Descriptions and Barcoding of Five New Chinese Deuterophlebia Species Revealing This Genus in Both Holarctic and Oriental Realms (Diptera: Deuterophlebiidae)

The monotypic family Deuterophlebiidae of China was recorded twice previously from far northwest upon adults, the most parts of this country have not been investigated, leaving a huge blank of knowledge on their morphology, diversity, biology, or distribution. After deliberated collecting and rearing in recent years, we obtained more than one thousand specimens of Deuterophlebiidae, they are classified into five new species herein: Deuterophlebia sinensis sp. nov., D. yunnanensis sp. nov., D. wuyiensis sp. nov., D. acutirhina sp. nov. and D. alata sp. nov. Detailed descriptions and photographs of gathered life stages are given for these new species. Adults of them can be identified by chaetotaxy and length ratio of flagellomeres and legs, microtrichia on postgena and shape of their clypeus, pupae can be recognized by thoracic spines and abdominal chitin bands, and larvae can be separated by setae on thorax and abdomen. Genetic distances between species are 0.086–0.175 based on their COI genes. This contribution represents the first database of the enigmatic Deuterophlebiidae from China and shows a new distribution pattern of Deuterophlebia. In addition, the discovery throws some light on the origin and biogeography of the genus and family

An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China

On the importance of wind turbine wake boundary to wind energy and environmental impact

The increase in global wind power installations has also increased the wind turbine density in wind farms. This has made the wake interactions between neighbouring turbines more significant and difficult to describe. Understanding wakes is important to predict the energy production and assess their environmental impacts. Although existing wake description methods can predict the average wind turbine wake under a high wind turbine density, they are unable to identify the wake boundary with an acceptable accuracy and computational cost. Consequently, the role of wake boundaries in modern wind farms has become unclear. To deal with this problem, this paper presents a comprehensive discussion on wind turbine wakes, especially the role of boundary identification in the wind farm planning stage. After a review of existing methods, an approach based on a newly derived mathematical formulation of the velocity field is proposed. Lidar-based field measurements and large-eddy simulations along with actuator line model-based numerical simulations were used to compare different wake boundary identification methods. The results show that the new approach is computationally cost-effective with a 5% increase in accuracy. The new approach also offers significant advantages as wake boundaries become increasingly complex

Association of motor index scores with fall incidence among community-dwelling older people

Abstract Background Several kinds of motor dysfunction have been studied for predicting future fall risk in community-dwelling older individuals. However, no study has tested the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of falling, as well as the specific fall type. Objective We investigated the associations of FINEA/GROSSA scores with fall risk, explained falls, and unexplained falls. Methods A total of 6267 community-dwelling adults aged ≥ 50 years from the Irish Longitudinal Study on Aging (TILDA) cohort were included. First, the associations of FINEA and GROSSA scores with the history of total falls, explained falls and unexplained falls were assessed in a cross-sectional study and further verified in a prospective cohort after 2 years of follow-up by Poisson regression analysis. Results We found that high FINEA and GROSSA scores were positively associated with almost all fall histories (FINEA scores: total falls: adjusted prevalence ratio [aPR] = 1.28, P = 0.009; explained falls: aPR = 1.15, P = 0.231; unexplained falls: aPR = 1.88, P < 0.001; GROSSA scores: total falls: aPR = 1.39, P < 0.001; explained falls: aPR = 1.28, P = 0.012; unexplained falls: aPR = 2.18, P < 0.001) in a cross-sectional study. After 2 years of follow-up, high FINEA scores were associated with an increased incidence of total falls (adjusted rate ratio [aRR] = 1.42, P = 0.016) and explained falls (aRR = 1.51, P = 0.020) but not with unexplained falls (aRR = 1.41, P = 0.209). High GROSSA scores were associated with an increased incidence of unexplained falls (aRR = 1.57, P = 0.041) and were not associated with either total falls (aRR = 1.21, P = 0.129) or explained falls (aRR = 1.07, P = 0.656). Compared with individuals without limitations in either the FINEA or GROSSA, individuals with limitations in both indices had a higher risk of falls, including total falls (aRR = 1.35, P = 0.002), explained falls (aRR = 1.31, P = 0.033) and unexplained falls (aRR = 1.62, P = 0.004). Conclusion FINEA scores were positively associated with accidental falls, while GROSSA scores were positively associated with unexplained falls. The group for whom both measures were impaired showed a significantly higher risk of both explained and unexplained falls. FINEA or GROSSA scores should be investigated further as possible tools to screen for and identify community-dwelling adults at high risk of falling