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This paper develops and verifies a multiscale computational fluid dynamics (CFD) model to investigate the airflow and heat transfer in kiwifruit cold storage under forced-air cooling (FAC). The CFD model incorporates the material properties, geometry, position of kiwifruit and ventilated packaging box, and the detailed structure of the cooling unit. For the multiscale modeling, the material characteristics are described using three interconnected sub-models, focusing each on different spatial scales: the warehouse-scale, packaging-scale, and kiwifruit-scale. In the FAC experiment, the measured airflow and temperature on these three spatial scales were obtained and compared with the simulated results. The data analysis shows that the environmental fluctuations at different scales weaken significantly in a stepwise manner with the cushioning of packaging and fruit flesh, indicating coupling between the spatial scales, which is well reflected in the numerical simulation. The average kiwifruit temperature decreases from 20 to 6 °C in 14.5 h (experimental) and 15.6 h (simulated). Specifically, the average mean absolute error, mean absolute percentage error, and root mean squared error of the predicted airflow and kiwifruit temperature were 0.116 m s−1, 1.26 °C; 26.8%, 14%; and 0.124 m s−1, 1.54 °C, respectively. These results indicate that the multiscale CFD model accurately and efficiently simulates the airflow and spatiotemporal temperature distribution in the given kiwifruit FAC system. Finally, this study provides a reference for accurately simulating large-scale industrial FAC systems and supports optimal decision-making for the design of sustainable kiwifruit cold chains. • Multiscale CFD modeling of warehouse-packaging-kiwifruit under forced-air cooling. • The established model is verified to be accurate through FAC experimental data. • Fine perception of product-scale in large-scale industrial FAC process. • Analysis of spatiotemporal coupling characteristics of multiscale environments. [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]

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]

Nonalcoholic fatty liver (NAFL) is emerging as a leading risk factor of hepatic ischemia/reperfusion (I/R) injury lacking of effective therapy. Lipid dyshomeostasis has been implicated in the hepatopathy of NAFL. Herein, we investigate the bioactive lipids that critically regulate I/R injury in NAFL. Lipidomics were performed to identify dysregulated lipids in mouse and human NAFL with I/R injury. The alteration of corresponding lipid-metabolizing genes was examined. The effects of the dysregulated lipid metabolism on I/R injury in NAFL were evaluated in mice and primary hepatocytes. Sphingolipid metabolic pathways responsible for the generation of sphingosine-1-phosphate (S1P) were uncovered to be substantially activated by I/R in mouse NAFL. Sphingosine kinase 1 (Sphk1) was found to be essential for hepatic S1P generation in response to I/R in hepatocytes of NAFL mice. Sphk1 knockdown inhibited the hepatic S1P rise while accumulating ceramides in hepatocytes of NAFL mice, leading to aggressive hepatic I/R injury with upregulation of oxidative stress and increase of reactive oxygen species (ROS). In contrast, administration of exogenous S1P protected hepatocytes of NAFL mice from hepatic I/R injury. Clinical study revealed a significant activation of S1P generation by I/R in liver specimens of NAFL patients. In vitro studies on the L02 human hepatocytes consolidated that inhibiting the generation of S1P by knocking down SPHK1 exaggerated I/R-induced damage and oxidative stress in human hepatocytes of NAFL. Generation of S1P by SPHK1 is important for protecting NAFL from I/R injury, which may serve as therapeutic targets for hepatic I/R injury in NAFL. Image 1 • I/R challenge activates the metabolic pathways for the generation of S1P in hepatocytes of NAFL mice. • Knockdown of Sphk1 exaggerates NAFL I/R by upregulating oxidative stress with ROS production in mice hepatocytes. • Exogenous S1P alleviate hepatic I/R injury by inhibiting oxidative stress with ROS production in hepatocytes of NAFL mice. • Inhibition of S1P generation aggravates I/R-induced injury in human hepatocytes of NAFL by promoting oxidative stress. [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]

Food safety has garnered much worldwide attention recently for reasons that are, unfortunately, not always positive. Traceability system (TS) is designed to assure safe and good quality food, while reducing the costs of food recalls. It should encompass all stakeholders, including governments, companies, and consumers, each of whom has an important role in the implementation and guardianship of such systems. The EU and China are amongst the main players implementing TS and are constantly exploring new opportunities and monitoring challenges for TS in a time of shifting consumer demands and rapid new technology innovation. This article states development stages from TS 1.0 to 3.0. and reviews TS development in a number of key countries and regions. Comparisons between the EU and China are drawn in terms of government, corporate, and consumer involvement in traceability. A functional TS, while providing bi-directional communication between trading partners, must meet the laws and regulations where it operates. A functional system must also consider consumer value and perception, which varies with geography. There are a variety of promising technologies available on the market today to modernize TS, including artificial intelligence (AI) and blockchain. A key finding of this research is that both the EU and China have developed significant trade links in recent years which will certainly positively impact both economies. Key to underpinning the sustainability of these trade links will be the adoption of common TS to prevent negative associations. • Present a comprehensive analysis framework from government, company and customer perspectives. • Comparative review between the EU and China. • Outline the trend of traceability development with the AI and blockchain technologies. • Highlight the strategic technological and consumer-based requirements for global scale traceability systems. • Provide multi-stakeholder perspectives and inputs of traceability. [ABSTRACT FROM AUTHOR]

Abstract Radio frequency identification (RFID) and quick response (QR) codes are an effective way to identify traceable resource units (TRUs) and are widely applied in traceability systems. TRU transformation is common for things such as beef segmentation and wheat-flour packaging. The present study describes the development and testing of equipment for barcode-RFID bidirectional transformation to improve traceability by conserving identification association and information correspondence while transforming barcodes to RFID and vice versa. The framework of traceable bidirectional identification labels is based on comparing the features of RFID and barcodes and on analyzing TRU transformation. The proposed equipment includes a tactile industrial controller, a RFID-reader module, an embedded printing module, and a barcode scanning module. Furthermore, we develop the main functions of RFID-barcode conversion, bar-code-RFID conversion, and condition monitoring. The system is tested in field experiments based on two typical scenarios: beef segmentation and wheat-flour packaging. The average conversion success rate in RFID-barcode processing is 97%, and in barcode-RFID processing is 93.48%. The proposed equipment is more rapid than the reference method: 3.2 versus 8.7 s for RFID-barcode transformation and 10.5 versus 13.3 s for barcode-RFID transformation. In addition, the proposed equipment is less expensive than the conventional equipment. Test results indicate that continuous traceability is improved because identification association and information correspondence are conserved after TRU transformation. Hightlights • Proposed a method to improve continuous traceability of food stuff by TRUs relating. • Developed a barcode-RFID transformation equipment to implement the proposed method. • Performed an experiments on two typical scenario to validate the feasibility. [ABSTRACT FROM AUTHOR]

Highlights • Freshness of tilapia fillets under variable temperatures were evaluated. • HSI and RBFNN determined freshness of fish under non-isothermal conditions. • Four freshness indicators are predicted simultaneously. • Nine optimal wavelengths are selected by SPA for further analysis. • RBFNN proved accurate for predicting freshness under non-isothermal conditions. Abstract This study develops a reliable radial basis function neural networks (RBFNNs) to estimate freshness for tilapia fillets stored under non-isothermal conditions by using optimal wavelengths from hyperspectral imaging (HSI). The results show that, for tilapia fillet stored at −3, 0, 4, 10, and 15 °C and non-isothermal conditions, total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and the K value increase whereas sensory scores decrease with increasing storage time. To simplify the models, nine optimal wavelengths were selected by using the successive projections algorithm (SPA), following which SPA–RBFNN models were built based on the selected wavelengths and the values of TVB-N, TAC, K , and sensory evaluations for tilapia fillets store isothermally. The ability of the models based on HSI to predict the freshness indicators were verified for tilapia fillets stored under non-isothermal conditions. HSI thus has an excellent potential for nondestructive determination of freshness in tilapia fillets. [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]

Recent legal requirements and market demands have motivated more food companies to implement traceability systems. Ensuring safe farming practices is the first step in food supply chain traceability, and reasonable pesticide use is a main feature of food safety and sustainable production. This study describes the design and development of a cloud-based platform for rational pesticide use to guarantee the source safety of traceable vegetables. The system includes a pesticide use control cloud platform (PUCC) and a pesticide user application (PUA), which interactively guide users through the steps of pesticide purchasing, pesticide application, harvest time, and pesticide evaluation. Models for evaluating and recommending potential pesticides were developed based on an open library of pesticide use rules. The PUCC, which includes the main functions of farmer registration, authentication of platform administrator, and information management for plant protection service agencies, was developed using Microsoft Visual Studio 2010 and deployed on the Internet. The PUA provides interfaces for pesticide purchasing guideline, pesticide application, optimal harvest time, and feedback. As a case study, the system was used for about a year in 24 vegetable bases in Tianjin. The effectiveness of the system was evaluated by investigating 8 management center staff members and 41 farmers. Management agencies noted the positive effects of promoting reasonable pesticide use, facilitating information accessibility, and enhancing management. Advantages to farmers included reducing the risk of unreasonable pesticide usage, decreasing the risk of counterfeit pesticides, and improving vegetable quality and safety; disadvantages included increased costs and reduced efficiency. In addition, the system improved external and internal traceability to ensure crop quality and safety. [ABSTRACT FROM AUTHOR]

The study assessed the feasibility of developing a machine vision system based on pupil and gill color changes in tilapia for simultaneous prediction of total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA) and total viable counts (TVC) during storage at 4 °C. The pupils and gills were chosen and color space conversion among RGB, HSI and L ∗ a ∗ b ∗ color spaces was performed automatically by an image processing algorithm. Multiple regression models were established by correlating pupil and gill color parameters with TVB-N, TVC and TBA (R 2 = 0.989–0.999). However, assessment of freshness based on gill color is destructive and time-consuming because gill cover must be removed before images are captured. Finally, visualization maps of spoilage based on pupil color were achieved using image algorithms. The results show that assessment of tilapia pupil color parameters using machine vision can be used as a low-cost, on-line method for predicting freshness during 4 °C storage. [ABSTRACT FROM AUTHOR]

Logistics and storage is the main processing for agro-food supply chain. Because of disconnection information between the two processing, it is difficult to trace continuously. An intelligent conveyer belt provides an effective method to associate storage and logistics by QR code scanning and information recording. Improving the QR code readability in movement state is the core of implementing continuous chain traceability with this belt. In this paper, a intelligent conveyer belt including automatic conveyer unit, barcode scanning unit, fault remove unit and control display unit was designed. Four factors affected QR readability were selected and the value range was confirmed, which was reading distance, code size, coded characters and belt moving speed. Based on the belt, an Central Composite Inscribed (CCI) experiment of four factors with five levels was designed using Response Surface Methodology (RSM) to obtain the optimal reading parameters. The result shows that the main factors of reading distance, belt moving speed and the interaction between reading distance and code size have the significant effect on QR code readability. Under the optimization condition of 141.45 mm reading distance, 34.58 mm code size, 100 bytes coded characters and 2.98 m/min belt moving speed, the average value of QR code readability was 95%. With the optimization parameters, the intelligent conveyer belt was used in an apple marketing enterprise. The result shows that the continuous traceability between storage and logistic can be implemented with the extended breadth, deepened depth and improved precision. [ABSTRACT FROM AUTHOR]

Recent developments in the legal establishment and the market have motivated more agro-food companies to implement traceability systems (TS). TS play an important role not only for planning system implementation before development, but also for analyzing system performance after using the system. A novel agro-food TS model is presented here, based on comprehensive and quantifiable granularity concepts. A 2-layer index system was established; the first layer was mainly factors such as precision, breadth, and depth, and the second layer included seven indicator sub-factors: external trace units, internal flow units, IU conversion, information collection content, information update frequency, forward tracking distance, and backward tracing distance. An indicator’s overall score was scaled with five contributing scores that graded the assignment method. Indicator weight was confirmed with the AHP method. The weight values of the seven indicators were 0.1985, 0.1141, 0.0872, 0.1870, 0.1248, 0.1442, and 0.1442, respectively. A weighted sum model was adopted to calculate the evaluation value. A high evaluation value indicated high granularity. The granularity model was applied in two enterprises, here identified as WPF and WFPE, which were located at different stages in wheat-flour supply chain. The survey results showed that WFPE should invest more in tracing equivalent granularity than WPF should because it involves multi-stage processing, a complicated supply chain structure, it is a large enterprise, and operates in a strict regulatory environment. Furthermore, WFPE was motivated to implement a high granularity level because of benefits in supply chain management, market and customer response, and recall and risk management. In the future, an updated granularity evaluation model that could combine enterprise characteristics and uncover hidden costs and benefits will be studied further. [ABSTRACT FROM AUTHOR]

Cold chains are effective in maintaining food quality and reducing food losses, especially for long-distance international food commerce. Several recent reports have demonstrated that frozen foods are serving as carriers of SARS-CoV-2 and transmitting the virus from one place to another without any human-to-human contact. This finding highlights significant difficulties facing efforts to control the spread of COVID-19 and reveal a transmission mechanism that may have substantially worsened the global pandemic. Traditional food cold chain management practices do not include specific procedures related to SARS-CoV-2-related environmental control and information warnings; therefore, such procedures are urgently needed to allow food to be safely transported without transmitting SARS-CoV-2. In this study, a conjoint analysis of COVID-19 and food cold chain systems was performed, and the results of this analysis were used to develop an improved food cold chain management system utilizing internet of things (IoT) and blockchain technology. First, 45 COVID-19-related food cold chain incidents in China, primarily involving frozen meat and frozen aquatic products, were summarized. Critical food cold chain control points related to COVID-19 were analyzed, including temperature and cold chain requirements. A conceptual system structure to improve food cold chain management, including information sensing, chain linking and credible tracing, was proposed. Finally, a prototype system, which consisted of cold chain environment monitoring equipment, a cold chain blockchain platform, and a food chain management system, was developed. The system includes: 1) a defining characteristic of the newly developed food cold chain system presented here is the use of IoT technology to enhance real-time environmental information sensing capacity; 2) a hybrid data storage mechanism consisting of off-chain and on-chain systems was applied to enhance data security, and smart contracts were used to establish warning levels for food cold chain incidents; and 3) a hypothetical food cold chain failure scenario demonstration in which information collection, intelligent decision making, and cold chain tracing were integrated and automatically generated for decision-making. By integrating existing technologies and approaches, our study provides a novel solution to improve traditional food cold chain management and thus meet the challenges associated with the COVID-19 pandemic. Although our system has been shown to be effective, subsequent studies are still required to develop precise risk evaluation models for SARs-CoV-2 in food cold chains and more precisely control the entire process. By ensuring food safety and reliable traceability, our system could also contribute to the formulation of appropriate mechanisms for international cooperation and minimize the effect of the COVID-19 pandemic on international food commerce. Check critical cold chain control points related to COVID-19 and environment. Credible cold chain traceability combining IOT and blockchain technology. Improve traditional food cold chain management under global COVID-19 pandemic. [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]

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]

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]

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]

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]

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]

Shelf-life is an important tool for visually conveying remaining food quality. However, the traditional printed shelf-life is a fixed value based on a stable environment, whereas food supplies undergo long-term dynamic conditions. Moreover, the accumulating effects of real-world uncertainty will lead to stochastic changes in remaining shelf-life in a probabilistic manner. Therefore, we proposed a novel dynamic shelf-life prediction method integrating the kinetic reaction and stochastic process. First, to predict practical changes in food quality indices (FQIs), we established the stochastic kinetic model, which combines basic deterministic modeling with zero-order reaction and the Arrhenius equation, and stochastic factor modeling with the Wiener process. Second, we conducted real-time probability analysis of remaining shelf-life to quantify the potential degeneration of food quality based on the established model. By using datasets monitoring firmness and Vitamin C of Kiwifruit in long-term cold storage to verify the performance of our proposed integrated model, we showed that our method was more accurate in modeling stochastic changes in FQIs than the traditional reaction model, resulting in mean absolute error, mean absolute percentage error, and root mean squared error (RMSE) less than 0.2136, 0.0162, and 0.7402, respectively. Furthermore, the shelf-life probability analysis was efficient, with relative RMSE 5.26% and computing time 0.047 s. This valuable food quality information can be provided to managers or customers to reduce food loss and waste. • A dynamic shelf-life prediction method considering actual uncertainty is proposed. • MAE, MAPE and RMSE of stochastic kinetic model are less than 0.2136, 0.0162, 0.7402. • Relative RMSE of shelf-life prediction reaches 5.26%. • The running time of the proposed algorithm is less than 0.047s. [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]

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]

• Established the mechanism of Sirt1, which alleviated liver injury associated with poly I:C-induced PBC. • Sirt1 activation by RSV inhibited the phosphorylation and acetylation levels of RelA/p65 subunit of NF-kB. • RSV improved the interaction between Sirt1 and p65, thereby reducing liver inflammation. This report sought to establish the mechanistic role of sirtuin-1 (Sirt1), a NAD+-dependent deacetylase in the modulation of primary biliary cholangitis (PBC) pathogenesis. 64 PBC patients (diagnosed based on practice guidelines for American Association for the Study of Liver Diseases) and 60 healthy controls were included in this study. Clinically, the mRNA expression level of Sirt1 in macrophages differentiated from peripheral blood mononuclear cells (PBMCs) of PBC subjects substantially decreased when compared with the healthy controls but not in other Sirt family genes (Sirt2-7). Consistent with clinical results, a PBC murine model showed that levels of Sirt1 significantly decreased in the liver and Kupffer cells of mice treated with polyinosinic/polycytidylic acid (poly I:C) for 16 weeks. A TAK1 inhibitor (NG25) prevented the poly I:C-induced Sirt1 protein level decreasing in Kupffer cells but not MAPK inhibitor. Sirt1 activators resveratrol (RSV) and SRT1720 (SRT) ameliorated poly I:C-induced hepatic injury observed via histopathologic analysis and decreased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels in the PBC murine model. Furthermore, Sirt1 activators significantly reduced pro-inflammatory cytokines levels such as interleukin-1 beta (IL-1β), IL-6, interferon-gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α) in serum in poly I:C-induced mice. In addition, Sirt1 activators significantly inhibited the phosphorylated and acetylated levels of the RelA/p65 subunit of the nuclear transcription factor (NF-κB) but not the interferon regulatory factor (IRF) 3 in poly I:C-injured mice livers. Significantly, RSV improved the interaction between Sirt1 and p65, which may contribute to the decreased activity of NF-κB. In summary, the Sirt1 signaling pathway plays an essential role in the development of PBC and this may represent a novel approach and target for the treatment of PBC. [ABSTRACT FROM AUTHOR]