposed strategy and characterized the successful formation of cavities specific to cortisol molecules. Thereafter, we measured the dependence of the current response on cortisol concentration in Phosphate Buffered Saline (PBS) buffer, which revealed a near-linear relationship between the logarithm of the cortisol concentration and the redox current from 10−9 mol/L to 10−5 mol/L, covering the optimal range of cortisol concentration in sweat. Subsequently, sensors with the same specifications were prepared and tested in PBS buffer, exhibiting good consistency. In artificial sweat, we further demonstrated that they have benign selectivity, interference immunity and great potential in practical applications.

Flexible pressure sensors capable of detecting normal and tangential forces through physical contact have garnered considerable interest in the realm of human-interactive systems. However, simultaneous detection of multi-directional forces is still a challenge for current research. Herein, a capacitive flexible pressure sensor based on a sandwich structure for three-dimensional force detection is proposed. The fabrication process of the sensor array is straightforward, capable of effectively distinguishing between normal and tangential forces. Polyimide (PI) serves as the flexible substrate for depositing the metal electrode pattern, while Polydimethylsiloxane (PDMS) acts as the intermediate dielectric layer material and the three-dimensional force conduction block. Through a comparative study of the thickness of the hollow dielectric layer, a pressure sensor with superior performance was prepared, featuring high sensitivity across a wide working range. Test results demonstrate its capability to detect normal forces ranging from 0 to 46 N (0–520 kPa) with a sensitivity of 0.442 N−1 (0.031 kPa−1) and tangential forces from 0 to 10 N with a sensitivity of 0.08 N−1 (X-axis) and 0.07 N−1 (Y-axis). The designed acquisition system can simultaneously gather data from 6 sensor arrays, totaling 240 channels, with a response time of 11 ms. This sensor array, characterized by flexibility, versatility, and a wide range, is suitable for applications in robot tactile perception.

Multi-channel neural electrodes as a crucial means are of great significance for information exchange between the brain and computers. Herein, we present a 16-channel Si-based active neural probe system that achieves a monolithic integration between the electrodes and circuits in a single probe, making it a standalone integrated electrophysiology recording system. The ASIC prepared on a base (2×2mm2) is a 16-channel analog frontend (AFE) for neural recording, and each channel has a low-noise amplifier (LNA), a bandpass filter (BPF), a buffer and a current bias circuit. The 258 neural signal recording electrodes (22×24μm2) are densely packed on a 50 μm thick, 100 μm wide, and 3 mm long shank. The ASIC of neural probe, internal interconnecting wires are all implemented in commercial SMIC 0.18 μm CMOS technology. The neural probe system achieves a 3.6 μVrms input-referred noise (IRN) in a bandwidth of 1.1Hz-10 kHz, 70.8 μW power consumption, 0.0785 mm2 area per channel, as well as an AFE gain of 58.1 dB Furthermore, the impedances of the Au electrodes can be obtained as 0.5–2.1 MΩ at a frequency of 1 kHz. The functionality of a 16-channel silicon-based neural probe is validated in an in-vivo experiment on lab rats.

To avoid the secondary frequency dip caused by the steep drop of the electrical power of wind turbines (WTs) at the end of frequency support stage, the torque limit-based iner-tial control (TLIC) method sets the power reference as a linear function of rotor speed, rather than the step form for the step-wise inertial control. However, the compensation effect on the frequency nadir (FN) caused by the load surge is weakened as the TLIC power is no longer in the step form. Specifically, the maximum point of the frequency response component (FRC) contributed by TLIC occurs earlier than the minimum point of FRC corresponding to the load surge, so that the FN cannot be adequately raised. Therefore, this paper first investigates the relation between the peak and nadir times of FRCs stimulated by the TLIC and load power. On this basis, with the compensation principle of matching the peak and nadir times of FRCs, the improved TLIC method based on delayed support is proposed. Finally, the effectiveness of the proposed method is validated via the experiments on the test bench of wind-integrated power system.

The Kangdian region is an important U metallogenic belt in southwestern China and has global significance for investigations of Neoproterozoic U metallogenesis. In this study, we reviewed the findings of previous studies and determined that U mineralization is associated with high-grade metamorphic, Na-rich volcano-sedimentary, and low-grade metasedimentary ore-bearing rocks. Based on geochronology data, U mineralization in the Kangdian region occurred at 830–700 Ma. Neoproterozoic U mineralization in the Kangdian region has been attributed to the breakup of the Rodinia supercontinent. The mineralization is linked to magmatism that included a low degree of partial melting of U-rich basement in an extensional setting. Favorable Neoproterozoic geological and mineralization events in the Kangdian region produced U mineralization, which suggests that this region may contain U deposits. Therefore, studying Neoproterozoic U mineralization in the Kangdian region is important for enhancing the current understanding of the evolution of Rodinia with respect to global events and associated mineralization.

Summary: New alien species are increasingly introduced and established outside their native range. The knowledge of the spatiotemporal dynamics of their accumulation and the factors determining their residence time in the introduced range is critical for proactive management, especially in emerging economies. Based on a comprehensive time series dataset of 721 alien angiosperms in China, we show that new alien flora has been accumulating steadily in China, particularly in the coastal regions, for the last 80 years without saturation. The ability to occupy a large number of habitats facilitates the early introduction of alien flora, whereas a large naturalized range, greater number of uses, and multiple introduction pathways directly contribute to their naturalization and invasion. The temporal pattern is predicted to remain consistent in the foreseeable future. We propose upgrading the country’s biosecurity infrastructure based on a standardized risk assessment framework to safeguard the country from ongoing and future invasions.

This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately. With integration of distributed optimization and data-driven distributionally robust optimization, the DDDR-VVC model is constructed to reduce power losses and maintain voltage within allowable range under uncertainty. To fully address the uncertainty impacts, construction of uncertain set is improved through that the probability distribution sets and trapezoidal fuzzy functions are developed to model PV supply and load demand respectively. An accelerated alternating optimization procedure which integrates the alternating direction method of multipliers and the column-and-constraint generation algorithm is proposed. This method improves the dual information update via Nesterov’s accelerated gradient method, thus solving the DDDR-VVC model at a high convergence rate. The effectiveness of proposed method is verified through numerical simulations using IEEE 123 bus test system.

Model predictive control is an effective approach for microgrid energy management. However, the main downside of such method is its expensive online computational cost, which is not amenable to most practical microgrid implementations. To address this issue, we propose a deep neural network assisted column generation approach that can accelerate the solution procedure of model predictive control. In each iteration, our approach leverages different deep neural networks to predict the optimal solutions of all the subproblems in column generation, which can accelerate the computation of all the subproblems and the entire process of column generation. The pre-existing knowledge of the microgrid is also exploited to guarantee the feasibility of the neural network outputs using multi-parametric programming theory. The numerical results show that our approach leads to a reduction in computational time of approximately 50% in a medium-sized microgrid compared with the full mixed integer solution based on traditional branch and bound method, while the optimality loss is only 0.02% in terms of operating costs.

IntroductionPhylogenetic relatedness is one of the important factors in the community assembly process. Here, we aimed to understand the large-scale phylogenetic relationship between alien plant species at different stages of the invasion process and how these relationships change in response to the environmental filtering process at multiple spatial scales and different phylogenetic extents.MethodsWe identified the alien species in three invasion stages, namely invasive, naturalized, and introduced, in China. The occurrence records of the species were used to quantify two abundance-based phylogenetic metrics [the net relatedness index (NRI) and the nearest taxon index (NTI)] from a highly resolved phylogenetic tree. The metrics were compared between the three categories of alien species. Generalized linear models were used to test the effect of climate on the phylogenetic pattern. All analyses were conducted at four spatial scales and for three major angiosperm families.ResultsWe observed significantly higher NRI and NTI values at finer spatial scales, indicating the formation of more clustered assemblages of phylogenetically closely related species in response to the environmental filtering process. Positive NTI values for the invasive and naturalized aliens suggested that the presence of a close relative in the community may help the successful naturalization and invasion of the introduced alien species. In the two-dimensional phylogenetic space, the invasive species communities significantly differed from the naturalized and introduced species, indicating that established alien species need to be phylogenetically different to become invasive. Positive phylogenetic measures for the invasive aliens across the spatial scales suggested that the presence of invasive aliens could facilitate the establishment of other invasive species. Phylogenetic relatedness was more influenced by temperature than precipitation, especially at a finer spatial scale. With decreased temperature, the invasive species showed a more clustered assemblage, indicating conservatism of their phylogenetic niche. The phylogenetic pattern was different at the family level, although there was a consistent tendency across families to form more clustered assemblages.DiscussionOverall, our study showed that the community assemblage became more clustered with the progression of the invasion process. The phylogenetic measures varied at spatial and taxonomic scales, thereby highlighting the importance of assessing phylogenetic patterns at different gradients of the community assembly process.

Optimal torque (OT) control is a widely used method for maximum power point tracking (MPPT) due to its simplicity. In order to overcome the adverse impacts of turbulent wind speed variations on MPPT, in several methods, modification factors have been proposed to dynamically modify the ideal power curve for OT control. However, this paper finds that the update cycles used in existing methods to adjust power curve modification factors are very long and hence these factors are difficult to be updated in a timely manner along with the wind speed variations. This thereby may deteriorate the effectiveness of wind energy extraction. Therefore, an optimal decreased torque gain (DTG) control method is proposed in this paper. Based on the persistence method, an offline mapping from the wind speed and rotor speed to optimal modification factors is established via optimal control theory. The power curve can be periodically modified online through the mapping relationship. In this method, the update cycles for these power curve modification factors are shortened from tens of minutes to seconds. The simulations and experiments show that the proposed method is more efficient than others in terms of energy extraction under varying wind speeds, especially for turbulent wind cases.

Pulmonary hypertension is a progressive fatal disease that currently has no specific therapeutic approaches. In this study, dsRNA‐dependent protein kinase (PKR) was considered a candidate molecule in pulmonary hypertension. We demonstrated that PKR is activated in the endothelium of experimental pulmonary hypertension models. Deletion of PKR or treatment with the PKR activation inhibitor C16 inhibited the development of pulmonary hypertension. To explore the mechanism of PKR in pulmonary hypertension, we detected its downstream signaling and found that PKR knockout represses apoptosis‐associated speck‐like protein containing CARD (ASC) activation to inhibit high mobility group box 1 (HMGB1) and interleukin‐1 beta release. To further explore whether ASC mediates the pro‐pulmonary hypertension role of PKR, we used ASC deletion mice and found that ASC deletion inhibits the development of pulmonary hypertension and the release of HMGB1 and interleukin‐1 beta. Furthermore, we co‐cultured pulmonary arterial endothelial cells (PAECs) and pulmonary arterial smooth muscle cells (PASMCs) and found that endothelial PKR promotes PASMCs proliferation through the release of HMGB1 and interleukin‐1 beta. In conclusion, these data indicate that endothelial PKR promotes the excessive proliferation of PASMCs by inducing ASC activation to release HMGB1 and interleukin‐1 beta, which lead to the development of pulmonary hypertension. Our study will provide a novel insight that PKR is a potential target in the future treatment of pulmonary hypertension.

Due to the frailty of elderly individuals’ physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher costs and complex wearable designs, limiting their wide-ranging applicability. In this paper, we propose a fall detection method based on nursing aids integrated with multi-array flexible tactile sensors. We design a kind of multi-array capacitive tactile sensor and arrange the distribution of tactile sensors on the foot based on plantar force analysis and measure tactile sequences from the sole of the foot to develop a dataset. Then we construct a fall detection model based on a graph convolution neural network and long-short term memory network (GCN-LSTM), where the GCN module and LSTM module separately extract spatial and temporal features from the tactile sequences, achieving detection on tactile data of foot and walking states for specific time series in the future. Experiments are carried out with the fall detection model, the Mean Squared Error (MSE) of the predicted tactile data of the foot at the next time step is 0.0716, with the fall detection accuracy of 96.36%. What is more, the model can achieve fall detection on 5-time steps with 0.2-s intervals in the future with high confidence results. It exhibits outstanding performance, surpassing other baseline algorithms. Besides, we conduct experiments on different ground types and ground morphologies for fall detection, and the model showcases robust generalization capabilities.

Fully convolutional networks (FCNs) have been widely applied for dense classification tasks such as semantic segmentation. As a large number of works based on FCNs are proposed, various semantic segmentation models have been improved significantly. However, duplicated upsampling and deconvolution operations in the FCNs will lead to information loss in semantic segmentation tasks and to problems such as ignoring the relationship between pixels and pixels and the lack of spatial consistency. In this study, we propose a parallel fully convolutional neural network that integrates holistically-nested edge detection (HED) network to capture image edge information, improving semantic segmentation performance. We carry out comprehensive experiments and achieve a better result on the PASCAL VOC 2012, PASCAL-Context and Cityscapes, comparing the results with some existing semantic segmentation methods.

Pulmonary hypertension (PH) is an extremely serious cardiopulmonary disease, finally leading to progressive right ventricular failure and death. Our previous studies have nominated HLQ2g, a pyrazolo[3,4-b] pyridine derivative stimulating soluble guanylate cyclase (sGC), as a new candidate for the treatment of PH, but the specific mechanism is still not clear. The PH model induced by hypoxia was established in rats. Right ventricular systolic pressure (RVSP) was assessed by jugular vein catheterization. RV weight was the index to evaluate RV hypertrophy. The protein levels of cGMP-dependent protein kinase type I (cGKI), bone morphogenetic protein receptor 2 (BMPR2), phosphorylated Smad1/5/8 (p-Smad1/5/8), and inhibitor of differention 1 (Id1) in pulmonary artery and human pulmonary artery smooth muscle cells (HPASMCs) were determined by western blotting. Cell proliferation and migration were evaluated. In the whole experiment, the first clinically available sGC stimulator Riociguat was used as the reference. In hypoxic PH rat model, elevated RVSP and RV hypertrophy were significantly reduced by HLQ2g treatment. Both Riociguat and HLQ2g attenuated vascular remodeling accompanied with up-regulated cGKI expression and BMP signaling pathway, which was characterized by elevated expression of BMPR2, p-Smad1/5/8, and Id1 in HPH rats. In addition, HLQ2g inhibited proliferation and migration of HPASMCs induced by hypoxia and platelet-derived growth factor (PDGF), restored BMPR2 signaling, which was recalled by Rp-8-Br-PET-cGMPS, the inhibitor of cGKI. In summary, the novel pyrazolo[3,4-b] pyridine derivative HLQ2g can alleviate HPH progression by up-regulating cGKI protein and BMP signaling pathway.

Abstract Background Breast cancer (BC) is the most common cancer in women worldwide and leading cause of cancer deaths indeveloping countries. There is very limited data on BC in the Central African Republic. The purpose of this study was to describe the epidemiological and histopathological characteristics of BC in Bangui. Methods This retrospective study reviewed cancer data registries and medical records from the Pathology Unit of the National Laboratory in Bangui and the General Surgery and Gyneacology service from 2003 to 2015. A questionnaire was designed to collect information and data was analysed using descriptive and inferential statistical methods. Results In total, 174 cases of BC were recorded, with an average annual frequency of13.4 cases per year. The age of the women at diagnosis varied from 16 to 90 years with a median of 45.5 years and InterQuartile range (IQR) 18 years. The age group of 45–54 years represented the majority of the study population (n = 51, 29.3%).About 25.9%ofthe patients were non-educated and 85.6% lived in cities. Over 48 % of the women were housewives with a moderate economic status (n = 99, 56.9%). Sixty nine percent of the specimens received at the pathology unit were pieces of breast tumour. Invasive ductal carcinoma (n = 113, 64.9%) was the main histological form and most of the tumours were of Grade III (n = 14, 46.7%). The only imaging assessment was ultrasound performed in (n = 53, 30.4%) women. Surgery was performed in (n = 166, 95.4%) patients, while (n = 159, 91.4%) received complementary chemotherapy. At the end of the study, 84.5%of the cases had died, 12.1% were alive and 3.4% were considered “lost to follow-up”. Conclusion BC is an important public health problem and affected most of the younger Central African women. Epidemiological and histological characteristics are more or less common to those described other developing countries. It is imperative to improve the awareness of health care institutions and women on the burden of BC, to carry out early screening of BC, and to strengthen the capacity of women’s health care system.

Abstract As the power control technology of wind farms develops, the output power of wind farms can be constant, which makes it possible for wind farms to participate in power system restoration. However, due to the uncertainty of wind energy, the actual output power can’t reach a constant dispatch power in all time intervals, resulting in uncertain power sags which may induce the frequency of the system being restored to go outside the security limits. Therefore, it is necessary to optimize the dispatch of wind farms participating in power system restoration. Considering that the probability distribution function (PDF) of transient power sags is hard to obtain, a robust optimization model is proposed in this paper, which can maximize the output power of wind farms participating in power system restoration. Simulation results demonstrate that the security constraints of the restored system can be kept within security limits when wind farm dispatch is optimized by the proposed method.

Wind turbine simulator (WTS) is an important test rig for validating the control strategies of wind turbines (WT). Since the inertia of WTSs is much smaller than that of WTs, the inertia compensation scheme is usually employed in WTSs for replicating the slow mechanical behavior of WTs. In this paper, it is found that the instability of WTSs applying the inertia compensation scheme, characterized by the oscillation of compensation torque, is caused by the one-step time delay produced in the acceleration observation. Hence, a linear discrete model of WTS considering the time delay of acceleration observation is developed and its stability is analyzed. Moreover, in order to stably simulate WTs with large inertia, an improved inertia compensation scheme, applying a first-order digital filter to mitigate deviation response induced by the time delay, is proposed. And, the criterion for selecting the filter coefficients is established based on the stability condition analysis. Finally, the WTS with the proposed scheme is validated by simulations and experiments.

Abstract Background Breast cancer is recognised as a major public health problem in developing countries; however, there is very limited evidence about its epidemiology in the Central African Republic. The aim of this study was to investigate the epidemiological and histopathological characteristics of breast cancer in Bangui. Methods This is a retrospective study based on the data collected from pathological anatomy records from 2003 to 2015 in Bangui. A questionnaire was designed to collect information and data was analysed using descriptive and inferential statistical methods. Results The mean age was 45.83 (SD = 13.5) years. The age group of 45–54 years represented the majority of the study population (29.3%). Over 69.5% of the women were housewives with a moderate economic status (56.9%). Less than 14% of the study population had a level of academic degree and 85.6% lived in cities. The breast cancer prevalence was 15.27%. The age-standardized incidence and death by world population (ASW) were 11.19/100,000 and 9.97/100,000 respectively. 50–54 years were most affected. Left breast cancer is mainly common and the time between first symptoms and consultation is more than 48 months. Most (69%) of the samples analysed were lumpectomy. The most common morphology of breast cancer was invasive ductal carcinoma (64.9%). Scarff Bloom Richardson III was the main grade in both common pathological types, but their proportion showed no significant increase along with time (χ2 = 7.06, p = 0.54). Invasion of regional lymph node differed significantly among the pathological type of breast cancer (χ2 = 24.6, p = 0.02). Surgery and chemotherapy were appropriate treatment yet 84.5% of the cases died. Conclusion The findings of this study showed that breast cancer is common and mostly affected women. Epidemiological trends are more or less common to those of developing countries with a predominance of invasive ductal carcinoma. However, most of the women studied live in an urban area and developed the disease in advanced stage. The establishment of an appropriate framework will effectively contribute to promoting the early detection and reducing the incidence of this disease in the population.

Breast cancer is recognized as a major public health problem in developing countries; however, there is very little evidence of behavioral factors associated with breast cancer risk. This study was conducted to identify lifestyles as risk factors for breast cancer among Central African women. A case-control study was conducted with 174 cases confirmed histologically by the pathology unit of the National Laboratory and 348 age-matched controls. Data collection tools included a questionnaire with interviews and medical records of patients. Data were analyzed using SPSS software version 20. Odd ratio (OR) and 95% confidence intervals (95% CI) were obtained by unconditional logistic regression. In total, 522 women were studied with a mean age of 45.8 (SD = 13.4) years. By unconditional logistic regression model, women with breast cancer were more likely to have attained illiterate and elementary education level [11.23 (95% CI, 4.65-27.14) and 2.40 (95% CI, 1.15-4.99)], married [2.09 (95% CI, 1.18-3.71)], positive family history [2.31 (95% CI, 1.36-3.91)], radiation exposure [8.21 (95% CI, 5.04-13.38)], consumption charcuterie [10.82 (95% CI, 2.39-48.90)], fresh fish consumption [4.26 (95% CI, 1.56-11.65)], groundnut consumption [6.46 (95% CI, 2.57-16.27)], soybean consumption [16.74 (95% CI, 8.03-39.84)], alcohol [2.53 (95% CI, 1.39-4.60)], habit of keeping money in bras[3.57 (95% CI, 2.24-5.69)], overweight [5.36 (95% CI, 4.46-24.57)] and obesity [3.11(95% CI, 2.39-20.42)]. However, decreased risk of breast cancer was associated with being employed [0.32 (95% CI, 0.19-0.56)], urban residence [0.16 (95% CI, 0.07-0.37)], groundnut oil consumption [0.05 (95% CI, 0.02-0.14)], wine consumption [0.16 (95% CI, 0.09-0.26)], non habit of keeping cell phone in bras [0.56 (95% CI, 0.35-0.89)] and physical activity [0.71(95% CI, 0.14-0.84)]. The study showed that little or no education, marriage, positive family history of cancer, radiation exposure, charcuterie, fresh fish, groundnut, soybean, alcohol, habit of keeping money in bras, overweight and obesity were associated with breast cancer risk among Central African women living in Bangui. Women living in Bangui should be more cautious on the behavioral risk associated with breast cancer.

The aims of this study are to identify the most important predictors of total diagnosed and undiagnosed diabetes and estimate the mean change in the predicted probability among aged 45+ adults in China. We used baseline data collected from 2011 wave of the China Health and Retirement Longitudinal Study (CHARLS) (n = 9,513). First, we estimated the prevalence of diagnosed, measured, total diagnosed, and undiagnosed diabetes. Second, we used probit models to determine whether individual attributes, socioeconomic characteristics and behavioral health factors, including smoking, alcohol consumption, obesity, central obesity, are associated with total diagnosed and undiagnosed diabetes. We also consider other factors, including contact with medical system, hypertension and urban/rural settings. Third, we estimated average marginal effects of variables in probit models. Among Chinese people aged 45+, the prevalence of diagnosed, measured, total diagnosed and undiagnosed diabetes were 5.8% (95%CI, 5.3%-6.3%), 14.7% (95%CI, 14.0%-15.4%), 17.0% (95%CI, 16.3%-17.7%), 11.3% (95%CI, 10.6%-12.0%), respectively. The probability of total diagnosed diabetes is 3.3% (95% CI, 1.2%-5.3%) and 10.2% (95% CI, 7.0%-13.5%) higher for overweight and obesity than normal BMI, 5.0% (95% CI, 3.0%-7.1%) higher for central obesity than normal waist circumference, 5.4% (95% CI, 3.7%-7.0%) higher for hypertensive than normotensive and 1.8% (95% CI, 0.8%- 2.7%) higher in urban areas than in rural areas, respectively. The probability of undiagnosed diabetes is 2.7% (95% CI, 1.2%-4.2%) and 7.2% (95% CI, 4.7%-9.6%) higher for overweight and obesity than normal BMI, 2.6% (95% CI, 0.9%-4.4%) higher for central obesity than normal waist circumference and 2.6% (95% CI, 1.2%-4.0%) higher for hypertensive than normotensive, respectively, and -1.5% (95% CI, -2.5% to -0.5%) lower for individuals who were in contact with the medical system. Greater focus on prevention of diabetes is necessary for obesity, central obesity, hypertensive and in urban areas for middle-aged and older in China.

Because of the slow dynamic behavior of the large-inertia wind turbine rotor, variable-speed wind turbines (VSWTs) are actually unable to keep operating at the design tip speed ratio (TSR) during the maximum power point tracking (MPPT) process. Moreover, it has been pointed out that although a larger design TSR can increase the maximum power coefficient, it also greatly prolongs the MPPT process of VSWTs. Consequently, turbines spend more time operating at the off-design TSRs and the wind energy capture efficiency is decreased. Therefore, in the inverse aerodynamic design of VSWTs, the static aerodynamic performance (i.e., the maximum power coefficient) and the dynamic process of MPPT should be comprehensively modeled for determining an appropriate design TSR. In this paper, based on the inverse design method, an aerodynamic optimization method for VSWTs, fully considering the impacts of the design TSR on the static and dynamic behavior of wind turbines is proposed. In this method, to achieve higher wind energy production, the design TSR, chord length and twist angle are jointly optimized, which is structurally different from the conventional separated design procedure. Finally, the effectiveness of the proposed method is validated by simulation results based on the Bladed software.

Due to the dynamic process of maximum power point tracking (MPPT) caused by turbulence and large rotor inertia, variable-speed wind turbines (VSWTs) cannot maintain the optimal tip speed ratio (TSR) from cut-in wind speed up to the rated speed. Therefore, in order to increase the total captured wind energy, the existing aerodynamic design for VSWT blades, which only focuses on performance improvement at a single TSR, needs to be improved to a multi-point design. In this paper, based on a closed-loop system of VSWTs, including turbulent wind, rotor, drive train and MPPT controller, the distribution of operational TSR and its description based on inflow wind energy are investigated. Moreover, a multi-point method considering the MPPT dynamic process for the aerodynamic optimization of VSWT blades is proposed. In the proposed method, the distribution of operational TSR is obtained through a dynamic simulation of the closed-loop system under a specific turbulent wind, and accordingly the multiple design TSRs and the corresponding weighting coefficients in the objective function are determined. Finally, using the blade of a National Renewable Energy Laboratory (NREL) 1.5 MW wind turbine as the baseline, the proposed method is compared with the conventional single-point optimization method using the commercial software Bladed. Simulation results verify the effectiveness of the proposed method.

Introduction: Phylogenetic relatedness is one of the important factors in the community assembly process. Here, we aimed to understand the large-scale phylogenetic relationship between alien plant species at different stages of the invasion process and how these relationships change in response to the environmental filtering process at multiple spatial scales and different phylogenetic extents. Methods: We identified the alien species in three invasion stages, namely invasive, naturalized, and introduced, in China. The occurrence records of the species were used to quantify two abundance-based phylogenetic metrics [the net relatedness index (NRI) and the nearest taxon index (NTI)] from a highly resolved phylogenetic tree. The metrics were compared between the three categories of alien species. Generalized linear models were used to test the effect of climate on the phylogenetic pattern. All analyses were conducted at four spatial scales and for three major angiosperm families. Results: We observed significantly higher NRI and NTI values at finer spatial scales, indicating the formation of more clustered assemblages of phylogenetically closely related species in response to the environmental filtering process. Positive NTI values for the invasive and naturalized aliens suggested that the presence of a close relative in the community may help the successful naturalization and invasion of the introduced alien species. In the twodimensional phylogenetic space, the invasive species communities significantly differed from the naturalized and introduced species, indicating that established alien species need to be phylogenetically different to become invasive. Positive phylogenetic measures for the invasive aliens across the spatial scales suggested that the presence of invasive aliens could facilitate the establishment of other invasive species. Phylogenetic relatedness was more influenced by temperature than precipitation, especially at a finer spatial scale. With decreased temperature, the invasive species showed a more clustered assemblage, indicating conservatism of their phylogenetic niche. The phylogenetic pattern was different at the family level, although there was a consistent tendency across families to form more clustered assemblages. Discussion: Overall, our study showed that the community assemblage became more clustered with the progression of the invasion process. The phylogenetic measures varied at spatial and taxonomic scales, thereby highlighting the importance of assessing phylogenetic patterns at different gradients of the community assembly process. [ABSTRACT FROM AUTHOR]