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There is rich knowledge in expert systems that can be used to solve practical problems, but its promotion and application must rely on information facilities. The application of both computers and the Internet for Chinese farmers are not common, which leads to restrictions on the promotion and application of expert systems in rural areas of China. On the other hand, the existing call centers lack a professional knowledge base and the method of automatically calling the knowledge base in real-time, which makes it difficult to meet the needs of users wanting to obtain knowledge in a timely manner. To address these problems, a call center embedded in an expert system inference algorithm and knowledge base for farmers to obtain agricultural knowledge through mobile phones or fixed-line telephones was established. By studying the event-condition-action-based (ECA-based) database triggering model, remote method invocation-based (RMI-based) communication and iterative dichotomiser 3 algorithm-based (ID3-based) parameter extraction, the cohesion between the call center and the expert system was realized. The agricultural knowledge audio acquisition model was then coupled with the call center and the expert system was constructed, allowing farmers to acquire agricultural knowledge through mobile phones or fixed phones with fast responses. When used for cotton disease diagnosis, it can achieve a high diagnostic success rate (above 75%) when at least three disease symptoms are input into the expert system via the voice call, which provides an effective channel for Chinese farmers to obtain agricultural knowledge. It presents good application prospects in China, where 5G technology is currently developing rapidly.

In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

Leafy vegetables are consumed in most daily xdiets worldwide. As living standards improve, food quality, safety requirements, and nutrition are becoming increasingly important to consumers when purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables, spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional quality and safety traceability system based on browser/server architecture and quick response (QR) code is then designed and developed for full traceability of leafy vegetable quality. This method can ensure food safety and hygiene through the control of key factors affecting food safety throughout the entire supply chain process.

Automatic visual inspection (AVI) is a popular tool for object detection and analysis in many fields. In fisheries, AVI systems designed for fish egg development analysis are used for fish egg assessments in commercial and experimental fish production systems and in ecological monitoring. The first step in AVI systems is to subdivide an image into meaningful regions. The accuracy of this segmentation directly influences the success of subsequent image analysis operations such as feature selection and classification. At present, a lot of image segmentation techniques are available, but there is no universal segmentation technique suitable for all kinds of images.This paper presents a cascading automatic segmentation method for microscopic digitised images of live common carp (Cyprinuscarpio) eggs in Petri dishes filled with water. The proposed method involves five main steps:1) image pre-processing, 2) image initial segmentation by Otsu’s method, 3) segmentation post-processing based on morphological operations, 4) image edge detection and morphological operations to remove incomplete eggs, and finally 5) a Watershed method was used to resolve the problem of separating adjoining fish eggs. Testing the method in Matlab software verified that fish eggs were recognized, segmented and counted with 100% accuracy compared to manual observation in all 96 test cases.

Aquatic products are well received by customers from around the world. Quality and safety of aquatic products have attracted increasing attention, especially in emerging economies. Cased by actual aquatic products cold chain logistics, this article introduces the efforts for developing and evaluating the intelligent monitoring system for aquatic products (IMS-CS): an intelligent traceability system integrated wireless sensor network with statistical process control. Observations, interviews, literature review and experts’ questionnaires were used for system requirement and knowledge acquisition; actual aquatic products cold chain logistics scenario was applied to evaluate and validate the performance of IMS-CS. The results show that that the IMS-CS could trace and monitor the real time temperature fluctuation and the quality of aquatic products, which improve the transparency of the cold chain logistics and enables more effective control of the traceability, quality and safety. The valuable suggestion indicates that the process was under medium risk level and had potential to be improved. Practical Applications The proposed method could be exploited widely by future researchers or practitioners in cold chain monitoring and traceability tasks.

Wireless Sensor Network (WSN) is applied widely in food cold chain logistics. However, traditional monitoring systems require significant real-time sensor data transmission which will result in heavy data traffic and communication systems overloading, and thus reduce the data collection and transmission efficiency. This research aims to develop a temperature Monitoring System for Frozen and Chilled Aquatic Products (MS-FCAP) based on WSN integrated with Compressed Sending (CS) to improve the efficiency of MS-FCAP. Through understanding the temperature and related information requirements of frozen and chilled aquatic products cold chain logistics, this paper illustrates the design of the CS model which consists of sparse sampling and data reconstruction, and shelf-life prediction. The system was implemented and evaluated in cold chain logistics between Hainan and Beijing in China. The evaluation result suggests that MS-FCAP has a high accuracy in reconstructing temperature data under variable temperature condition as well as under constant temperature condition. The result shows that MS-FCAP is capable of recovering the sampled sensor data accurately and efficiently, reflecting the real-time temperature change in the refrigerated truck during cold chain logistics, and providing effective decision support traceability for quality and safety assurance of frozen and chilled aquatic products.

A new method for extracting key frames of greenhouse videos was proposed.The method took full considerations of characteristics of greenhouse videos.The method combined the visual saliency and online clustering.The IG algorithm was modified by two parameters confirmed by a new measurement. Research reported in this paper aims to improve the identification of greenhouse vegetable diseases based on the greenhouse monitoring video. It presents a method that combines the visual saliency and online clustering to extract the key frame from greenhouse vegetables monitoring video. Firstly X2 histograms are used to measure the similarity of each frame to the first frame, which eliminates the meaningless frames and improve data processing efficiency and costs. Then, all frames will be converted to HSV color space and a saliency map of each frame is generated based on H component value and S component value. According to the saliency map, the salient region can be obtained. During the process of extracting the salient region, there is a possibility that the information of disease spots is lost. Therefore, morphological method would be utilized to restore the lost information. Finally, online clustering is performed to classify the salient regions into different clusters, and mean pixels value is used to select the key frames. The results indicate that this method can obtain information of entire leaf area of vegetables and extract the key frame effectively.

BACKGROUND The main export varieties in China are brand-name, high-quality bred aquatic products. Among them, tilapia has become the most important and fast-growing species since extensive consumer markets in North America and Europe have evolved as a result of commodity prices, year-round availability and quality of fresh and frozen products. As the largest tilapia farming country, China has over one-third of its tilapia production devoted to further processing and meeting foreign market demand. RESULTS Using by tilapia fillet processing, this paper introduces the efforts for developing and evaluating ITS-TF: an intelligent traceability system integrated with statistical process control (SPC) and fault tree analysis (FTA). Observations, literature review and expert questionnaires were used for system requirement and knowledge acquisition; scenario simulation was applied to evaluate and validate ITS-TF performance. CONCLUSION The results show that traceability requirement is evolved from a firefighting model to a proactive model for enhancing process management capacity for food safety; ITS-TF transforms itself as an intelligent system to provide functions on early warnings and process management by integrated SPC and FTA. The valuable suggestion that automatic data acquisition and communication technology should be integrated into ITS-TF was achieved for further system optimization, perfection and performance improvement. © 2014 Society of Chemical Industry

Temperature monitoring, shelf-life visibility and Least Shelf-life First Out (LSFO) stock strategy are important contents in perishable food cold chain logistics for both cold chain managers and workers in order to reduce quality and economic losses. This paper illustrates a wireless sensor network (WSN) based integrated Cold Chain Shelf Life Decision Support System (C2SLDS) designed for perishable food product cold chain management. The system is implemented based on the WSN and time temperature indicator (TTI) features. Compared with traditional cold chain management methods used before, the C2SLDS not only bridges the information gap which exists between different cold chain phase enterprises and provide a seamless information flow along the whole chain but also enables cold chain enterprises to predict perishable food's shelf-life and helps make a smart LSFO strategy to reduce the quality and economic loss. System test and evaluation shows that the infield radio transmission is reliable and the whole system meets most of the users' requirements raised in system analysis.

Cold chain is one of the most important ways to guarantee the quality and safety of table grapes. It is necessary to monitoring the temperature in the whole cold chain process to keep the constant temperature and guarantee the quality and safety of table grapes in cold chain. This paper aims to develop a battery-free wireless sensor system with compressed sensing for the table grapes cold chain. The battery-free wireless sensor system was realized by using the ultra high frequency radio frequency identification (UHF RFID) on-chip temperature sensor with no battery. The temperature fluctuation performance of the table grapes in the cold chain, the compressed sensing (CS) performance of the temperature reconstructed error with the original data and the battery-free wireless sensor system with compressed sensing evaluation were comprehensively analyzed and discuss. The developed battery-free wireless sensor system with compressed sensing could effectively monitor the actual temperature with high reconstruction accuracy to save the data storage space, transmission and operation resources, improve the temperature transparency in cold chain, and finally ensure the quality and safety of table grapes. The developed battery-free wireless sensor system with compressed sensing could be extended for other cold chain applications to improve their transparency and transmission efficiency of the wireless monitoring. It is also possible to integrate the relative humidity, gas sensors into the battery-free wireless sensor system to monitor more environment parameters in cold chain.

Beef has become a kind of important food in China because of its nutritional value perceived by consumers. With increasing consumers’ awareness and governments’ regulations on beef quality and safety, traceability is becoming a mandatory requirement in cattle/beef industry. This paper developed and evaluated a cattle/beef traceability system that integrated RFID technology with PDA and barcode printer. The system requirements, the business flow of the cattle/beef chain, and the key traceability information for the system were identified through a survey. Then a conceptual model was proposed to describe the process of traceability information acquisition, transformation and transmission along the supply chain. Finally, the system was evaluated and optimized in the sampled supply chain. The results show that the major benefits gained from the RFID-enabled traceability system are the real-time and accurate data acquisition and transmission, and the high efficiency of information tracking and tracing across the cattle/beef supply chain; the main barriers for implementing the system are the inapplicable method of inputting information, the inefficient sequence of data input and communication mechanism associated with RFID reader, and the high implementation cost.

Variable-rate spraying technology can change the application rate by the target information. And the tree canopy volume was a very important target information. An ultrasonic measurement method of canopy volume was presented, and an ultrasonic measurement system of canopy volume was developed. The average of Wheaton and Albrigo manual measurement values was defined as the manual measurement values in this paper.150 peach trees having different canopy volumes were selected to as the experimental objects. Experiment results indicated that the ultrasonic measurement system had high stability (R2=0.968, Std. Error of the Estimate was 0.061719), the manual measurement method had high stability(R2=0.973, Std. Error of the Estimate was 0.060214), and the manual measurement values and the ultrasonic measurement values were in a good agreement(R2=0.968, Std. Error of the Estimate was 0.065314), that is the ultrasonic measurement system had high precision.

This paper presents a method to extract color and texture features of an image quickly for content-based image retrieval (CBIR). First, HSV color space is quantified rationally. Color histogram and texture features based on a co-occurrence matrix are extracted to form feature vectors. Then the characteristics of the global color histogram, local color histogram and texture features are compared and analyzed for CBIR. Based on these works, a CBIR system is designed using color and texture fused features by constructing weights of feature vectors. The relevant retrieval experiments show that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results.

Swine-Vet is a kind of a web-based expert system for swine disease diagnosis and has been promoted in north of China past 3years. The few issues relevant with low diagnosis efficiency and error have arisen during the system test and promotion due to the limitation of previous adopted reasoning techniques and immaturity of diagnosis domain knowledge. This paper proposes a hybrid multimodal reasoning mechanism to resolve the swine intercurrent diseases and improve the system diagnosis efficiency. The framework consists of two modules: one is the forward diagnosing module, information fusion method based on evidence theory is used to solve the barriers of syndrome in swine diagnosis field, and the other is the backward confirming module, set pair analysis (SPA) is adopted to resolve evidence conflict and distinguish uncertain and unknown symptom information during the process of diagnosis. Then, in order to demonstrate this model, a case study is given. Finally, the benefits and challenges of the hybrid multimodal reasoning application in expert system are discussed.

Traceability system can effectively trace food quality and reduce safety scares. Recent researches on traceability systems in China focus on technology innovation, traceability system management, and determinants of traceability system implementation. This paper proposes four criteria to analyze strengths and limitations of the operating mechanisms. The result shows the operating mechanism of traceability system in Chinese agribusiness can be classified into three categories and none of operating mechanism of traceability system completely meets the agribusiness traceability service requirement, and each of the three operating mechanisms has its own strengths and limitations. It is suggested that an integrated mechanism is needed to implement traceability system in agribusiness.

Expert systems (ESs) were introduced more than two decades ago, but their effectiveness and success are still in debate. This paper attempts to make a contribution to the better understanding of ES applications from a knowledge transfer perspective. This paper argues that an ES application is knowledge transfer that uses Information and Communication Technologies (ICT). Underpinned by knowledge transfer theories and through a series of empirical investigations of ES projects in agriculture, this study recognises the importance of human interactions in the ESs implementation process. Based on the evidence collected, a number of key players are examined. They are knowledge provider/sender, knowledge engineer, knowledge extensionist and knowledge recipient. This paper represents a first attempt to acknowledge the role of a knowledge extensionist in the ICT-based knowledge transfer process. The name ‘extensionist’ is borrowed from earlier literature and describes an actor whose role is an intermediary in supporting transferring knowledge towards the knowledge user. Findings demonstrate the significant contributions made by extensionists towards the success of ESs applications. It is argued that the rigidity and limitations of ESs in ICT-based knowledge transfer can be significantly reduced with the involvement of close human interactions with the knowledge recipient.

Because the traditional FTP search engines usually adopt centralised spiders to collect data, insufficient temporal effectiveness is their major demerit. For solving this problem, this paper presents an efficient hierarchical FTP search engine model that deploys the spider agent on the node host of some specific network for collecting file data of FTP servers. The key technologies involve a regional responsibility mechanism, a search mechanism based on the asynchronous retrieval technology and a PAT Tree storage mechanism. The simulation shows that the responding time is less than that of the traditional system and the temporal effectiveness arrives at the application level. In addition, the test results show that the architecture has a good scalability.

Resolving inventory and location problems is the most important and fundamental tactical decision in the initial stages of developing a distribution network design. This is especially true for a logistics system dealing with perishable food. To minimise both the total inventory transportation cost of fresh agri‐products and also waste in the supply chain, the evolution of the food supply chain structure is analysed and an inventory location allocation model is presented describing the real‐life problem. The key issues are to describe food demand distribution and to determine the balance of transportation and inventory cost. To improve the quality of the solution, a local search is embedded in the two‐grade delayed particle swarm optimisation. Experimental results demonstrate the algorithm can effectively resolve conflict between different costs and improve development decisions regarding Beijing's perishable food distribution centres.

This paper presents a research attempt to develop and evaluate a Web-based tele-diagnosis system for fish diseases. Fish disease diagnosis is a complicated process and requires high level of expertise. A tele-diagnosis system T-VET is being developed and tested in China. T-VET is the key sub system of a Web-based expert system for fish disease diagnosis. This paper looks at why a tele-diagnosis system is needed and what are the advantages and difficulties in developing and using such a system. The system's architecture and components are described. A user-centered approach was adopted throughout the development process to ensure the system would meet users' needs and will be accepted. As a result, the system functions were emerged and improved by users' requirements. The experience and feedback from testing and evaluating the system with potential users are discussed. Some valuable insights into the use of the Internet as a tele-diagnosis tool in aquaculture are highlighted.

As one of the widely consumed foods, aquatic products are prone to spoilage and deteriorate in the cold chain. It is very important and necessary to trace and track the aquatic products' quality and safety in the cold chain. This paper aims to develop a quality and safety traceability system integrated with wireless sensor network (WSN) and quick response (QR) code (SMS-CQ) for aquatic products. This paper analyzed temperature fluctuations of aquatic products in cold-chain logistics that is implemented and evaluated in an actual cold chain. The results show that compared with the traditional system, SMS-CQ is an effective quality management tool that leads to real-time monitoring and tracing of the aquatic products in the cold chain: WSN technology enables SMS-CQ to automatically realize the real-time temperature acquisition, wireless remote transmission and monitoring; QR code provides valid means for the QR code generation, error correction and static and sensed dynamic information inquiry. Practical Applications The proposed method could be extended by future researchers or practitioners for future work in many wide areas in cold-chain monitoring and traceability applications.

Aquaculture has moved from conventional open systems to high density and highly productive land-based recirculation systems. Consumers have increased consumption of fish and fish products due to recognition of their nutritional value along with social progress and the improvement of living standards. A traceability system is considered as an effective tool to guarantee safety in fish products and improve the supply chain transparency. This paper developed a Wireless Sensor Network (WSN) based Traceability System for Recirculation Aquaculture (RATS). System tests shows that the WSN-based traceability system has comparable data accuracy and advantage of easy installment and configuration.

The ecological construction for demonstration area is an idealized model for the establishment of rural sustainable development in the county-scale. This paper presents a GIS-based evaluation system of ecological construction for demonstration area. It includes an index system with the 4 secondary indices and 24 factor indices, and a method of ecological construction for demonstration area utilizing multi-attribute decision models of AHPCI method for comprehensive performance assessment. Taking Xinyang city of Henan province in China as a case study, the comprehensive assessment result were found to be coincident with practical situation, so it proves that the GIS-based evaluation system was fit for county-level ecological construction assessment for demonstration area as a beneficial reference framework elsewhere.

This paper identifies the key aspects of perishable food distribution problem in metropolis. A multi-objective model of vehicle routing problem with time window is constructed including fixed vehicle cost, operation cost, shelf life loss and default cost. In order to reduce the increase distribution cost via meeting the time window, two-generation Ant Colony Optimization with ABC customer classification (ABC-ACO) is applied to solve the problem. The computational results show that the ACO with targeted customer classification is 20.8% faster and with 15.9% cost reduction.

Due to the overall complexity of wireless sensor networks, programming sensor networks poses considerable impediments and makes application development nontrivial. A middleware layer is a novel approach to glue together the operating systems, network stacks, and applications. In this paper, a new wireless sensor network middleware for collaborative object tracking has been proposed, which is consisted of three related algorithms: the node selection algorithm, the object management algorithm, and the session maintenance algorithm. The simulation results using ns-2 show that in a wireless sensor network using IEEE 802.15.4 protocol, the system with the proposed middleware has better performance than system without the proposed middleware.

ZigBee technology is designed to be used on applications that require low data rate, low-cost, low power consumption. However, the ZigBee networks are vulnerable to various attacks. In this paper, we have proposed an Identity-based authentication protocol for a ZigBee network structured to have multiple clusters consisting of one coordinator and many devices. Further more, we have designed a secure protocol used for exchanging public parameters between two clusters. Our proposal could realize secure, fast, low-cost and scalable authentication between any two nodes in a clustered ZigBee network.

Knowledge management and retrieval are important parts of an e-Learning system. The retrieval intention is hard to express by traditional keywords based retrieval mode. In this paper, based on analyzing keywords in semantic layer, semantic similarity is calculated to catch the user's retrieval intention. Finally, the proper semantic mapping from keywords to concepts in knowledge base is realized. The semantic retrieval approach in this paper is significant for the individualized e-Learning system construction.

Since ontology was put forward by Neches and Fikes in 1991, the research on ontology is becoming increasingly matured after more than 10 years development. Although usages on concepts and terms related with ontology do not keep completely identical, the agreement of actual use has emerged. Most researches are concentrated on theoretic in current ontology research, while practical application of ontology is still less. Ontology takes the role as a philosophical concept originally, which regarded as an explanation or description to an objective existing system from the philosophy category, and concerns the abstract nature of the objective reality. Later, it is given a new definition by the artificial intelligence category with the development of artificial intelligence. Neches and Fikes (1991) define ontology as the “explicit formal specifications of the terms in the domain and relations among them”. The terms and relations are abstract description of phenomenon happened in the word. Gruber (1993) gives a most popular definition to ontology:”ontology is the clear specification on a conceptual model”. Based on this definition, another new concept of ontology is given by Borst, i.e. (1997) as “ontology is the clear specification on a sharing conceptual model”. Studer (1998) thinks that ontology consists of four layers meaning after an in-depth studying on the two above definitions, which are “conceptualization”, “explicit”, “formal” and “share”. Conceptualization refers to models obtained by abstracting some related phenomenon in the objective world, which performs independent of the specific state of the environment. Explicit refers to explicit definitions to used concepts and their restriction. Formal refers to the ontology model can be processed by computer. Share refers to ontology aims at group consensus instead of individual consensus. The ontology goal is to capture the knowledge of relevant domain, provide a common understanding to the knowledge, confirm vocabularies of the domain and define the relationships between the vocabularies clearly. Some scholars regard ontology as an approach to construct knowledge base. There are different ontology category modes according to different ontology attributes. Gruber (1993) divides ontology into four classes as its level of detail and level of dependence: top level

Dissolved oxygen (DO) concentration plays a very important role in fish life and aquaculture, but DO prediction is very difficult. So a decision support system for DO prediction based on fuzzy model and neural network was attempted. The paper was based on vast monitored data, every day detecting for two years, in aquaculture pond in North China for two years. This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of aquaculture water quality. It proposes a model to be used effectively in prediction of DO concentration in aquaculture. This is really a crucial task, especially during the long dry summer months. The prediction of potential risk due to low DO is also very important. This data volume was divided in the training subset comprising of 106 cases and in the testing subset containing 26 cases. The input parameters are sunlight, wind speed, temperature, water temperature, air pressure, pH value and NH-NH3. Consequently three structural and seven dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one.

In this paper, genetic algorithms and machine learning theory and method of knowledge acquisition for expert system structure is proposed to solve the "bottleneck" problems in the traditional machine learning methods on the basis of AQ, the model of knowledge acquisition based on GA is proposed and its application is used in the Fish Disease Diagnosis reasoning system, the rules is accessed to fish disease diagnosis. Fish Disease Diagnosis and the problems of knowledge accessed into combinatorial optimization are solved.

As China's meat production is growing, the quality safety of meat food as an important human food sources has increasingly become a major issue which related to the national economy and people's livelihood. Using early warning technique, tracking and tracing techniques and so on in the key link of meat food production in order to fulfill quality control is an important method which can effectively guarantee the food quality and safety. The use of RFID techniques and handhelds can automatically collect information about sheep breeding. Meanwhile this information could be inquired conveniently through Internet. This study uses C/S (Client/Server) model structure, and builds the sheep breeding traceability system based on RFID.

Efficient knowledge management is one of the central challenges for the successful construction and following use of expert system. This paper presents a framework of knowledge management in Fish-expert. The main benefit is high improvement and facilitate in the precision. At present the version of fish-expert V2.0 has been successfully used for some areas in north of China. Compared with the first version, the amount of rules had decreased from 1890 to 312 and the reasoning speed and veracity had been promoted from 1 second to 0.5 second and 85% to 95 % or more separately. The result indicates that Fish-expert performance is at least as good as the average of the human experts but can be promoted in the parasite identifies.

A method to express Profile Hidden Markov Model (Profile HMM) parameters with compressing matrix is presented, which is obtained by imposing the characteristics of both the state transfer and the character output in the Profile HMM. Profile HMM, Multiple sequence alignment, Bioinformatics

With the recent rapid progress of computer technology, researchers have attempted to adopt artificial intelligence and computer networks to develop computer-aided tutoring systems. Meanwhile, some have also attempted to develop more effective programs to test and enhance the learning performance of students. This study surveyed the current theoretical basis and design principle and proposed a web-based knowledge aided tutoring system for vegetable supply chain. The main idea of this paper was focused on the interactive design and feedback control. The proposed concept was illustrated by the functions of chat system, discussion section, questionnaires and online learning record, etc.

This paper proposed a new method to acquire vegetable supply chain domain knowledge from three semantic extension layers. For a single domain concept, qualitative reasoning and quantitative reasoning are used to reach the first semantic extension layer; for more than two domain concepts, Advanced Similarity Measure (ASM) arithmetic is adopted to reach the second semantic extension layer. These two semantic extension layers are achieved internal of vegetable supply chain ontology model. WordNet and Locally Linear Embedding (LLE) arithmetic are adopted to reach the third semantic extension layer, which is achieved external of the ontology model. The experiments have proven the efficiency of this method. The method can be expanded to domain concepts acquisition of other fields.

The paper presents a method of assessment on e-learning's influence to the learners' mental model and the effectiveness of the learning performance. The result shows that e-learning has been proved to positively influence learners' cognitive perception and structuring, while the psychological process of learning as a whole is positively influenced by these technological features.

In this paper, a kind of 3-layer index inverted table is proposed, which differs from the usual one for the introduction of the additional layer for semantic items. We take advantage of some semantic markups and convert the content of documents to a set of ontology terms. The index tables for search engine obtains those converted terms, which match with the terms from the query, in that the precision of matching can be improved effectively.

Knowledge representation and organization takes an important role in an e-learning system. In this paper, we put forward a metadata model for agriculture e-commerce knowledge management on the base of our investigation. The metadata model contains three classes which are personnel information class, agriculture e-commerce knowledge class and agriculture e-commerce domain class. Furthermore, we also gave the subclasses of these three classes. Then these three classes and their subclasses are integrated together through the description of their relationships. Finally the metadata model is formalized by resource description framework. And the metadata model is proven to be useful in our knowledge searching system.

This paper offers a qualitative study on the process of knowledge transfer that focuses on the expert system development. Inter-disciplinary approach is applied to review, analyse and develop a framework for the process. A framework of knowledge transfer process is proposed as having: (1) interactive transfer process: knowledge acquisition, representation, assimilation and dissemination; and (2) a dynamic knowledge conversion and creation process for the explicit and tacit knowledge. A case study demonstrating the development of a group of aquaculture disease diagnosis systems is empirically studied for the verification of the process. As a result of the study's findings, the framework is proposed with its validity. The related implications and limitations of the study are also given.

Ant colony optimization based algorithm for vehicle routing problem with time windows (VRPTW) is presented. To solve it, a two-generation (father and children) ant colony algorithm is proposed. The aim of the first children generation is construction of the sub-tours, whereas in the second father generation the sub-tours are composed of feasible solutions. The computational results show that the two-generation ant colony algorithm is effective in solving multi-objective VRPTW.

Fish disease diagnosis is a complicated process and requires high level of expertise, an expert system for fish disease diagnosis is considered as an effective tool to help fish farmers. However, many farmers have no computers and are not able to access the Internet. Telephone and mobile uses increase rapidly, so, the provision of call centre service appears as a sound alternative support channel for farmer to acquire counseling and support. This paper presents a research attempt to develop and evaluate a call center oriented Hybrid disease diagnosis & consulting system (H-Vet) in aquaculture in China. This paper looks at why H-Vet is needed and what are the advantages and difficulties in the developing and using such a system. A machine learning approach is adopted, which helps to acquire knowledge when enhancing expert systems with the user information collected through call center. This paper also proposes a fuzzy Group Support Systems (GSS) framework for acquiring knowledge from individual expert and aggregating knowledge into workgroup knowledge by H-Vet in the situation of difficult disease diagnosis. The system’s architecture and components are described.

There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge's representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic cases.