ADHD and family life: A cross-sectional study of ADHD prevalence among pupils in China and factors associated with parental depression

Background: Attention Deficit Hyperactivity Disorder (ADHD) is increasingly recognized as a major problem for children and their families in China. However, its influence on parental mental health has been seldom explored. Objective: To examine the prevalence of attention deficit hyperactivity disorder in a community sample of children aged 6–13 years, and the extent to which it impacts parental mental health. Method: Cross-sectional study of primary school pupils (number = 2497) in Deyang, Sichuan Province, South-West China. We used standardized instruments to identify children with ADHD symptoms and parent depression. Results: The prevalence of ADHD was 9.8%. Factors associated with the likelihood of ADHD, included family environment(P = 0.003), time spent with children(P = 0.01), parenting style(P = 0.01), and parental relationship, pupils self-harm and lower academic ability (P = 0.001). After controlling for other factors, having a child with ADHD increased the likelihood of parents’ depression (OR = 4.35, CI = 2.68~7.07), additional factors included parent relationship. Conclusions: ADHD may be a common disorder among Chinese children, the symptoms of which may increase the likelihood of parent depression. There is a need for greater detection of ADHD in schools, acknowledgement of the challenges the disorder creates for academic success and family wellbeing, and psychoeducational tools for supporting parents of children with ADHD

Behavior-Based Fuzzy Control for Mobile Robot Navigation

A new behavior-based fuzzy control method for mobile robot navigation is presented. It is based on behavioral architecture which can deal with uncertainties in unknown environments and has the ability to accommodate different behaviors. Basic behaviors are controlled by specific fuzzy logic controllers, respectively. The proposed approach qualifies for driving a robot to reach a target while avoiding obstacles in the environment. Simulation and experiments are performed to verify the correctness and feasibility of the proposed method

Oxytetracycline, copper, and zinc effects on nitrification processes and microbial activity in two soil types

The distribution, fate, and effects of antibiotics and heavy metal residues in agricultural soil caused by long‐term application of organic fertilizers are of increasing concern. However, the ecotoxic effects of the interaction between antibiotics and heavy metals vary with the physicochemical properties of the soil, and it is still unclear how these substances interact with soil microbial functions. A short‐term microcosm experiment was conducted to investigate effects of the typical antibiotic oxytetracycline (OTC) with heavy metals (zinc [Zn] and copper [Cu]) alone or in combination on nitrification process and soil microbial activity in two different types of soil (FQ: sandy loam soil and NB: clay loamy soil). Results indicated that soil types influenced the toxic effects of antibiotics and heavy metals. Zn and Cu alone and when combined with OTC inhibited and retarded nitrification processes and reduced nitrous oxide emissions, which were mainly attributed to the inhibitory effects on ammonia‐oxidizing microorganisms. Moreover, Zn and Cu alone or combined with OTC increased soil respiration, but decreased the abundances of bacteria and fungi. In contrast, OTC alone had no significant effect on soil respiration but increased the abundance of fungi in both soils. Together, our results suggest that the widespread occurrence of antibiotics and heavy metals in agriculture soils may pose significant eco‐environmental risks by altering nitrification process and soil microbial activity

Reconciling Life Cycle Environmental Impacts with Ecosystem Services: A Management Perspective on Agricultural Land Use

Impacts on ecosystem services that are related to agricultural land use greatly differ depending on management practices employed. This study aimed to reveal issues associated with evaluating ecosystem services related to land use at the management level during life cycle assessment (LCA) and to consider future challenges. Firstly, a relationship between agricultural ecosystem services and management practices was outlined. Then, a survey was performed to disclose the current status of assessment of impact of land use in agricultural LCA case studies that compared between different management practices. In addition, this study also investigated how management practices have been differently considered by factors that characterize ecosystem services that are related to land use. The results show that the number of agricultural LCA cases where land use impacts instead of land areas were assessed was still small. The results of limited LCA case studies, which using factors could differentiate between various management practices, suggest that although organic farming methods have been employed over large land areas, lower impact may be caused by agricultural land use. For factors developed in existing research, services related to soil quality, and some of the regulatory services were considered, those unique to agriculture were missing. Although most of factors were calculated at levels of intensity or land use type, some of them were based on a process-based model that could consider management practices. In the future, factors that characterize the impacts of land use on ecosystem services, such as carbon storage and erosion prevention, will need to be calculated at the management level. For ecosystem services, such as habitat conservation and pollination, further efforts in accumulating evaluation case studies that collect and accumulate foreground data are important

FPGA-Based BNN Architecture in Time Domain with Low Storage and Power Consumption

With the increasing demand for convolutional neural networks (CNNs) in many edge computing scenarios and resource-limited settings, researchers have made efforts to apply lightweight neural networks on hardware platforms. While binarized neural networks (BNNs) perform excellently in such tasks, many implementations still face challenges such as an imbalance between accuracy and computational complexity, as well as the requirement for low power and storage consumption. This paper first proposes a novel binary convolution structure based on the time domain to reduce resource and power consumption for the convolution process. Furthermore, through the joint design of binary convolution, batch normalization, and activation function in the time domain, we propose a full-BNN model and hardware architecture (Model I), which keeps the values of all intermediate results as binary (1 bit) to reduce storage requirements by 75%. At the same time, we propose a mixed-precision BNN structure (model II) based on the sensitivity of different layers of the network to the calculation accuracy; that is, the layer sensitive to the classification result uses fixed-point data, and the other layers use binary data in the time domain. This can achieve a balance between accuracy and computing resources. Lastly, we take the MNIST dataset as an example to test the above two models on the field-programmable gate array (FPGA) platform. The results show that the two models can be used as neural network acceleration units with low storage requirements and low power consumption for classification tasks under the condition that the accuracy decline is small. The joint design method in the time domain may further inspire other computing architectures. In addition, the design of Model II has certain reference significance for the design of more complex classification tasks

FPGA-Based BNN Architecture in Time Domain with Low Storage and Power Consumption

With the increasing demand for convolutional neural networks (CNNs) in many edge computing scenarios and resource-limited settings, researchers have made efforts to apply lightweight neural networks on hardware platforms. While binarized neural networks (BNNs) perform excellently in such tasks, many implementations still face challenges such as an imbalance between accuracy and computational complexity, as well as the requirement for low power and storage consumption. This paper first proposes a novel binary convolution structure based on the time domain to reduce resource and power consumption for the convolution process. Furthermore, through the joint design of binary convolution, batch normalization, and activation function in the time domain, we propose a full-BNN model and hardware architecture (Model I), which keeps the values of all intermediate results as binary (1 bit) to reduce storage requirements by 75%. At the same time, we propose a mixed-precision BNN structure (model II) based on the sensitivity of different layers of the network to the calculation accuracy; that is, the layer sensitive to the classification result uses fixed-point data, and the other layers use binary data in the time domain. This can achieve a balance between accuracy and computing resources. Lastly, we take the MNIST dataset as an example to test the above two models on the field-programmable gate array (FPGA) platform. The results show that the two models can be used as neural network acceleration units with low storage requirements and low power consumption for classification tasks under the condition that the accuracy decline is small. The joint design method in the time domain may further inspire other computing architectures. In addition, the design of Model II has certain reference significance for the design of more complex classification tasks

A Dual-Band, Dual-Polarized Filtering Antenna Based on Cross-Shaped Dielectric Strip Resonator

A dual-band, dual-polarized filtering antenna with a cross-shaped dielectric strip resonator is proposed. The dual-band filtering radiation function is achieved by utilizing the odd and even modes of the stub loaded microstrip resonator to excite the TMδ1 and TMδ3 mode in each polarization direction of the cross-shaped dielectric strip resonator. The cross-shaped dielectric strip resonator is synthesized by the E-field distributions and the magnitude comparison along different polarization directions, which can ensure the isolation between two polarizations. Compared with dual-band filtering dielectric antennas, the proposed antenna has the characteristic of dual-polarized radiation, as well as a low profile. A prototype is fabricated and measured, which operates at 3.5 GHz and 4.9 GHz with the fractional bandwidths (FBW) of 5.40% and 2.03%, respectively, and the gains of these two bands are 6.4 dBi and 6.2 dBi, respectively. The two radiation nulls are located at 4.4 GHz and 5.1 GHz. Furthermore, the measured isolation between the two ports in the frequency band can achieve 16 dB