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As the global fruit growing area continues to increase and the population aging problem continues to intensify, fruit and vegetable production is constrained by the difficulties of labor shortages and high costs. Single-arm harvesting robots are inefficient, and in order to balance harvesting accuracy and efficiency, research on multi-arm harvesting robots has become a hot topic. This paper summarizes the performance of multi-arm harvesting robots in indoor and outdoor environments from the aspects of automatic navigation technology, fruit and vegetable identification and localization, multi-arm workspace optimization, and multi-arm harvesting task planning and analyzes their advantages and challenges in practical applications. The results show that the lack of application of automatic field navigation for multi-arm harvesting robots, the low harvesting rate in non-structured environments, and the complexity of algorithms for multi-arm harvesting robots' task planning are the main challenges hindering their wide-scale application. Future studies need to focus on building a standardized growing environment to control the amount of information acquired by the robots and optimize the multi-arm control strategy of these challenges, which is an important direction for research on multi-arm harvesting robots. [ABSTRACT FROM AUTHOR]

The mechanical properties of a plug seedling substrate determine whether it will crush during the transplantation, thereby affecting the integrity of the root system and the survival rate of transplanted seedlings. In this study, we measured eight morphological parameters of pepper seedlings using machine vision and physical methods, and the corresponding substrate mechanical parameters of the plug seedlings were tested using a texture analyzer. Based on the experimental data, a BPNN framework was constructed to predict the substrate mechanical properties of plug seedlings at different growth stages. The results indicate that the BPNN with a framework of [8, 15, 15, 1] exhibits higher R2 and lower errors. The mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error (MAPE) values are 7.669, 88.842, and 9.076%, respectively, with an R2 of 0.867. The average prediction accuracy of 20 test data set is 90.472%. Finally, predictions and experimental validations were conducted on the substrate mechanical properties of seedlings grown for 47 days. The results revealed that the BPNN achieved an average prediction accuracy of 93.282%. Additionally, it exhibited faster speed and lower computational costs. This study provides a reference for the non-intrusive estimation of substrate mechanical properties in plug seedlings and the design and optimization of transplanting an end-effector. [ABSTRACT FROM AUTHOR]

In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning. [ABSTRACT FROM AUTHOR]

Determining pre-harvest fruit maturity is vital to ensure the quality of kiwifruit, and dry-matter content is an important indicator of kiwifruit ripeness. To predict the pre-harvest dry-matter content of kiwifruit continuously in real-time with high accuracy, this study uses hyperspectral data of pre-harvest Jintao kiwifruit obtained by using a hyperspectral image acquisition device. The raw data underwent whiteboard correction, spectral data extraction, spectral pre-processing, and feature-band extraction, following which the dry-matter content of the fruit was predicted by using partial least squares (PLS) regression. The feature bands extracted by the random frog method were 538.93, 671.14, 693.41, 770.61, 796.98, 813.24, 841.21, 843.29, and 856.80 nm, which improve the accuracy of the PLS method for predicting dry-matter content, with R2 = 0.92 and a root mean square error (RMSE) of 0.41% for the training set, and R2 = 0.85 and a RMSE of 0.50% for the test set. These results show that the proposed method reduces the number of required bands while maintaining the prediction accuracy, thereby demonstrating the reliability of using hyperspectral data to predict the pre-harvest dry-matter content of kiwifruit. This method can effectively guide the management of kiwifruit harvesting period, establishing a theoretical foundation for precise unmanned harvesting. [ABSTRACT FROM AUTHOR]

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards. [ABSTRACT FROM AUTHOR]

The effectiveness evaluation of the traceability system (TS) is a tool for enterprises to achieve the required traceability level. It plays an important role not only for planning system implementation before development but also for analyzing system performance once the system is in use. In the present work, we evaluate traceability granularity using a comprehensive and quantifiable model and try to find its influencing factors via an empirical analysis with 80 vegetable companies in Tianjin, China. We collect granularity indicators mostly through the TS platform to ensure the objectivity of the data and use the TS granularity model to evaluate the granularity score. The results show that there is an obvious imbalance in the distribution of companies as a function of score. The number of companies (21) scoring in the range (50,60) exceeded the number in the other score ranges. Furthermore, the influencing factors on traceability granularity were analyzed using a rough set method based on nine factors pre-selected using a published method. The results show that the factor "number of TS operation staff" is deleted because it is unimportant. The remaining factors rank according to importance as follows: Expected revenue > Supply chain (SC) integration degree > Cognition of TS > Certification system > Company sales > Informationization management level > System maintenance investment > Manager education level. Based on these results, the corresponding implications are given with the goal of (i) establishing the market mechanism of high price with high quality, (ii) increasing government investment for constructing the TS, and (iii) enhancing the organization of SC companies. [ABSTRACT FROM AUTHOR]

The granite complex in the Maohuo area of Litang County is located in the central and western parts of the Yidun-Shaluri island arc in the Sanjiang area; the intrusion is located in the sand slate of the Tumugou Formation in Daocheng District. In this paper, the petrographic analysis of the granodiorite porphyry and biotite monzogranite in the granite complex in the Volcano area is carried out, a systematic study of rock geochemical characteristics and chronology, elucidating the genetic connection between the two in the nature and tectonic background of the source region. Zircon LA-ICP-MS U–Pb dating shows, granodiorite porphyry, medium-coarse-grained biotite monzogranite, the ages of diorite inclusions in biotite monzonitic granites are 220.7 ± 1.3 Ma, 220.4 ± 1.6 Ma, and 219.8 ± 1.4 Ma, three are roughly formed at the same time, and it is a product of late Triassic magmatic activity. Rock geochemical features show that granodiorite porphyry in the granite complex, biotite monzonite granite, scintillation inclusions it has high total alkali, Rb content and relatively low Al2O3, Sr and other content, has a moderately negative europium anomaly, obvious loss of Ta, Nb, Ti and other high field strength elements, and aluminum saturation index < 1.1 is characterized. Combined with geological data granodiorite porphyry, both biotite monzonitic granites are also I-type granites formed in a typical island-arc environment. Combined with rock geochemical characteristics and U–Pb chronology research results, three should be a set of rock assemblages formed by the evolution of a set of magma melting events. Among them, biotite monzogranite and granodiorite porphyry are the products of homologous magma crystallization and differentiation. The diorite enclaves in the biotite monzogranites are the result of their own evolution when the granitic magma undergoes fusion and immiscibility. [ABSTRACT FROM AUTHOR]

This article examines the characteristics of a penetrator with a triangular cross-section perpendicular impacting a semi-infinite metal target. Experiments and numerical simulations were conducted at an impact velocity of 1600 to 1800 m/s. The model, which was constructed to analyze penetration depth, showed the effect of the nose-shaped structure of the penetrator on penetration characteristics. The model can analyze the penetration characteristics of a penetrator with triangular cross-section that attacks semi-infinite targets. The results obtained by the model are primarily attributed to the self-sharpening characteristics of the penetrator. [ABSTRACT FROM AUTHOR]

Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low‐temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade‐off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon. Industry empowerment through next‐generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real‐time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low‐carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development. [ABSTRACT FROM AUTHOR]

Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing. [ABSTRACT FROM AUTHOR]

Background: Liver hepatocellular carcinoma (HCC) is the third most common cause of death by cancer and has a high mortality world-widely. Approximately 75– 85% of primary liver cancers are caused by HCC. Uncovering novel genes with prognostic significance would shed light on improving the HCC patient's outcome. Objective: In this research, we aim to identify novel prognostic biomarkers in hepatocellular carcinoma. Methods: Integrated proteomics and bioinformatics analysis were performed to investigate the expression landscape of prognostic biomarkers in 24 paired HCC patients. Results: As a result, eight key genes related to prognosis, including ACADS, HSD17B13, PON3, AMDHD1, CYP2C8, CYP4A11, SLC27A5, CYP2E1, were identified by comparing the weighted gene co-expression network analysis (WGCNA), proteomic differentially expressed genes (DEGs), proteomic turquoise module, The Cancer Genome Atlas (TCGA) cohort DEGs of HCC. Furthermore, we trained and validated eight pivotal genes integrating these independent clinical variables into a nomogram with superior accuracy in predicting progression events, and their lower expression was associated with a higher stage/risk score. The Gene Set Enrichment Analysis (GSEA) further revealed that these key genes showed enrichment in the HCC regulatory pathway. Conclusion: All in all, we found that these eight genes might be the novel potential prognostic biomarkers for HCC and also provide promising insights into the pathogenesis of HCC at the molecular level. [ABSTRACT FROM AUTHOR]

Global food trade has become an increasingly crucial element for feeding the world's population. Enhancing bilateral or multilateral trust in food safety in international food trade is not only important for promoting the sustainable development of trade but is also beneficial for cooperation when facing a global food crisis. However, highly credible traceability systems (TSs) for the cross‐border movement of food are still absent in many countries and regions. Blockchain is regarded as a promising technology that can help build trust for transparency and security issues. In this paper, an interconnected conceptual traceability framework based on blockchain is proposed in order to increase trust in food safety during food trade. Taking the food trade between China and the European Union as an example, a conceptual framework is designed in order to take full advantage of existing TSs in these two locations, and the features of logistical flow, data flow, and blockchain flow are analyzed. Considering the data capacity and data privacy level, a hybrid data storage method combining on‐chain and off‐chain is adopted. Smart contracts according to the features of cross‐border food trade—including the recording of exportation data, exporter inspection data, shipment data, importer inspection data, importation data, and tracing queries—are packaged and deployed to a blockchain network. An effective operation mechanism involving the distribution of related rights for different roles is presented. The blockchain‐based TS framework has the advantages of enhancing bilateral trust in cross‐border food trade, providing a flexible and intelligent technical framework, and having effective operability. Future challenges, such as data security, special smart contracts, and consensus mechanisms, and interoperability with other systems, are discussed. [ABSTRACT FROM AUTHOR]

Purpose: Emerging evidences have raised concerns about electrolyte disorders caused by restrictive fluid management in the enhanced recovery after surgery (ERAS) protocol. This study aims to investigate the morbidity and treatment of electrolyte disorders associated with ERAS in patients undergoing hepato-pancreato-biliary (HPB) surgery. Methods: Clinical data from 157 patients under the ERAS program and 166 patients under the traditional (Non-ERAS) program after HPB surgery were retrospectively analyzed. Risk factors and predictive factors of postoperative electrolyte disorders were analyzed by logistic regression analysis and receiver operator characteristic (ROC) curve analysis, respectively. Results: The average of intravenous fluid, sodium, chloride, and potassium supplementation after surgery were significantly lower in the ERAS group. Hypokalemia was the most common type of electrolyte disorders in the ERAS group, whose incidence was substantially increased compared to that in the Non-ERAS group [28.77% vs. 8.97%, p < 0.001, on postoperative (POD) 5]. Logistic regression analysis identified the ERAS program and age as independent risk factors of hypokalemia. ROC curve analysis identified serum potassium levels below 3.76 mmol/L on POD 3 (area under curve 0.731, sensitivity 58.54%, specificity 82.69%) as a predictive factor for postoperative hypokalemia in ERAS patients. Oral supplementation at an average of 35.41 mmol potassium per day was effective in restoring the ERAS-associated hypokalemia. Conclusions: ERAS procedures were particularly associated with a lower supplementation of potassium and a higher incidence of hypokalemia in patients after HPB surgery. Oral potassium supplementation could be an adopted ERAS program for the elderly undergoing HPB surgery. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. MicroRNA-942 (miR-942) plays a critical role in promoting proliferation and metastasis of cancer cells and is associated with poor prognosis in some types of cancers. However, the prognostic value of miR-942 and its functional role in HCC remain unclear. Materials and methods: Real-time PCR (RT-PCR) was used to detect the expression of miR-942 in HCC tissues and adjacent normal liver tissues. Next, the correlations between miR-942 expression and clinicopathological parameters including the survival rate were analyzed. Interaction between miR-942 and ribonucleotide reductase regulatory TP53 inducible subunit M2B (RRM2B) was determined by RT-PCR, Western blot and luciferase assay. The biological influence of miR-942 on HCC cell lines was studied using CCK-8 assay, colony formation assay and transwell assay in vitro. Western blot and RT-PCR were used to analyze the change of downstream genes after miR-942 mimics transfection. Results: miR-942 was significantly up-regulated in HCC. Its high expression was associated with serum alanine transaminase level (P=0.0350), tumor size (P=0.0195), T stage (P=0.0045) and lymphatic metastasis (P=0.0013). High expression of miR-942 was associated with shorter overall survival and disease-free survival time of HCC patients. RRM2B was validated as a target gene of miR-942. miR-942 mimics markedly promoted the malignant phenotypes of Huh7 and MHCC97H cell lines, while its inhibitor had the opposite effect. miR-942 can regulate the downstream genes of RRM2B including Egr-1 and PTEN, markers of epithelial-mesenchymal transition and matrix metalloproteinases. Conclusion: miR-942 may serve as a potential biomarker for HCC and its inhibitor may be a therapeutic agent for the treatment of this deadly disease. [ABSTRACT FROM AUTHOR]

This study developed principal component analysis and radial basis function neural networks (PCA-RBFNNs) for predicting freshness in tilapia fillets stored at different temperatures by integrating an electronic nose and electronic tongue. Total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and K value increased at 0, 4, 7, and 10 °C, while sensory scores decreased significantly. The electronic nose and tongue acquired the volatiles and dissolved chemical compounds in the stored samples. Gas chromatography-mass spectrometry (GC-MS) verified the changes in gas species and contents in fillets stored for different periods of time at different temperatures. PCA-RBFNNs based on data fusion were developed and presented good performance for prediction of TVB-N, TAC, K value, and sensory score in tilapia fillets. The established PCA-RBFNNs based on feature variables of the electronic nose and tongue is a promising method to predict changes in the freshness of fillets stored from 0 to 10 °C in the cold chain. [ABSTRACT FROM AUTHOR]

Herein, a highly-efficient quantum dot light-emitting diode (QLED) with stacked blue and yellow quantum dot (QD) layers can be demonstrated to output white light. A buffer layer of ZnO nanoparticles (NPs) inserted between two QD layers can prevent them from mixing and penetrating. In order to promote charge balance, Zn0.95Mg0.05O was employed as the electron transport layer (ETL), which can block excessive electrons due to the 0.07 eV higher conduction band minimum (CBM) level compared to that of pristine ZnO, leading to the suppression of exciton quenching. In addition, the lower defect concentration and lowered conductivity of Zn0.95Mg0.05O due to the reduction of oxygen vacancies by doping the Mg element into ZnO can also benefit the inhibition of exciton quenching. The device can be demonstrated with a maximum luminance current efficiency of 24.6 cd A−1 and power efficiency of 25.8 lm W−1, exhibiting a 19% and 38% enhancement, respectively. The strategy demonstrated here demonstrates a promising way to realize highly efficient white QLEDs and also other optoelectronic devices. [ABSTRACT FROM AUTHOR]

A serum metabolomic method based on ultra-high-performance liquid chromatography coupled with mass spectrometry was developed to characterize hyperuricemia-related metabolic profiles and delineate the mechanism of Sanmiao wan (SMW), a traditional Chinese medicine (TCM), in treating hyperuricemic rats. With partial least-squares discriminant analysis for classification and selection of biomarkers, 13 potential biomarkers in mouse serum were identified in the screen, primarily involved in purine metabolism, arginine and proline metabolism, citrate cycle, phenylalanine metabolism, tryptophan metabolism and glycerophospholipid metabolism. Taking these potential biomarkers as screening indexes, SMW could reverse the pathological process of hyperuricemia through partially regulating the perturbed metabolic pathway except for glycerophospholipid metabolism. Our results showed that a metabolomic approach offers a useful tool to identify hyperuricemia-related biomarkers and provides a new methodological cue for systematically dissecting the underlying efficacies and mechanisms of TCM in treating hyperuricemia. [ABSTRACT FROM AUTHOR]

In order to protect the quality and safety of products of the fresh cut vegetable processing and improve fresh cut vegetable production and processing efficiency, a fresh cut vegetable processing batch mixing optimization model was put forward, which can reduce the recall cost resulted from the mixed batches during the manufacture. The batch-mixing phenomenon of raw materials often occurs during the production process of fresh-cut vegetables. On the other hand, different batches of materials have different properties which will exert some effects on the rate of the production yield. Proper allocation of the materials' batches will reduce the recall cost of defective products. In order to enhance the ability of production efficiency and improve the supervision on the quality and safety for fresh-cut vegetables, an optimized model for this problem should be developed. In this research, after the analysis of the basic process in fresh-cut vegetables manufacture, a model based on the order of the daily manufacture in enterprise and the batch of materials was established, which could reduce the batch-mixing level of the materials and improve the production efficiency. The manufacture process of the fresh-cut vegetables was taken as the core research point, and the experiment parameters were obtained from a cooperative enterprise in Beijing which was a typical manufacture enterprise of fresh-cut vegetables. Unlike other areas of vegetable manufacture, the manufacture of fresh-cut vegetables needed to take the different products and different batches of material processing into account. According to the actual production requirements of small and medium-sized fresh-cut vegetable processing enterprises, the production process, which could be well applicable to the pattern of single material warehouse and single warehouse of finished product, was studied in details. Based on the reality limits, the orders' sequence and the batch of materials should be optimized. The processing of product order and the selection and sorting of raw material batch belong to the NP-Hard problem, and the scale of the problem rapidly increased with the expanding of the order and batch. So it was necessary to adopt intelligent optimization methods to solve the problem. And based on the actual condition in the manufacture enterprises of fresh-cut vegetables, the processing sequence of the manufacture order and the selection sequence of the different batches of materials were optimized using genetic algorithm. The population size was set to 100, two-point crossover rate was set to 0.9, the mutation rate was set to 0.1, the weight of material mixing was set to 0.3, the average yield for the processing was set to 0.7 and the maximum amount of iterations was limited to 50. The objective function consisted of the average recall size and the average yield of product. The model was applied in a manufacture enterprise of fresh-cut fruits and vegetables, and the test results showed that the value of the objective function was improved by 10.5%, which could reduce the average recall rate and improve the production efficiency. This model can provide suggestion of material batch distribution and optimization of production for the small and medium-sized fresh-cut vegetable processing enterprises. [ABSTRACT FROM AUTHOR]

Background: A subset of interleukin (IL)-10-producing regulatory B (Breg) cells that suppress T-cell-mediated immunity was recently identified; however, their role in chronic hepatitis B (CHB) remains elusive. Aim: To explore the possible role of Breg in the interaction with Th cells and consequent pathogenesis of CHB. Methods: The prevalence of Breg as well as 3 major effector T-cell subsets-CD4CD25Foxp3 regulatory T (Treg) cells, T helper 1 cells (Th1), and T helper 2 cells (Th2)-was assessed in the peripheral blood of 31 patients with CHB, 28 patients with acute hepatitis B (AHB), and 25 healthy controls (HC). Results: Compared to patients with AHB and HC, the prevalence of Breg and Treg cells and the concentration of IL-10 in the supernatant of cultured peripheral blood mononuclear cells (PBMCs) were greatly increased in patients with CHB. A significantly decreased proportion of Th1 cells was also observed in patients with CHB and was demonstrated to have a negative correlation with the prevalence of Breg. Furthermore, depletion of Treg cells in the PBMCs of patients with CHB did not alter the frequency of Breg cells or their ability to produce IL-10, indicating little, if any, impact of Treg cells on the generation and maintenance of Breg cells. Conclusions: Our data indicate that increased Breg cells might be a major source of elevated IL-10 in CHB and represent a critical and independent regulatory force in the development of impaired anti-HBV immunity, consequently contributing to the pathogenesis of CHB. [ABSTRACT FROM AUTHOR]

In apple cultivation, simulation models may be used to monitor fruit size during the growth and development process to predict production levels and to optimize fruit quality. Here, Fuji apples cultivated in spindle-type systems were used as the model crop. Apple size was measured during the growing period at an interval of about 20 days after full bloom, with three weather stations being used to collect orchard temperature and solar radiation data at different sites. Furthermore, a 2-year dataset (2011 and 2012) of apple fruit size measurements were integrated according to the weather station deployment sites, in addition to the top two most important environment factors, thermal and sunshine hours, into the model. The apple fruit diameter and length were simulated using physiological development time (PDT), an indicator that combines important environment factors, such as temperature and photoperiod, as the driving variable. Compared to the model of calendar-based development time (CDT), an indicator counting the days that elapse after full bloom, we confirmed that the PDT model improved the estimation accuracy to within 0.2 cm for fruit diameter and 0.1 cm for fruit length in independent years using a similar data collection method in 2013. The PDT model was implemented to realize a web-based management information system for a digital orchard, and the digital system had been applied in Shandong Province, China since 2013. This system may be used to compute the dynamic curve of apple fruit size based on data obtained from a nearby weather station. This system may provide an important decision support for farmers using the website and short message service to optimize crop production and, hence, economic benefit. [ABSTRACT FROM AUTHOR]

Image segmentation is the precondition of feature extraction and recognition. In order to improve segmentation accuracy of touching objects in pest identification and counting system, an image segmentation algorithm based on shape factor and separation point location was presented. In this method, a shape factor that was defined using area and perimeter of a region was used to be a parameter to justify whether the region was one touching region or not. In this paper, the threshold of shape factor was set to be 0.50. And then, if a region was a touching one, its contour was stripped layer by layer. In each contour, it was necessary to check whether a local segmentation point existed or not. There were two types of local segmentation points. The first type was a point that was found twice in one contour at the same time, whose traversal sequence number satisfied the determined threshold condition. The second type was one point that could be found in one contour with its four connected region points at the same time, and the difference between their traversal sequence numbers satisfied the same threshold condition. Once the local segmentation point was found, two separating points of this touching region were searched and located in its original contour. The search method was based on the shortest distance between the local segmentation and the background pixel points. At last, the segmentation lines were plotted between the local segmentation and the two separating points. In order to verify the validity of the proposed algorithm, three types of touching images, such as serial connection, loop connection and hybrid connection images were used. The results showed that the proposed method could locate the local segmentation points and separating points more accurately than the watershed method. In addition, the lab and field images were used to test reliability of the proposed method. In the lab experiment, 100 yellow peach moth (Conogethes punctiferalis(Guenée) ) were collected and divided into two independent groups with 50 individuals in each one. In the field experiment, two sticky trap images of the Oriental fruit moth (Grapholitha molesta (Busck) ) were used. In this paper, three criteria such as SR (segmentation rate), SERR (segmentation error rate), and SEFR (segmentation efficiency rate) were used to evaluate the segmentation results between the proposed method and the watershed method. The results showed that, in the lab experiment, the mean SR of watershed method was more than the proposed method, but the average segmentation error rate of the proposed segmentation method was 7%, which was reduced by 6 percentage points than the watershed method. The average segmentation efficiency rate of the proposed segmentation method was 92.65%, which was more than watershed method by 5.7 percentage points. In the field experiment, the average segmentation error rate of the proposed segmentation method was 2.24%, which was reduced by 4.29 percentage points than the one of the watershed method. The average segmentation efficiency rate of the proposed segmentation method was 97.8%, which was more than the one of watershed method by 3.95 percentage points.The series of data showed that the proposed segmentation algorithm located points accurately and its invalid segmentation rate was low. The presented segmentation method for touching pest image could improve the segmentation performance and had a remarkable significance for the feature extraction and pest identification. [ABSTRACT FROM AUTHOR]

The detection information of farm products increases rapidly, and plays an important role in traceability. The existed detection processing method used the mode of testing sample with detection instruments and manual recording data. This mode leads to the hazard of unassociated batches and distorted information, so the direct and credible effects of detection information in the traceability content are needed. In this paper, the reasons why detection information is not able to be integrated in the existed traceability system were analyzed. Modified system framework with detection information extracting and identification was proposed, which combined the rapid real-time detection instrument for vegetable pesticide residues mainly using enzyme inhibition method. In the detection process, information including detection ID, farm product batch and inhibition rate was recorded. In order to implement the automation reading and extracting of detection information, the access flowchart of detection data included the steps of instrument connection, vegetable detection, data extraction and data storing. The correlation between vegetable plant batch, detection identification number and product traceability code was the other important step for detection information tracing. Because one vegetable plant batch may be divided into many package units, the relationship between the batch code of vegetable plants and product traceability code was the type of 'one to many'. Because of the similar characteristics in one plant batch, the same batch of vegetable was detected once and the relationship between vegetable plant batch code and detection identification code was the type of 'one to one'. The batch correlation between product traceability code and detection identification code was established with the link of vegetable plant batch code. Based on .Net platform and the existed vegetable safety production management system, the vegetable production management and traceability system was updated for three functions. The detection instrument linked the vegetable production management and traceability system with serial port, and linking self-checking was implemented in the instrument link function. Then detection data was obtained through special data format. The detection data could be displayed but not modified in the data management function. The updated system was applied in 153 vegetable production bases in Tianjin city. During March 1st to May 30th in 2014, 28038 vegetable samples were detected and data obtain success rate with the updated system was 100%, and the data acquirement efficiency was improved. Furthermore, the detection results of 20 samples were compared between rapid real-time detection by enterprise self-check and casual inspection by supervision departments. The 20 samples were divided into two groups with No.1-8 samples using the manual recording mode of detection data without the updated the system and No.9-20 obtaining the detection data by automatic reading with the updated the system. The application results showed that the data acquisition success rate was 100% and the difference between the enterprise self-check value and the supervision casual inspection value was low with the absolute value of 4.37 after using the updated the system. The detection value with manual recording of No.3 sample was obviously lower than the one by supervision departments, which has the possible of distort data. Furthermore, the traceability results for using and not using the updated system were compared. Through using the system, the traceability results could indicate the precision detection information through the batches' correlation. The study in this paper presents an important step for satisfying the deeper requirement in traceability system. [ABSTRACT FROM AUTHOR]

The detection information of farm products increases rapidly, and plays an important role in traceability. The existed detection processing method used the mode of testing sample with detection instruments and manual recording data. This mode leads to the hazard of unassociated batches and distorted information, so the direct and credible effects of detection information in the traceability content are needed. In this paper, the reasons why detection information is not able to be integrated in the existed traceability system were analyzed. Modified system framework with detection information extracting and identification was proposed, which combined the rapid real-time detection instrument for vegetable pesticide residues mainly using enzyme inhibition method. In the detection process, information including detection ID, farm product batch and inhibition rate was recorded. In order to implement the automation reading and extracting of detection information, the access flowchart of detection data included the steps of instrument connection, vegetable detection, data extraction and data storing. The correlation between vegetable plant batch, detection identification number and product traceability code was the other important step for detection information tracing. Because one vegetable plant batch may be divided into many package units, the relationship between the batch code of vegetable plants and product traceability code was the type of 'one to many'. Because of the similar characteristics in one plant batch, the same batch of vegetable was detected once and the relationship between vegetable plant batch code and detection identification code was the type of 'one to one'. The batch correlation between product traceability code and detection identification code was established with the link of vegetable plant batch code. Based on .Net platform and the existed vegetable safety production management system, the vegetable production management and traceability system was updated for three functions. The detection instrument linked the vegetable production management and traceability system with serial port, and linking self-checking was implemented in the instrument link function. Then detection data was obtained through special data format. The detection data could be displayed but not modified in the data management function. The updated system was applied in 153 vegetable production bases in Tianjin city. During March 1st to May 30th in 2014, 28038 vegetable samples were detected and data obtain success rate with the updated system was 100%, and the data acquirement efficiency was improved. Furthermore, the detection results of 20 samples were compared between rapid real-time detection by enterprise self-check and casual inspection by supervision departments. The 20 samples were divided into two groups with No.1-8 samples using the manual recording mode of detection data without the updated the system and No.9-20 obtaining the detection data by automatic reading with the updated the system. The application results showed that the data acquisition success rate was 100% and the difference between the enterprise self-check value and the supervision casual inspection value was low with the absolute value of 4.37 after using the updated the system. The detection value with manual recording of No.3 sample was obviously lower than the one by supervision departments, which has the possible of distort data. Furthermore, the traceability results for using and not using the updated system were compared. Through using the system, the traceability results could indicate the precision detection information through the batches' correlation. The study in this paper presents an important step for satisfying the deeper requirement in traceability system. [ABSTRACT FROM AUTHOR]

Pest identification and classification is time-consuming work that requires expert knowledge for integrated pest management. Automation, including machine vision combined with pattern recognition, has achieved some applications in areas such as fruit sorting, robotic harvesting, and quality detection, etc. Automatic classification and counting of pests using machine vision is still a challenge because of variable and uncertain poses of trapped pests. Therefore, using Pseudaletia separata, Conogethes punctiferalis, Helicoverpa armigera, Agrotis ypsilon with different poses as research objects, this paper presents a novel classification method for multi-pose pests based on color and texture feature groups and using a multi-class support vector machine. 320 images were taken using field samples with an original resolution of 4 288×2 848. The subimages of pests with 640×640 pixel size were obtained from original images for computational efficiency. Color features in RGB and HSV spaces, statistical texture features, and wavelet-based texture features were extracted. Six feature vector groups were constructed using those features. In order to select effective feature parameters of each group, a genetic algorithm was designed to optimize feature vectors based on 10-fold cross-validation. Finally, the one-against-one DAGMSVM (acronym as yet undefined) algorithm was applied to classify and recognize the four kinds of target pests and to find the best feature group. 80 images (60 for the training set and 20 for the testing set) were adopted for each species. Parameter numbers were calculated and analyzed after optimization, thus the best parameters were selected for each group. The training time of the SVM model and classification accuracy, which contains false negative and false positive details, were compared between pre-optimization and post-optimization. The results showed that the highest parameter optimization ratio is from the sixth feature group with a dimension reduction rate of 61.11%. Compared with the RGB and statistical texture feature group F2, the optimization ratio of HSV and statistical texture feature group F3 is much better; that is, the latter one is more suitable to pest classification. Analysis and comparison between the optimization results of feature group F5 and F6 shows that the latter one is more suitable for multi-pose pest classification. The modeling time of each group has been greatly decreased, especially the one of group F6 (about 8 s), which is the minimum time of all groups with a decreased rate of 74.5%. Average accuracies of all groups have been improved beyond 97%. The sixth group has the highest accuracy (100%). Consequently, the sixth feature group, the feature vector of the wavelet filter in HSV color space, is an effective feature set for use in the classification of multi-pose pests. In addition, we have found that the feature parameters are similar among the misclassification pest sets, which may be improved by increasing the number of sample images in the training set. [ABSTRACT FROM AUTHOR]

With the development of research and application of traceability systems, it plays a key role that the traceability system is established with appropriate granularity for deeper application according to the supply chain actual requirement and informatization level of subjects. The existing research in traceability width, depth, and accuracy were descriptive language, or lacked a quantitative evaluation model, which lead to the difference of the traceability system not being able to be measured well. In this paper, three layers of architecture of a basic traceability system were analyzed, which included product identification, information collection, and database construction of the center. The descriptive definition of traceability granularity was established with traceable unit size and its horizontal information capacity and longitudinal tracking depth. A two-layer index system was constructed with the core of traceability precision, width, and depth. The evaluation factors included a external traceability unit, an internal turning unit, an identification unit exchange, information collection content, information update frequency, forward tracing distance, and backward tracing distance. In order to eliminate qualitative indicators difficult to measure, a five point grading assignment method was used to obtain a qualitative index value. The evaluation factors' weight was confirmed using an AHP (analytic hierarchy process) method with three steps through investigation on 20 related areas experts. First, the initial judgment matrix and comparison matrix was established through the comparison of various elements. Secondly, a single order layer was taken. Finally, the final judgment matrix was obtained through the consistency test, and then all the target weights set were obtained. In the weights set, the external traceability unit weight was the highest with 0.1985. For the cooperative supply chain, an external traceability unit with the difference between a single product, single batch and the mixed batch, affected other unit sizes and information collection, so it played an important role in the whole index system. The lowest weight was an identification unit exchange with the value of 0.0872. Because the identification unit exchange had a correlation with an external traceability unit and an internal turning unit, the identification unit exchange had relatively less effects on traceability granularity with a determined internal and external unit. This research can provide quantitative evidence for evaluating the difference of traceability systems. [ABSTRACT FROM AUTHOR]

Tectono-geochemical samples were systematically collected from 5 drillcores along the No. 304 exploration line and at the −310 m level under the pit in the mining area, totalling 705 samples from the metamorphic rocks, granites, altered rocks and orebodies, and were determined for their contents of 20 kinds of elements including Au, Ag, As, Sb, Hg, Cu,-Pb, Zn, Sn, Bi, Mo, Co, Ni, Mn, Cr, V, Ti, Ba, Rb, Sr, etc. By using the Surfer software the geochemical exploration line profile maps for the 20 kinds of elements and the curves for the element geochemical contents of individual ore vein groups were established. In conbination with the geochemical map analytical method and Gregorian's zoning index calculation method, the vertical, longitudinal and lateral zonation sequences of the elements were ascertained and the geochemical three-dimentional zonation model of the primary halos was established on the basis of the analysis of metallogenic structures and alteration zonation. Coupled with the results of analysis of the geology and geochemistry data, it may be concluded that the process of alteration of granites in the mining area is also accompanied with the process of gold enrichment and mineralization. With the intensification of alteration of granites from granite →potash feldspathization granite →sericite-quartz alteration granite, sericite-quartz rocks →beresitized granite, pyrite sericite-quartz rock, silicified granite →gold ore, the contents of thiophile ore-forming elements such as Au, Ag, As, Cu, Bi, Mo, Pb, Sb, Hg, and Sn tended to increase. Factor analysis of trace elements indicated: factor F2 (Au, Ag, Cu, Sn, As) represents the element association brought in at the main stage of hydrothermal metallogenesis; factor F4 (Bi, Sb) and factor F5 (Pb, Zn) represent the ore-forming element association supperposed during the late stage of Au-bearing sulfides. By using the Gregorian's zoning index and map analysis method we have ascertained the primary halo axial zonation sequence (form frontal halo →tail halo): Pb, Zn, Mn, Ba, Sr, Rb, As, Sb, Ag, Cu, Sn, Mo, Au, Bi, Hg, Ti, Cr, V, Ni, and Co. The geochemical anomalies in the mining area display a tendency of lateral plunging to WS, which is consistent with the lateral plunging of orebodies and mineralization alteration zone. Comprehensive analysis of the results of investigations on ore-controlling structures, mineralization-alteration zonation and geochemical zonation of the primary halos indicates that the gold orebodies in this area are still of greater extension. [ABSTRACT FROM AUTHOR]

Through a systematic observation of water level and temperature, and a comprehensive analysis of the data on major/trace elements, nitrite, hydrogen-oxygen isotopes, the conclusion has been drawn that there are two relatively independent groundwater systems (cool water and hot water), and the geochemical indicators of hot/cool waters are described. The cool water system is relatively enriched in Ca2+, Mg2+ and HCO3 −. Its TDS is relatively low, about 1400–1800 mg/L. The hot water system is relatively enriched in K+, Na+, Cl− and SO4 2−. Its TDS is relatively high, about 2200-2300 mg/L. The cool water system is enriched in Ba, Ga, Cd, and the hot water system is enriched in B, Ti, Cr, Ni, Cu, Mo, Rb, and Cs, relatively. Especially, the contents of Rb and Cs in the hot water system are more than five times as high as those in the cool water system. The NO3 − contents of cool water discharged from the gold mine are relatively high, and those of hot water are extremely low. The δD and δ18O values follow an increasing order of surface water>mine cool water>mine hot water. The cool water comes mainly from the lateral supply of phreatic water, while the hot water comes mainly from the vertical supply of deeply circulating structure-fracture water. The ratio of cool water over hot water was estimated to be about 1:1 by a water quality model.. [ABSTRACT FROM AUTHOR]