Search

The management of global food security is one of the major issues of concern to the international community today. Ensuring the stability of food sources and preventing crop pests and diseases are crucial in maintaining social stability and promoting economic development. In modern agriculture, computer vision has emerged as a tool to aid in pest and disease prevention. For instance, when calculating the overall fruit yield of fruit trees and identifying and categorising pests and diseases, traditional neural networks tend to preserve duplicate data during image prediction. Traditional neural networks store unnecessary information when predicting images, leading to more classification calculations and thus higher computing costs. By utilising the concept of deep compressed perception, classification, and other operations can be carried out on compressed data. In this paper, combining compressed sensing theory and traditional neural network techniques, a novel deep compressed sensing network model called CSLSNet is proposed. The model utilizes a parallel convolution and residual structure comprising of convolution, the LR module, and the LSR module. The model directly categorizes images in the compressed domain, leading to decreased computation and a reduction in the number of model parameters. By comparing experiments using different SR (sampling rates) and traditional image compression methods alongside existing network models, this paper demonstrates that our model achieves higher classification accuracy under the same experimental conditions. Also, in fewer training cycles, the loss trend of the proposed model rapidly converges, and the loss curve becomes smoother. The results show that, at a sampling rate of 0.5, our model, CSLSNet, achieves an accuracy of 90.08%. In contrast, other networks involved in the comparison, such as CSBNet and AlexNet, achieve only 84.81% and 86.5%, respectively.

Developmental coordination disorder (DCD) is a developmental disorder characterized by impaired motor coordination, often co-occurring with attention deficit disorder, autism spectrum disorders, and other psychological and behavioural conditions. The aetiology of DCD is believed to involve brain changes and environmental factors, with genetics also playing a role in its pathogenesis. Recent research has identified several candidate genes and genetic factors associated with motor impairment, including deletions, copy number variations, single nucleotide polymorphisms, and epigenetic modifications. This review provides an overview of the current knowledge in genetic research on DCD, highlighting the importance of continued research into the underlying genetic mechanisms. While evidence suggests a genetic contribution to DCD, the evidence is still in its early stages, and much of the current evidence is based on studies of co-occurring conditions. Further research to better understand the genetic basis of DCD could have important implications for diagnosis, treatment, and our understanding of the condition’s aetiology.

In practice, the outflow from a diffuser is highly non-uniform due to many reasons. However, the air outflow velocity from a diffuser is uniform in most current studies. Little research has been conducted to determine under what conditions uniform velocity can be used. Therefore, based on the non-uniformity of velocity, airflow characteristics of grille and ceiling diffusers were investigated experimentally and numerically. Two generic CFD cases (non-uniform and uniform velocity) are presented. The velocity field near the diffuser is investigated with measurements in order to determine velocity-inlet boundary conditions. The study shows that the uniform velocity-inlet boundary condition can be considered accurate only under certain conditions. For the grille diffuser, the aspect ratio affects the distribution of the outflow velocity. Using uniform velocity as the velocity-inlet boundary condition for the grille diffuser would result in at least a 14.2% error in the jet region, except when the outflow aspect ratio is 1 and the average velocity is greater than 1.83 m/s. However, when the average velocity of the ceiling diffuser is 3.64 m/s, the error of using uniform velocity as the velocity-inlet boundary condition reaches 58.3%. This study provides the basis for determining the velocity-inlet boundary conditions during numerical simulations.

Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately. While traditional neural networks require complete processing of data when processing data, by compressed sensing, only one part of the data needs to be processed, which greatly reduces the amount of data processed by the network. In this paper, a combination of compressed perception and neural networks is used to classify and identify pest images in the compressed domain. A network model for compressed sampling and classification, CSBNet, is proposed to enable compression in neural networks instead of the sensing matrix in conventional compressed sensing (CS). Unlike traditional compressed perception, no reduction is performed to reconstruct the image, but recognition is performed directly in the compressed region, while an attention mechanism is added to enhance feature strength. The experiments in this paper were conducted on different datasets with various sampling rates separately, and our model was substantially less accurate than the other models in terms of trainable parameters, reaching a maximum accuracy of 96.32%, which is higher than the 93.01%, 83.58%, and 87.75% of the other models at a sampling rate of 0.7.

In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the data burden and energy costs. Therefore, compressed sensing (CS) which can conduct under-sampling and reconstruction at the same time is adopted in the ECG monitoring application. Recently, deep learning used in CS methods improves the reconstruction performance significantly and can removes of some of the constraints in traditional CS. In this paper, we propose a deep compressive-sensing scheme for ECG signals, based on modified-Inception block and long short-term memory (LSTM). The framework is comprised of four modules: preprocessing; compression; initial; and final reconstruction. We adaptively compressed the normalized ECG signals, sequentially using three convolutional layers, and reconstructed the signals with a modified Inception block and LSTM. We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the highest at all of the sensing rates in our experiments on both of the databases, and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing significant improvement in reconstruction performance compared to the comparative methods. Our method also showed good recovering quality in the noisy data.

Parents and teachers have knowledge of children’s daily motor performance yet may make different judgments about the levels of competence observed at home and school. The current study aimed to examine the discrepancies between parent and teacher reports using the Movement ABC-2 Checklist and the Developmental Coordination Disorder Questionnaire (DCDQ) on children with and without suspected Developmental Coordination Disorder (DCD). The Movement ABC-2 Test was administered to 1276 children aged 5–10 years in China. The Movement ABC-2 Checklist and DCDQ were completed by both parents and teachers of all children. A total of 172 children achieving a score below the 15th percentile on the Movement ABC-2 Test were identified as children with suspected DCD. Both parents and teachers showed suitable agreement in judging children’s motor competence but low sensitivity in identifying children with DCD. Parent scores of children’s motor competence were more closely associated with test performance scores compared to teacher scores. Teachers tended to over-rate children’s motor competence. The motor difficulties identified by parents were associated with low Movement ABC-2 Test scores on Manual Dexterity and Balance components, while motor difficulties identified by teachers were associated with the Balance component only. The results demonstrated discrepancies between parent and teacher reports, suggesting the importance of using a range of measures to identify and describe motor difficulties in children.

Traditional pest detection methods are challenging to use in complex forestry environments due to their low accuracy and speed. To address this issue, this paper proposes the YOLOv4_MF model. The YOLOv4_MF model utilizes MobileNetv2 as the feature extraction block and replaces the traditional convolution with depth-wise separated convolution to reduce the model parameters. In addition, the coordinate attention mechanism was embedded in MobileNetv2 to enhance feature information. A symmetric structure consisting of a three-layer spatial pyramid pool is presented, and an improved feature fusion structure was designed to fuse the target information. For the loss function, focal loss was used instead of cross-entropy loss to enhance the network’s learning of small targets. The experimental results showed that the YOLOv4_MF model has 4.24% higher mAP, 4.37% higher precision, and 6.68% higher recall than the YOLOv4 model. The size of the proposed model was reduced to 1/6 of that of YOLOv4. Moreover, the proposed algorithm achieved 38.62% mAP with respect to some state-of-the-art algorithms on the COCO dataset.

The association between religion and health is well debated and receives continuous attention in research. Selection bias is often a major concern among the observatory data routinely used worldwide to examine this topic. Adopting the propensity score matching (PSM) method, the present study tries to assess the treatment effects of religion on self-reported health status. The final sample from the 2007 Spiritual Life Study of Chinese Residents (SLSC) contains 6194 valid responses. The average treatment effects (ATEs) estimated by the PSM method show that respondents with religious affiliations are on average significantly more likely to report being very healthy by 5.2 percentage points (by 3.6 and 9.6 percentage points among Buddhists and Protestants), especially, by 16.2 percentage points among those regarding religion as being very important in their lives. Meanwhile, ATEs of religion on reporting being very happy is 17.0 among Protestants and 13.4 among those regarding religion with high importance and 11.3 among those with “regular religious attendance”.

Most previous studies performed in Western social contexts have revealed that religion can influence an individual’s sense of happiness. Few studies have sought to clarify the influence of religion in a Chinese social context, however, and there has been no study specifically about the potential differences in the dichotomous social environments of urban and rural areas in China. Via the nationwide survey data of the 2007 Spiritual Life Study of Chinese Residents (SLSC), this study examines the association between religion and happiness among urban and rural residents of China. The results reveal that there is a generally positive association between religion and happiness among those with religious affiliations in China. Regardless of affiliation with Buddhism or Protestantism, there is a strong positive association among rural respondents, an insignificant association among urban respondents, and mixed results among town residents.

The development of a sacrificial bond provided unique inspiration for the design of advanced elastomers with excellent mechanical properties, but it is still a huge challenge to construct a homogenous polar sacrificial network in a nonpolar elastomer. In this effort, we proposed a novel strategy to engineer a multi-ionic network into a covalently cross-linked 1,2-polybutadiene (1,2-PB) facilitated by in-situ intercalated organic montmorillonite (OMMT) without phase separation. XRD, SEM, and TEM analysis were carried out to characterize the microstructure of the resulting polymers. Crosslinking density, dielectric performance, and cyclic tensile tests were used to demonstrate the interaction of zinc methacrylate (ZDMA) and OMMT. The dynamic nature of ionic bonds allowed it to rupture and reform to dissipate energy efficiently. Stretching orientation brought parallelism between polymer chains and OMMT layers which was beneficial for the reconstruction of the ionic network, ultimately resulting in high strength and a low stress relaxation rate. Overall, our work presented the design of a uniform and strong sacrificial network in the nano-clay/elastomer nanocomposite with outstanding mechanical performances under both static and dynamic conditions.

In this study, a butadiene-isoprene coordination polymerization was initiated by a binary molybdenum (Mo)-based catalytic system consisting of modified MoO2Cl2 as the primary catalyst, triethyl aluminum substituted by m-cresol as the co-catalyst and tris(nonyl phenyl) phosphate (TNPP) as the ligand. The effects of the amount of catalyst and type of co-catalyst were investigated in detail. Experimental results indicated that when the butadiene-isoprene coordination polymerization was initiated by the binary Mo-based catalytic system, the monomer conversion could reach 90%. The resulting butadiene units were primarily based on 1,2-structures, and the reactivity ratios of butadiene and isoprene were 1.13 and 0.31, respectively. The reaction in the catalytic system was attributed to the non-ideal and non-constant ratio copolymerization. When the addition of isoprene monomers was relatively low, the isoprene units on the butadiene-isoprene copolymers were primarily based on the 1,2- and 3,4-structures. Moreover, the orientation of active centers to 1,2- and 3,4-structures gradually decreased with an increase in the addition of isoprene monomers, which resulted in the generation of high vinyl butadiene-isoprene copolymers.

Uterine corpus endometrial carcinoma (UCEC) poses significant clinical challenges due to its high incidence and poor prognosis, exacerbated by the lack of effective screening methods. The standard treatment for UCEC typically involves surgical intervention, with radiation and chemotherapy as potential adjuvant therapies. In recent years, immunotherapy has emerged as a promising avenue for the advanced treatment of UCEC. This study employs a multi-omics approach, analyzing RNA-sequencing data and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), and GeneMANIA databases to investigate the prognostic value of MutL Homolog 1 (MLH1) gene expression in UCEC. The dysregulation of MLH1 in UCEC is linked to adverse prognostic outcomes and suppressed immune cell infiltration. Gene Set Enrichment Analysis (GSEA) data reveal MLH1’s involvement in immune-related processes, while its expression correlates with tumor mutational burden (TMB) and microsatellite instability (MSI). Lower MLH1 expression is associated with poorer prognosis, reduced responsiveness to Programmed cell death protein 1 (PD-1)/Programmed death-ligand 1 (PD-L1) inhibitors, and heightened sensitivity to anti-cancer agents. This comprehensive analysis establishes MLH1 as a potential biomarker for predicting prognosis, immunotherapy response, and drug sensitivity in UCEC, offering crucial insights for the clinical management of patients.