Search

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency of manual operation, this paper designs a clip stem automatic transplanting and seedling picking device based on the yolov5 algorithm. First of all, through the study of the characteristics of tomato seedlings of different seedling ages, the age of tomato seedlings suitable for transplanting was obtained. Secondly, the improved yolov5 algorithm was used to determine the position and shape of tomato seedlings. By adding a lightweight upsampling operator (CARAFE) and an improved loss function, the feature extraction ability and detection speed of tomato seedling stems were improved. The accuracy of the improved yolov5 algorithm reached 92.6%, and mAP_0.5 reached 95.4%. Finally, the seedling verification test was carried out with tomato seedlings of about 40 days old. The test results show that the damage rate of the device is 7.2%, and the success rate is not less than 90.3%. This study can provide a reference for research into automatic transplanting machines.

Magnetic resonance imaging (MRI) diagnosis, enhanced by deep learning methods, plays a crucial role in medical image processing, facilitating precise clinical diagnosis and optimal treatment planning. Current methodologies predominantly focus on feature extraction from the image domain, which often results in the loss of global features during down-sampling processes. However, the unique global representational capacity of MRI K-space is often overlooked. In this paper, we present a novel MRI K-space-based global feature extraction and dual-path attention fusion network. Our proposed method extracts global features from MRI K-space data and fuses them with local features from the image domain using a dual-path attention mechanism, thereby achieving accurate MRI segmentation for diagnosis. Specifically, our method consists of four main components: an image-domain feature extraction module, a K-space domain feature extraction module, a dual-path attention feature fusion module, and a decoder. We conducted ablation studies and comprehensive comparisons on the Brain Tumor Segmentation (BraTS) MRI dataset to validate the effectiveness of each module. The results demonstrate that our method exhibits superior performance in segmentation diagnostics, outperforming state-of-the-art methods with improvements of up to 63.82% in the HD95 distance evaluation metric. Furthermore, we performed generalization testing and complexity analysis on the Automated Cardiac Diagnosis Challenge (ACDC) MRI cardiac segmentation dataset. The findings indicate robust performance across different datasets, highlighting strong generalizability and favorable algorithmic complexity. Collectively, these results suggest that our proposed method holds significant potential for practical clinical applications.

This research initiative, developed in response to the need for enhanced mechanization efficiency within solar greenhouses, particularly under yellow sand cultivation conditions, introduces an integrated ditching and film-covering machine. A novel spiral staggered throw-cut combined ditching knife was specifically engineered and optimized to meet the exacting agronomic requirements of embedded substrate cultivation. Extensive analyses of soil interactions and the formulation of dynamic equations for soil particles facilitated the determination of key operational parameters: a tangent height of 650 mm for the ditching knife, a soil-throwing width of 300 mm, a piece width of 120 mm, and an inclination angle of 30°. Performance simulations of the ditching knife, conducted using the discrete element method (DEM), revealed superior soil disturbance control and improved soil return compared to conventional designs. Critical operational variables such as forward speed, knife shaft speed, and ditching depth were rigorously tested, with trench depth quality and power consumption as primary evaluation metrics. The results demonstrated that knife shaft speed profoundly influences performance, with optimal operating parameters established through detailed field testing: a speed of 0.5 m/s, a blade shaft speed of 200 rpm, and a ditching depth of 300 mm. Under these optimized conditions, the machine achieved power consumption of 0.668 kW, trench depth stability of 86.7%, a surface width of 413 mm, a bottom width of 304 mm, and an average ditching depth of 310 mm, achieving a qualification rate of 87.1%. The post-ditching soil crushing rate was 92.4%. Both simulation and field evaluations validated that the innovative ditching knife markedly enhances ditching and soil-throwing quality in sandy soil, fulfills agronomic requirements for tomato sowing, and provides an essential reference for the mechanized planting of crops in the yellow sand matrix cultivation mode of solar greenhouses.

Background: Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by substantial anatomical distortions and sensitivity artifacts, which can hinder its practical utility. Presently, enhancing DWI’s image quality necessitates reliance on cutting-edge hardware and extended scanning durations. The development of a rapid technique that optimally balances shortened acquisition time with improved image quality would have substantial clinical relevance. Objectives: This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. Methods: The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 s/mm2) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 s/mm2). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. Results: The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87–0.90; s-ADC 0.88–0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85–0.86, s-ADC 0.85–0.93; Reader2 ICC: b-ADC 0.86–0.87, s-ADC 0.89–0.92, respectively). Conclusions: ADCVCGAN shows excellent promise in generating gastric cancer DWI images. It effectively reduces scanning time, improves image quality, and ensures the authenticity of s-DWI images and their s-ADC values, thus providing a basis for assisting clinical decision making.

Kernel number per row (KNR) is an essential component of maize (Zea mays L.) grain yield (GY), and understanding its genetic mechanism is crucial to improve GY. In this study, two F7 recombinant inbred line (RIL) populations were created using a temperate–tropical introgression line TML418 and a tropical inbred line CML312 as female parents and a backbone maize inbred line Ye107 as the common male parent. Bi-parental quantitative trait locus (QTL) mapping and genome-wide association analysis (GWAS) were then performed on 399 lines of the two maize RIL populations for KNR in two different environments using 4118 validated single nucleotide polymorphism (SNP) markers. This study aimed to: (1) detect molecular markers and/or the genomic regions associated with KNR; (2) identify the candidate genes controlling KNR; and (3) analyze whether the candidate genes are useful in improving GY. The authors reported a total of 7 QTLs tightly linked to KNR through bi-parental QTL mapping and identified 21 SNPs significantly associated with KNR through GWAS. Among these, a highly confident locus qKNR7-1 was detected at two locations, Dehong and Baoshan, with both mapping approaches. At this locus, three novel candidate genes (Zm00001d022202, Zm00001d022168, Zm00001d022169) were identified to be associated with KNR. These candidate genes were primarily involved in the processes related to compound metabolism, biosynthesis, protein modification, degradation, and denaturation, all of which were related to the inflorescence development affecting KNR. These three candidate genes were not reported previously and are considered new candidate genes for KNR. The progeny of the hybrid Ye107 × TML418 exhibited strong heterosis for KNR, which the authors believe might be related to qKNR7-1. This study provides a theoretical foundation for future research on the genetic mechanism underlying KNR in maize and the use of heterotic patterns to develop high-yielding hybrids.

Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient (RC2) and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient (RP2) and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil.

Drying is one of the best methods to preserve the quality of fresh fruits and prolong their shelf life. This study focuses on Prunus armeniaca L. cv. ‘Diaogan’ (commonly known as Diaogan apricot) sourced from Xinjiang, China to explore the impact of two natural drying methods (shade drying and open-air drying in the rocky desert) on the drying kinetics, color, textural characteristics, microstructure, chemical properties, and antioxidant capacity of Diaogan apricots. The experimental results indicate that throughout the natural drying process, the time required for open-air drying in the rocky desert was reduced by 26.47% compared to shade drying. The L*, a*, and b* values of the shade- and ventilation-dried Diaogan apricots were higher than those sun-dried in the rocky desert, exhibiting a lower color difference (ΔE) than apricots dried through rocky desert sun drying. Specifically, the ΔE for shade-dried Diaogan apricots was 19.66 ± 0.24. The Diaogan apricots dried in the rocky desert exhibited greater hardness, lower elasticity, stronger adhesiveness, and higher chewiness compared to those dried in the shade, with the hardness, adhesiveness, and chewiness being, respectively, 14.71%, 18.89%, and 35.79% higher. Scanning electron microscopy (SEM) observations revealed that the high temperatures experienced during open-air drying in the rocky desert caused rapid dehydration of the Diaogan apricot’s skin, leading to clogging and crust formation in the flesh pores, along with deformation or tearing of the tissue structure, ultimately resulting in poor rehydration ability. After drying, there was a significant increase in the soluble solids in the Diaogan apricots, whereas titratable acidity, total phenols, ascorbic acid, and antioxidant capacity were significantly decreased (p < 0.05). In summary, the quality of dried Diaogan apricots post-drying is dependent on the natural drying method employed, with shade drying resulting in superior quality of Diaogan apricots compared to open-air drying in the rocky desert. This study offers fundamental data and serves as a theoretical reference for the industrialized production of apricots.

An appropriate drying method is crucial for producing high-quality dried apricots. In this study, the effects of four drying methods, hot air drying (HAD), infrared drying (IRD), pulse vacuum drying (PVD), and vacuum freeze-drying (VFD), on the drying kinetics and physical and nutritional characteristics of apricot slices were evaluated. PVD required the shortest time (16.25 h), followed by IRD (17.54 h), HAD (21.39 h), and VFD (34.64 h). VFD resulted in the best quality of apricot slices, with the smallest color difference (ΔE = 13.64), lowest water activity (0.312 ± 0.015) and browning degree (0.35), highest color saturation (62.84), lowest hardness (8.35 ± 0.47 N) and shrinkage (9.13 ± 0.65%), strongest rehydration ability (3.58 ± 0.11 g/g), a good microstructure, and high nutrient-retention rates (ascorbic acid content: 53.31 ± 0.58 mg/100 g, total phenolic content: 12.64 ± 0.50 mg GAE/g, and carotenoid content: 24.23 ± 0.58 mg/100 g) and antioxidant activity (DPPH: 21.10 ± 0.99 mmol Trolox/g and FRAP: 34.10 ± 0.81 mmol Trolox/g). The quality of PVD-treated apricot slices was second-best, and the quality of HAD-treated apricot slices was the worst. However, the energy consumption required for VFD was relatively high, while that required for PVD was lower. The results of this study provide a scientific basis for the large-scale industrial production of dried apricots.

As matrix representations of magic labelings of related hypergraphs, magic squares and their various variants have been applied to many domains. Among various subclasses, trimagic squares have been investigated for over a hundred years. The existence problem of trimagic squares with singly even orders and orders 16n has been solved completely. However, very little is known about the existence of trimagic squares with other even orders, except for only three examples and three families. We constructed normal trimagic squares by using product constructions, row–square magic rectangles, and trimagic pairs of orthogonal diagonal Latin squares. We gave a new product construction: for positive integers p, q, and r having the same parity, other than 1, 2, 3, or 6, if normal p × q and r × q row–square magic rectangles exist, then a normal trimagic square with order pqr exists. As its application, we constructed normal trimagic squares of orders 8q3 and 8pqr for all odd integers q not less than 7 and p, r ∈ {7, 11, 13, 17, 19, 23, 29, 31, 37}. Our construction can easily be extended to construct multimagic squares.

The presence of microplastics in soil has become a significant global concern, posing a threat to environmental, agricultural, and human health. However, the use of various detection methods has led to significant discrepancies in the statistics reported for the abundance of soil microplastics across the available literature, resulting in poor comparability across the available literature. Studies have shown that plastic film residue is among the main primary sources of microplastics in farmland soils. Therefore, we manufactured and selected polyethylene microplastics (PE-MPs) from plastic film and combined them with soil samples after density separation treatment, forming a spiked test soil sample, which was subjected to density separation and extraction using a ZnCl2 solution. The experiment used visual inspection by stereo microscopy, micro-Fourier transform infrared spectroscopy, micro-Raman spectrometry, laser direct infrared imaging, and pyrolysis-gas chromatography–mass spectroscopy for the quantitative analysis of spiked soil. This study systematically assessed and compared these five common soil microplastic detection methods. The results revealed different quantitative detection accuracies across various particle size ranges. Micro-Fourier transform infrared spectroscopy showed a broader applicable particle size range (20–5000 μm) and higher detection rate (91%), while the detection range (>50 μm) and rate (70%) of visual inspection through stereo microscopy were lower. For the identification of microplastic morphology, laser direct infrared imaging performed better, whereas micro-Fourier transform infrared spectroscopy had the advantage in determining the types of polymers present over micro-Raman spectrometry. The advantages, disadvantages, and detection rates of the above five detection methods were clarified during the detection process. Considering these findings and similar studies, we suggest three combinations of the evaluated methods to optimize the detection of PE-MPs.

Film cooling technology is of great significance to enhance the performance of aero-engines and extend service life. With the increasing requirements for film cooling efficiency, researchers and engineers have carried out a lot of work on the precision and digital measurement of cooling holes. Based on the above, this paper outlines the importance and principles of film cooling technology and reviews the evolution of cooling holes. Also, this paper details the traditional measurement methods of the cooling hole used in current engineering scenarios with their limitations and categorizes digital measurement methods into five main types, including probing measurement technology, optical measurement technology, infrared imaging technology, computer tomography (CT) scanning technology, and composite measurement technology. The five types of methods and integrated automated measurement platforms are also analyzed. Finally, through a generalize and analysis of cooling hole measurement methods, this paper points out technical challenges and future trends, providing a reference and guidance for forward researches.

The absorption of microplastics (MPs; size < 5 mm) by plants has garnered increasing global attention owing to its potential implications for food safety. However, the extent to which leafy vegetables can absorb large amounts of MPs, particularly those > 1 μm, remains insufficiently demonstrated. To address this gap in knowledge, we conducted water culture experiments using water spinach (Ipomoea aquatica F.) as a model plant. The roots of water spinach were exposed to a mixed solution that contained fluorescently labeled polystyrene (PS) beads with particle sizes of 200 nm and 1 μm for 10 d. We utilized laser confocal scanning microscopy and scanning electron microscopy to record the absorption, migration, and patterns of accumulation of these large particle sizes of MPs within water spinach. Our findings revealed that micron-sized PS beads were absorbed by the roots in the presence of submicron PS beads and subsequently transported through the exosomes to accumulate to significant levels in the leaves. Short-term hydroponic experiments further indicated that high concentrations of PS bead solutions significantly inhibited the growth of water spinach owing to their large specific surface area that hindered the uptake of water and nutrients by the roots. In conclusion, both sizes of PS beads were found to be absorbed by water spinach, thereby increasing the risk associated with direct human consumption of microplastics in fruits and vegetables. This study provides valuable scientific insights to assess the pollution risks related to fruits and vegetables, as well as ensuring vegetable safety.

With the increasing complexity of engineering environments in tunnel construction, some projects are located in areas with adverse geological conditions, including areas prone to fault movements and other geological hazards. Fiber-reinforced concrete can significantly enhance structural performance in such challenging conditions. Currently, the application of high-performance steel-polypropylene fiber in tunnel linings and its anti-fault performance are quite limited. Therefore, this paper focuses on studying the application of steel-polypropylene mixed fiber-reinforced concrete in tunnel lining under reverse fault movements. In this paper, the constitutive relationship of mixed fiber-reinforced concrete is modified based on literature data. Utilizing the Najar formula, fiber-related variables were designed, and a three-dimensional numerical simulation model of a reverse fault tunnel was established. By designing various parameter combination cases, the structural response of tunnel linings was calculated and studied. then the influence patterns of mixed fiber parameters on the anti-fault performance of tunnel linings are summarized, the relationships between main variables are explored, and a reasonable value range for the two fibers is determined, which can provide guidance for practical engineering.

Girdling is an important means of improving the yield and quality of jujube trees, but this measure can easily cause injury, or even death, to jujube trees. A technology for increasing yield and improving quality, based on local thermal damage of jujube bark, is proposed to address a series of issues in current jujube-tree-girdling technology. First, we measured the thermophysical parameters of jujube bark and established a heat-transfer model for jujube bark. Then, in order to investigate the impact of local thermal damage on jujube-tree yield and fruit quality, local heating experiments were conducted on jujube-tree bark, using the heat-transfer model. The experimental results indicated that heating the jujube bark at a certain temperature for an appropriate time can effectively improve the yield and quality of jujube fruit. Compared with traditional girdling techniques, this method has less impact on the health of jujube trees and does not form permanent wounds on them. The research results provide new ideas for exploring sustainable yield-increase methods for fruit trees.

Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network’s ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network’s feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method’s strong performance and feasibility for rice disease classification in real-life scenarios.

The present study focuses on summarizing the recent advancements in the field of fish swimming mode research and bionic robotic fish prototypes based on smart materials. It has been widely acknowledged that fish exhibit exceptional swimming efficiency and manoeuvrability compared to conventional underwater vehicles. In the pursuit of developing autonomous underwater vehicles (AUVs), conventional experimental methods often prove to be complex and expensive. Hence, the utilization of computer simulations for hydrodynamic modelling provides a cost-effective and efficient approach for analysing the swimming behaviour of bionic robotic fish. Additionally, computer simulations can provide data that are difficult to obtain through experimental methods. Smart materials, which integrate perception, drive, and control functions, are increasingly being applied to bionic robotic fish research. However, the utilization of smart materials in this field is still an area of ongoing research and several challenges remain unresolved. This study provides an overview of the current state of research on fish swimming modes and the development of hydrodynamic modelling. The application of four distinct types of smart materials in bionic robotic fish is then reviewed, with a focus on analysing the advantages and disadvantages of each material in driving swimming behaviour. In conclusion, the paper highlights the key technical challenges that must be addressed for the practical implementation of bionic robotic fish and provides insights into the potential future directions of this field.

Atomically thin two-dimensional (2D) CN sheets have attracted extensive attention in the field of photocatalysis because of their shorter diffusion path of photogenerated carriers and abundant surface reaction sites than bulk CN. However, 2D CNs still exhibit poor visible-light photocatalytic activity because of a strong quantum size effect. Here, PCN-222/CNs vdWHs were successfully constructed using the electrostatic self-assembly method. The results showed that PCN-222/CNs vdWHs with 1 wt.% PCN-222 enhanced the absorption range of CNs from 420 to 438 nm, which improved the absorption capacity of visible light. Additionally, the hydrogen production rate of 1 wt.% PCN-222/CNs is four times that of the pristine 2D CNs. This study provides a simple and effective strategy for 2D CN-based photocatalysts to promote visible light absorption.

Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.

To improve the automation, welding efficiency, and welding quality of duckbill welding of the cotton seeder, this study designed a cotton seeder duckbill welding robot. According to the characteristics of the duckbill weldment and welding requirements, the overall structure of the welding robot was determined, including the girdle feeding mechanism, static duckbill feeding mechanism, hinge feeding mechanism, welding fixture, welding actuator, and control system. To realize the continuous automatic feeding, positioning, fixing, welding, and unloading of the workpiece in the duckbill welding, the feeding mechanism adopts the method of cooperative cooperation of inductive proximity switch, electromagnet, and cylinder. The main body of the welding fixture adopts the pneumatic clamping method; the welding actuator adopts the synchronous belt module electric drive so that the welding torch can move in a straight line along the X axis and the Z axis. The welding process of the duckbill was simulated by Simufact Welding software, and the deformation and stress changes of the weldment were compared and analyzed when the single-sided single welding, the bilateral symmetrical double welding torch, two welding forms, and two welding process parameters were used to determine the welding process parameters of the welding robot. The prototype was made and the welding test was carried out. The test results show that the duckbill welding robot of the cotton seeder has stable feeding, solid clamping, accurate positioning, and high welding efficiency. According to the national standard, the appearance of the duckbill weld is inspected. The surface of the duckbill weld and the heat-affected zone has no cracks, incomplete fusion, slag inclusion, crater, and porosity. The forming quality of the welded parts is good. The design of the duckbill welding robot for cotton seeder is helpful in solving the problems of cumbersome positioning and clamping and low efficiency in manual and semi-automatic duckbill welding robots, which provides a strong guarantee for the large-scale and standardized welding production of the dibbler duckbill.

To mitigate reduced apricot fruit quality due to mildew, browning and sand dust from natural drying under low temperatures and humidity, we determined the characteristics of three different methods of drying (via hanging) and further established their mathematical models. Various comparative analyses of natural drying by laying apricots in an area of the Gobi Desert, subjecting them to ventilated drying in an air-drying house, and hanging them on trees were conducted. The least and most efficient methods were hanging on trees (302 h) and laying them in an area of the Gobi Desert (>192 h), respectively. The loss rate and the total sugar content were 5.26% and 70.16%, and up to 18.31% and 68.54% for fruits dried by ventilated drying in an air-drying house and those hung on trees, respectively. Fruits dried by ventilated drying in an air-drying house showed the least loss and the best comprehensive quality. Using experimental data from assessments of the drying methods, mathematical models were constructed and fitted to drying mathematical models of seven kinds of porous media. The Wang and Singh model had the best fitting degree, an error value of less than 0.05, and the regression that most accurately explained the drying mathematical model for apricots under natural conditions.

In order to study the detection effect of cotton boll opening after spraying defoliant, and to solve the problem of low efficiency of traditional manual detection methods for the use effect of cotton defoliant, this study proposed a cotton detection method improved YOLOv5x+ algorithm. Convolution Attention Module (CBAM) was embedded after Conv to enhance the network’s feature extraction ability, suppress background information interference, and enable the network to focus better on cotton targets in the detection process. At the same time, the depth separable convolution (DWConv) was used to replace the ordinary convolution (Conv) in the YOLOv5x model, reducing the convolution kernel parameters in the algorithm, reducing the amount of calculation, and improving the detection speed of the algorithm. Finally, the detection layer was added to make the algorithm have higher accuracy in detecting small size cotton. The test results show that the accuracy rate P (%), recall rate R (%), and mAP value (%) of the improved algorithm reach 90.95, 89.16, and 78.47 respectively, which are 8.58, 8.84, and 5.15 higher than YOLOv5x algorithm respectively, and the convergence speed is faster, the error is smaller, and the resolution of cotton background and small target cotton is improved, which can meet the detection of cotton boll opening effect after spraying defoliant.

Fault diagnosis of rolling bearings can be a serious challenge, as rolling bearings often work under complex conditions and their vibration signals are typically nonlinear and nonstationary. This paper proposes a novel approach to diagnosing faults of rolling bearings based on variational mode decomposition (VMD) and genetic algorithm-optimized wavelet threshold denoising. First, VMD was used to decompose the vibration signals of faulty rolling bearings into a series of band-limited intrinsic mode functions (BLIMFs). During the decomposition, the parameters of VMD were selected by Kullback–Leibler (K–L) divergence. Then, the effective BLIMFs were determined by the analysis of their correlation coefficients and variance contributions. Finally, genetic algorithm-optimized wavelet threshold denoising was proposed to optimize the selection of important parameters, and the optimized threshold function used not only ensures the continuity of the threshold function but also avoids the fixed deviation of the soft threshold. The validity and superiority of the proposed approach were verified by theoretical calculations, numerical simulations and application studies. The results indicate that the proposed approach is promising in fault diagnosis of rotary machinery.

Film mulching agriculture in arid areas is faced with pollution caused by film mulching, and currently mainly adopts the mechanized recycling of mulch film. However, residual mulch film in the soil will bind with soil under the farming environment, which affects the recycling effect. The main factors affecting the recycling of mulch film in the soil are not clear. In order to find out the specific factors, the actual dry-wet cycle water environment was simulated by using a small soil trough system based on the film lifting, separation and recycling problem of residual mulch film in the soil. The film lifting force and recycling efficiency of the residual mulch film under the action of wet-dry cycle were studied. The following results were obtained: soil compaction, film lifting angle, and the dry-wet cycle had a significant influence on the film lifting force value, indicating that the dry-wet cycle including water fertilizer had an impact on the soil structure. After mechanical loosening, the film lifting force decreased and the recycling rate of residual mulch film increased obviously. The optimal film recycling effect could be obtained under the following conditions, namely, a film lifting angle of 21.37–45.37°, the number of dry-wet cycles 2. The experimental results can provide technical reference for residual mulch film pollution control and mechanized recycling.

Tools for robust identification of crop diseases are crucial for timely intervention by farmers to minimize yield losses. Visual diagnosis of crop diseases is time-consuming and laborious, and has become increasingly unsuitable for the needs of modern agricultural production. Recently, deep convolutional neural networks (CNNs) have been used for crop disease diagnosis due to their rapidly improving accuracy in labeling images. However, previous CNN studies have mostly used images of single leaves photographed under controlled conditions, which limits operational field use. In addition, the wide variety of available CNNs and training options raises important questions regarding optimal methods of implementation of CNNs for disease diagnosis. Here, we present an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) based on different training strategies for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images. We developed a Field-based Wheat Diseases Images (FWDI) dataset of field-acquired images to supplement the public PlantVillage dataset of individual leaves imaged under controlled conditions. We found that a transfer-learning method employing retuning of all parameters produced the highest accuracy for all CNNs. Based on this training strategy, Inception-v3 achieved the highest identification accuracy of 92.5% on the test dataset. While lightweight CNN models (e.g., ShuffleNet-v2 and MobileNetV3) had shorter processing times (

In order to improve fruit yield, girdling technology is widely used in fruit production. However, in the practice of Hui jujube planting in Southern Xinjiang, it was found that the jujube fruit quality declined and the health problems and death rate of jujube trees increased after jujube trees have been girdled continuously for many years, which seriously affected the economic benefits of jujube planting. So, this study was carried out to explore the specific influence law of continuously jujube-tree girdling for many years on the main nutrients (soluble solid, protein, total acid, vitamin C, total sugar, fat and so on), physical quality attributes (single-fruit weight, hardness and geometric-mean diameter) of jujube fruit and tree health. Through the detection of the nutrients of jujube fruit, it was found that the long-term girdling of jujube trees significantly reduced the main nutrients of jujube fruit. The total sugar and reducing sugar content of jujube-fruit samples from the trees without girdling were 134.23% and 109.62% of the corresponding indexes of jujube-fruit samples from girdled ones, respectively; furthermore, the sugar–acid ratio of jujube samples from the girdled trees was 149.95, while the sugar–acid ratio of fruit from non-girdled trees reached 183.78. Moreover, the protein content of fruit from non-girdled trees was 5.91% higher than that of fruit from girdled ones. In addition, the single-fruit weight, hardness and geometric-average diameter of jujube from non-girdled jujube trees were 20.25%, 13.31% and 20.21% higher than those of jujube from girdled ones, respectively. Moreover, microstructural analysis of the callus and the newly formed phloem showed that the adverse effects of girdling on the phloem function were persistent and permanent. It was also found that jujube-tree girdling can also cause damage, to a certain extent, to the xylem around the girdling wound. The above research results further explained the internal cause of jujube-tree death caused by long-term continuously jujube-tree girdling. The results of this study can provide some scientific basis for the effective application of fruit-tree-girdling technology in fruit production.

Main cables, comprising a number of wire strands, constitute a vital element in long-span suspension bridges. The determination of their alignment during construction is of great importance, and relative sag is commonly measured for the efficient sag adjustment of general strands. The conventional approach uses the caterpillar method, which is inconvenient, difficult-to-implement, and potentially dangerous. In order to realize the high-precision measurement of cable alignment in a strong wind environment, a vision-based method for relative sag measurement of the general cable strands is proposed in this paper. In the proposed measurement system, images of pre-installed optical targets are collected and analyzed to realize the remote, automatic, and real-time measurement of the relative sag. The influences of wind-induced cable shaking and camera shaking on the accuracy of the height difference measurement are also theoretically analyzed. The results show that cable strand torsion and camera roll have a great impact on the measurement accuracy, while the impacts of the cable strand swing and vibration, camera swing and vibration, and camera pitch and yaw are insignificant. The vision-based measurement system tested in the field experiment also shows a measurement error within 3 mm, which meets the requirements for cable adjustment construction. At the same time, the vision-based measurement method proposed and validated in this paper can improve the measurement accuracy and efficiency of strand alignment in a strong wind environment. Potential risks involved in the manual measurement, e.g., working at heights and in strong wind environments, can be eliminated, facilitating the automation of the cable erection process.

Fast and precise estimation of the available nitrogen content in vermiculite substrates promotes prescription fertilization in desert facility agriculture. This study explored near-infrared spectroscopy for rapid detection of the available nitrogen content in vermiculite substrates in desert facility agriculture. The spectra of vermiculite matrices with different available nitrogen contents were collected through a self-assembled near-infrared spectrometer. Partial least squares expression (PLSR) established the available nitrogen spectrum prediction model optimized using different pretreatments. After pretreatment, the prediction model of the available nitrogen spectrum was simplified by adopting three feature extraction methods. A comprehensive comparison of the results of each prediction model showed that the prediction model combining the first derivative with SG smoothing pretreatment was the best. The correlation coefficients of the corresponding calibration and prediction sets were 0.9972 and 0.9968, respectively. The root mean square errors of the calibration and prediction sets were 149.98 and 159.65 mg/kg, respectively, with 12.57 RPD. These results provide a feasible method for rapidly detecting the available nitrogen content of vermiculite substrates in desert facility agriculture.

To investigate the thermal deformation behavior of Ti–10V–2Cr–3Al titanium alloy, the hot compression experiments were carried out via a strain rate of 0.1–0.001 s−1 and deformation temperature of 730~880 °C. The results showed that the rheological stress decreases when the deformation temperature increases or strain rate decreases. Due to the deformation conditions, some flow curves exhibited significant discontinuous yielding and flow softening. Flow softening in the α+β phase region was dominated by dynamic recrystallization (DRX), while in the β phase region, it was centered on dynamic recovery (DRV). A high-temperature constitutive equation, with good predictive power, was established.

Jujube girdling is an important method to improve the yield and quality of jujube fruit. However, girdling usually causes the tearing of jujube bark, resulting in the slow healing and even death of jujube trees. These problems directly affect the economic benefits of jujube planting. In this paper, in order to reveal the tearing mechanism of jujube bark caused by girdling, and provide the basic mechanical parameters of jujube bark for the research and development of automatic girdling equipment, the structure and mechanical characteristics of jujube bark were studied through theory and experimental methods. The results of microscopic observation showed that jujube bark is a biological composite material mainly composed of the phloem, periderm and epidermis. Furthermore, a mathematical model-based analysis of the growth stress of jujube bark showed that growth stress can be regarded as a biaxial stress state, which can promote the longitudinal tearing of jujube bark during girdling. The tensile test results of jujube bark showed that jujube bark belongs to the category of anisotropic and biologic material. Its longitudinal and transverse mechanical characteristics are significantly different. The maximum longitudinal tensile load was in the range of 105.9~293 N, the longitudinal tensile strength was 16.3 MPa and the elastic modulus was 255.6 MPa. The average transverse tensile load was only 27.9% of the longitudinal tensile load. The transverse tensile strength was 3.5 Mpa and its elastic modulus was 724.14 MPa. The transverse cutting test of jujube bark showed that the average cutting force was 118.2 N and the cutting strength was 9.8 MPa. The results of this study showed that both the growth stress and the anisotropic natural polymer of jujube bark have adverse effects on girdling. Subsequently, issues relating to the selection of appropriate girdling tools and the matters needing attention in the process of the girdling operation were proposed, so as to guide the correct application of girdling technology. In addition, some suggestions on the research and development of girdling tools in the future are put forward.

Background: The relationship between utricle diseases and structural lesions is not very clear in the clinic due to the complexity and delicacy of the utricle structure. Therefore, it is necessary to study the perception mechanism of the utricle. Methods: Imitating the sensory cells in the macula of the utricle, a symmetrical metal core PVDF fiber (SMPF) was designed as a bionic hair sensor to fabricate a bionic macula (BM), a bionic macula with sand (BMS) and a bionic utricle (BU). Then experiments were carried out on them. Results: This indicated the SMPF sensor can sense its bending deformation, which was similar to the sensory cell. The amplitude of the output charges of the SMPF in BMS and BU were significantly improved. The SMPF, whose electrode boundary was perpendicular to the impact direction, exhibited the largest output charges. Conclusion: The presence of otoliths and endolymph can improve the sensing ability of the utricle. The human brain can judge the direction of head linear accelerations based on the location of the sensory cell in the macula that produces the largest nerve signals. This provides a possibility of studying utricle abnormal functions in vitro in the future.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its Rcv2, RMSEcv, and MAEcv are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path planning as its crucial technology has developed into a hot research direction in the field of USV research. This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the selection and calculation of the target Q value action to eliminate overestimation. The prioritized experience replay method is adopted to extract experience samples from the experience replay unit, increase the utilization rate of actual samples, and accelerate the training speed of the neural network. Then, the neural network is optimized by introducing a dueling network structure. Finally, the soft update method is used to improve the stability of the algorithm, and the dynamic ϵ-greedy method is used to find the optimal strategy. The experiments are first conducted in the Open AI Gym test platform to pre-validate the algorithm for two classical control problems: the Cart pole and Mountain Car problems. The impact of algorithm hyperparameters on the model performance is analyzed in detail. The algorithm is then validated in the Maze environment. The comparative analysis of simulation experiments shows that IPD3QN has a significant improvement in learning performance regarding convergence speed and convergence stability compared with DQN, D3QN, PD2QN, PDQN, PD3QN. Also, USV can plan the optimal path according to the actual navigation environment with the IPD3QN algorithm.

Salination poses serious hazards to farmland soil. For the purpose of solving soil salination, desalination of water sources, and other problems faced by arid areas, a separation and desalination process for farmland saline-alkaline water is proposed; a separation and desalination device based on this process is also presented and tested. Results indicate that water associated with the pretreatment device satisfied the working conditions of the composite nanofiltration (NF)-reverse osmosis (RO) membrane system. The composite NF-RO membrane system produced a better filtering effect than either the NF membrane or the RO membrane. When used for filtering saline-alkaline water, the composite NF-RO membrane system achieved a desalination rate of 96.06%, a total hardness removal rate of 98.93%, and a Cl- removal rate of 99.32%, adhering to the standard for irrigation water quality. The flashing-condensation process realized a fresh water recovery rate greater than 70%. During brine evaporation using solar salt making processes, the primary compound of crystals precipitated was NaCl (with a relative content of 93%), suggesting that the precipitates have the potential values of industrial salts. These findings offer new technical references for solving the problem of farmland irrigation water faced by saline-alkaline areas worldwide.

Customers pay significant attention to the organoleptic and physicochemical attributes of their food with the improvement of their living standards. In this work, near infrared hyperspectral technology was used to evaluate the one-color parameter, a*, firmness, and soluble solid content (SSC) of Korla fragrant pears. Moreover, iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) were applied together to construct evaluating models for their quality parameters. A set of 200 samples was chosen and its hyperspectral data were acquired by using a hyperspectral imaging system. Optimal spectral preprocessing methods were selected to obtain out partial least square regression models (PLSRs). The results show that the combination of multiplicative scatter correction (MSC) and Savitsky-Golay (S-G) is the most effective spectral preprocessing method to evaluate the quality parameters of the fruit. Different characteristic wavelengths were selected to evaluate the a* value, the firmness, and the SSC of the Korla fragrant pears, respectively, after the 6 iterations. These values were obtained via IRIV and the reverse elimination method. The correlation coefficients of the validation set of the a* value, the firmness, and the SSC measure 0.927, 0.948, and 0.953, respectively. Furthermore, the values of the regression error weight, γ, and the kernel function parameter, σ2, for the same parameters measure (8.67 × 104, 1.21 × 103), (1.45 × 104, 2.93 × 104), and (2.37 × 105, 3.80 × 103), respectively. This study demonstrates that the combination of LS-SVM and IRIV can be used to evaluate the a* value, the firmness, and the SSC of Korla fragrant pears to define their grade.

Power capacitors suffer multiple impulse voltages during their lifetime. With the multiple impulse voltage aging, the internal insulation, oil-film dielectric may deteriorate and even fail in the early stage, which is called accumulative effect. Hence, the time-domain dielectric response of oil-film dielectric with multiple impulse voltage aging is studied in this paper. At first, the procedure of the preparation of the tested samples were introduced. Secondly, an aging platform, impulse voltage generator was built to test the accumulative effect of capacitor under multiple impulse voltage. Then, a device was used to test the time-domain dielectric response (polarization depolarization current, PDC) of oil-film dielectric in different aging states. And finally, according to the PDC data, extended Debye model and characteristic parameters were obtained by matrix pencil algorithm identification. The results indicated that with the increase of impulse voltage times, the time-domain dielectric response of oil-film dielectric changed accordingly. The polarization current curve moved up gradually, the insulation resistance decreased when subjected to the repeated impulses. In frequency domain, the frequency spectrum of tan δ changed along with the impulse accumulation aging, especially at low frequency. At last, combined with the aging mechanism of oil-film dielectric under multiple impulse voltage, the test results were discussed.

Lipopolysaccharide (LPS) is the major component of the outer membrane of Gram-negative bacteria, and its integrity is monitored by various stress response systems. Although the Rcs system is involved in the envelope stress response and regulates genes controlling numerous bacterial cell functions of Yersinia enterocolitica, whether it can sense the truncated LPS in Y. enterocolitica remains unclear. In this study, the deletion of the Y. enterocolitica waaF gene truncated the structure of LPS and produced a deep rough LPS. The truncated LPS increased the cell surface hydrophobicity and outer membrane permeability, generating cell envelope stress. The truncated LPS also directly exposed the smooth outer membrane to the external environment and attenuated the resistance to adverse conditions, such as impaired survival under polymyxin B and sodium dodecyl sulfate (SDS) exposure. Further phenotypic experiment and gene expression analysis indicated that the truncated LPS was correlated with the activation of the Rcs phosphorelay, thereby repressing cell motility and biofilm formation. Our findings highlight the importance of LPS integrity in maintaining membrane function and broaden the understanding of Rcs phosphorelay signaling in response to cell envelope stress, thus opening new avenues to develop effective antimicrobial agents for combating Y. enterocolitica infections.

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

Excitation-emission fluorescence spectra is very effective to predict the concentration of organics in samples. However, redundant information and noises in the excitation-emission matrix (EEM) decrease the accuracy of the prediction concentration. Here we proposed a method to select more useful excitation and emission spectra from the EEM to increase the accuracy of prediction concentration and reduce the processing time. First, the excitation wavelengths were selected based on the clustering method to limit the redundant information in the EEM. Then the emission wavelengths were selected based on the Monte-Carlo method. To validate this method, we established the concentration prediction model with the spectra corresponding to the selected wavelengths by partial least square regression and predicted the multi component concentrations in the test samples. Our studies indicate that incorporation of this method increases the accuracy of the prediction concentration of organics and reduces the processing time.

The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality monitoring requirements. Based on the above questions, it’s meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China.

The fabrication and luminescent performance of novel phosphors Na2YMg2V3O12:Dy3+ were investigated by a conventional solid-state reaction method. Under near-UV light, the Na2YMg2V3O12 host self-activated and released a broad emission band (400−700 nm, with a peak at 524 nm) ascribable to charge transfer in the (VO4)3− groups. Meanwhile, the Na2YMg2V3O12:Dy3+ phosphors emitted bright yellow light within both the broad emission band of the (VO4)3- groups and the sharp peaks of the Dy3+ ions at 490, 582, and 663 nm at a quenching concentration of 0.03 mol. The emission of the as-prepared Na2YMg2V3O12:Dy3+ phosphors remained stable at high temperatures. The obtained phosphors, commercial Y2O3:Eu3+ red phosphors, and BaMgAl10O17:Eu2+ blue phosphors were packed into a white light-emitting diode (WLED) device with a near-UV chip. The designed WLED emitted bright white light with good chromaticity coordinates (0.331, 0.361), satisfactory color rendering index (80.2), and proper correlation to a color temperature (7364 K). These results indicate the potential utility of Na2YMg2V3O12:Dy3+ phosphor as a yellow-emitting phosphor in solid-state illumination.

Iron plays an essential role in all organisms and is involved in the structure of many biomolecules. It also regulates the Fenton reaction where highly reactive hydroxyl radicals occur. Iron is also important for microbial biodiversity, health and nutrition. Excessive iron levels can cause oxidative damage in cells. Saccharomyces cerevisiae evolved mechanisms to regulate its iron levels. To study the iron stress resistance in S. cerevisiae, evolutionary engineering was employed. The evolved iron stress-resistant mutant “M8FE” was analysed physiologically, transcriptomically and by whole genome re-sequencing. M8FE showed cross-resistance to other transition metals: cobalt, chromium and nickel and seemed to cope with the iron stress by both avoidance and sequestration strategies. PHO84, encoding the high-affinity phosphate transporter, was the most down-regulated gene in the mutant, and may be crucial in iron-resistance. M8FE had upregulated many oxidative stress response, reserve carbohydrate metabolism and mitophagy genes, while ribosome biogenesis genes were downregulated. As a possible result of the induced oxidative stress response genes, lower intracellular oxidation levels were observed. M8FE also had high trehalose and glycerol production levels. Genome re-sequencing analyses revealed several mutations associated with diverse cellular and metabolic processes, like cell division, phosphate-mediated signalling, cell wall integrity and multidrug transporters.

The formation of black-odor water in urban rivers has a long history. It not only seriously affects the image of the city, but also easily breeds germs and damages the urban habitat. The prevention and treatment of urban black-odor water have long been important topics nationwide. “Action Plan for Prevention and Control of Water Pollution” issued by the State Council shows Chinese government’s high attention to this issue. However, treatment and monitoring are inextricably linked. There are few studies on the large-scale monitoring of black-odor water, especially the cases of using unmanned aerial vehicle (UAV) to efficiently and accurately monitor the spatial distribution of urban river pollution. Therefore, in order to get rid of the limitations of traditional ground sampling to evaluate the point source pollution of rivers, the UAV-borne hyperspectral imagery was applied in this paper. It is hoped to grasp the pollution status of the entire river as soon as possible from the surface. However, the retrieval of multiple water quality parameters will lead to cumulative errors, so the Nemerow comprehensive pollution index (NCPI) is introduced to characterize the pollution level of urban water. In the paper, the retrieval results of six regression models including gradient boosting decision tree regression (GBDTR) were compared, trying to find a regression model for the retrieval NCPI in the current scenario. In the first study area, the retrieval accuracy of the training dataset (adjusted_R2 = 0.978), and test dataset (adjusted_R2 = 0.974) was higher than that of the other regression models. Although the retrieval effect of random forest is similar to that of GBDTR in both training accuracy and image inversion, it is more computationally expensive. Finally, the spatial distribution graphs of NCPI and its technical feasibility in monitoring pollution sources were investigated, in combination with field observations.

In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman’s rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R2, RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.

The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.

Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400−900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination ( R 2 ) of 0.98, a root mean square error (RMSE) of 0.68 mg/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.

Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.

The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and coarse mode aerosols) in East Asia. Here, using ground-based observations in six AERONET sites, we characterize typical aerosol optical properties (including their spatiotemporal variation) in eastern China, and evaluate their impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrieval bias. Both the SSA and fine-mode particle sizes increase from northern to southern China in winter, reflecting the effect of relative humidity on particle size. The SSA is ~0.95 in summer regardless of the AEROENT stations in eastern China, but decreases to 0.85 in polluted winter in northern China. The dominance of larger and highly scattering fine-mode particles in summer also leads to the weakest phase function in the backscattering direction. By focusing on the analysis of high aerosol optical depth (AOD) (>0.4) conditions, we find that the overestimation of the AOD in Dark Target (DT) retrieval is prevalent throughout the whole year, with the bias decreasing from northern China, characterized by a mixture of fine and coarse (dust) particles, to southern China, which is dominated by fine particles. In contrast, Deep Blue (DB) retrieval tends to overestimate the AOD only in fall and winter, and underestimates it in spring and summer. While the retrievals from both the DT and DB algorithms show a reasonable estimation of the fine-mode fraction of AOD, the retrieval bias cannot be attributed to the bias in the prescribed SSA alone, and is more due to the bias in the prescribed scattering phase function (or aerosol size distribution) in both algorithms. In addition, a large yearly change in aerosol single scattering properties leads to correspondingly obvious variations in the time series of MODIS AOD bias. Our results reveal that the aerosol single scattering properties in the MODIS algorithm are insufficient to describe a large variation of aerosol properties in eastern China (especially change of particle size), and can be further improved by using newer AERONET data.

For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future.

This paper analyses the effects of doubly fed induction generator (DFIG) tidal current turbines on a distribution grid under unbalanced voltage conditions of the grid. A dynamic model of an electrical power system under the unbalanced network is described in the paper, aiming to compare the system performance when connected with and without DFIG at the same location in a distribution grid. Extensive simulations of investigating the effect of DFIG tidal current turbine on stability of the distribution grid are performed, taking into account factors such as the power rating, the connection distance of the turbine and the grid voltage dip. The dynamic responses of the distribution system are examined, especially its ability to ride through fault events under unbalanced grid voltage conditions. The research has shown that DFIG tidal current turbines can provide a good damping performance and that modern DFIG tidal current power plants, equipped with power electronics and low-voltage ride-through capability, can stay connected to weak electrical grids even under the unbalanced voltage conditions, whilst not reducing system stability.

Maximum power point tracking (MPPT) plays an important role in increasing the efficiency of a wind energy conversion system (WECS). In this paper, three conventional MPPT methods are reviewed: power signal feedback (PSF) control, decreased torque gain (DTG) control, and adaptive torque gain (ATG) control, and their potential challenges are investigated. It is found out that the conventional MPPT method ignores the effect of wind turbine inertia and wind speed fluctuations, which lowers WECS efficiency. Accordingly, an improved adaptive torque gain (IATG) method is proposed, which customizes adaptive torque gains and enhances MPPT performances. Specifically, the IATG control considers wind farm turbulences and works out the relationship between the optimal torque gains and the wind speed characteristics, which has not been reported in the literature. The IATG control is promising, especially under the ongoing trend of building wind farms with large-scale wind turbines and at low and medium wind speed sites.