Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized

Total contact casts versus removable offloading interventions for the treatment of diabetic foot ulcers: a systematic review and meta-analysis

ObjectiveThis study aimed to evaluate the effectiveness of total contact casts (TCCs) versus removable offloading interventions among patients with diabetic foot ulcers (DFUs).MethodsA comprehensive search was done in databases Embase, Cochrane Library, and, PubMed. The references of retrieved articles were reviewed, up until February 2023. Controlled trials comparing the effects of TCCs with removable offloading interventions (removable walking casts and footwear) in patients with DFUs were eligible for review.ResultsTwelve studies were included in the meta-analysis, involving 591 patients with DFUs. Among them, 269 patients were in the intervention group (TCC), and 322 in the control group (removable walking casts/footwear). The analysis revealed that the TCC group had higher healing rates (Risk Ratio(RR)=1.22; 95% confidence interval(CI):1.11 to 1.34, p<0.001), shorter healing time (Standard Mean Difference(SMD)=-0.57; 95%CI: -1.01 to -0.13, P=0.010), and elevated occurrence of device-related complications (RR=1.70; 95%CI:1.01 to 2.88, P=0.047), compared with the control group. Subgroup analysis illustrated patients using TCCs had higher healing rates than those using removable walking casts (RR=1.20; 95%CI:1.08 to 1.34, p=0.001) and footwear (RR=1.25; 95%CI:1.04 to 1.51, p=0.019), but they required comparable time for ulcer healing compared with those using removable walking casts (SMD=-0.60; 95%CI: -1.22 to 0.02, P=0.058) or footwear group (SMD=-0.52; 95%CI: -1.17 to 0.12, P=0.110). Although patients using TCCs had significantly higher incidence of device-related complications than those using footwear (RR=4.81; 95%CI:1.30 to 17.74, p=0.018), they had similar one compared with those using the removable walking casts (RR=1.27; 95%CI:0.70 to 2.29, p=0.438).ConclusionThe use of TCCs in patients with DFUs resulted in improved rates of ulcer healing and shorter healing time compared to removable walking casts and footwear. However, it is important to note that TCCs were found to be associated with increased prevalence of complications

Deep Segmentation of OCTA for Evaluation and Association of Changes of Retinal Microvasculature with Alzheimer’s Disease and Mild Cognitive Impairment

BackgroundOptical coherence tomography angiography (OCTA) enables fast and non-invasive high-resolution imaging of retinal microvasculature and is suggested as a potential tool in the early detection of retinal microvascular changes in Alzheimer's Disease (AD). We developed a standardised OCTA analysis framework and compared their extracted parameters among controls and AD/mild cognitive impairment (MCI) in a cross-section study.MethodsWe defined and extracted geometrical parameters of retinal microvasculature at different retinal layers and in the foveal avascular zone (FAZ) from segmented OCTA images obtained using well-validated state-of-the-art deep learning models. We studied these parameters in 158 subjects (62 healthy control, 55 AD and 41 MCI) using logistic regression to determine their potential in predicting the status of our subjects.ResultsIn the AD group, there was a significant decrease in vessel area and length densities in the inner vascular complexes (IVC) compared with controls. The number of vascular bifurcations in AD is also significantly lower than that of healthy people. The MCI group demonstrated a decrease in vascular area, length densities, vascular fractal dimension and the number of bifurcations in both the superficial vascular complexes (SVC) and the IVC compared with controls. A larger vascular tortuosity in the IVC, and a larger roundness of FAZ in the SVC, can also be observed in MCI compared with controls.ConclusionOur study demonstrates the applicability of OCTA for the diagnosis of AD and MCI, and provides a standard tool for future clinical service and research. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of AD and MCI

Study of Promoter Methylation Patterns of HOXA2, HOXA5, and HOXA6 and Its Clinicopathological Characteristics in Colorectal Cancer

Research on DNA methylation offers great potential for the identification of biomarkers that can be applied for accurately assessing an individual's risk for cancer. In this article, we try to find the ideal epigenetic genes involved in colorectal cancer (CRC) based on a CRC database and our CRC cohort. The top 20 genes with an extremely high frequency of hypermethylation in CRC were identified in the latest database. Remarkably, 3 HOXA genes were included in this list and ranked at the top. The percentage of methylation in the HOXA5, HOXA2, and HOXA6 genes in CRC were up to 67.62, 58.36, and 31.32%, respectively, and ranked first in CRC among all human tumor tissues. Paired colorectal tumor samples and adjacent non-tumor colorectal tissue samples and four CRC cell lines were selected for MethylTarget™ assays. The results demonstrated that CRC tissues and cells had a stronger methylation status around the 3 HOXA gene promoter regions compared with adjacent non-tumor colonic tissue samples. The Receiver operator characteristic curve (ROC) curves for HOXA genes show excellent diagnostic ability in distinguishing tissue from healthy individuals and CRC patients, especially for Stage I patients (AUC = 0.9979 in HOXA2, 0.9309 in HOXA5, and 0.8025 in HOXA6). An association analysis between the methylation pattern of HOXA genes and clinical indicators was performed and found that HOXA2 methylation was significantly associated with age, N, stage, M, lymphovascular invasion, perineural invasion, lymph node number. HOXA5 methylation was associated with age, T, M, stage, and tumor status, and HOXA6 methylation was associated with age and KRAS mutation. Notably, we found that the highest methylation of HOXA5 and HOXA2 occurs in the early stages of colorectal cancer tissues such as stage I, N0, MO, and non-invasive tissues. The methylation levels declined as tumors progressed. However, methylation level at any stage of the tumor was still significantly higher than in normal tissues (p < 0.0001). The mRNA of the 3 HOXA genes was downregulated in early tumor stages due to hypermethylation of CpG islands adjacent to the promoters of the genes. In addition, hypermethylation of HOXA5 and HOXA6 mainly occurred in patients < 60 years old and with MSI-L, MSS, CIMP.L and non-CIMP tumors. Together, this suggests that epigenetic silencing of 3 adjacent HOXA genes may be an important event in the progression of colorectal cancer

Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection

PurposeFast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT).MethodsUnlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF’s potential as a biomarker of DME.ResultsResults unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea.ConclusionOur study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications

Recent Advances in Synthetic Bioelastomers

This article reviews the degradability of chemically synthesized bioelastomers, mainly designed for soft tissue repair. These bioelastomers involve biodegradable polyurethanes, polyphosphazenes, linear and crosslinked poly(ether/ester)s, poly(ε-caprolactone) copolymers, poly(1,3-trimethylene carbonate) and their copolymers, poly(polyol sebacate)s, poly(diol-citrates) and poly(ester amide)s. The in vitro and in vivo degradation mechanisms and impact factors influencing degradation behaviors are discussed. In addition, the molecular designs, synthesis methods, structure properties, mechanical properties, biocompatibility and potential applications of these bioelastomers were also presented

A Novel Defect Inspection System Using Convolutional Neural Network for MEMS Pressure Sensors

Defect inspection using imaging-processing techniques, which detects and classifies manufacturing defects, plays a significant role in the quality control of microelectromechanical systems (MEMS) sensors in the semiconductor industry. However, high-precision classification and location are still challenging because the defect images that can be obtained are small and the scale of the different defects on the picture of the defect is different. Therefore, a simple, flexible, and efficient convolutional neural network (CNN) called accurate-detection CNN (ADCNN) to inspect MEMS pressure-sensor-chip packaging is proposed in this paper. The ADCNN is based on the faster region-based CNN, which improved the performance of the network by adding random-data augmentation and defect classifiers. Specifically, the ADCNN achieved a mean average precision of 92.39% and the defect classifier achieved a mean accuracy of 97.2%

New Second-Order Finite Difference Scheme for the Problem of Contaminant in Groundwater Flow

We develop a new efficient second-order finite difference scheme for two-dimensional problem of contaminant in groundwater flow. Theoretical analysis shows that the scheme is second-order convergence in the L2 norm and is unconditionally stable. Numerical results demonstrate that the error measures of the second-order scheme are several times smaller than those of the standard implicit finite difference scheme