Traceability Model Research Articles

Enabling Traceability in Agri-Food Supply Chains Using an Ontological Approach

Abstract Traceability of food products to their sources is critical for quick responses to food emergencies. US law now requires stakeholders in the agri-food supply chain to support traceability by tracking food materials they acquire and sell. However, having the complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. There are multiple reasons that food traceability is a challenging task, including diversity of stakeholders and their lexicons, standards, tools, and methods; unwillingness to expose information about internal operations; lack of a common understanding of the steps in a supply chain; and incompleteness of data. The objective of this work is to address the traceability challenge by developing a formal ontology that can provide a shared and common understanding of the traceability model across all stakeholders in bulk food supply chains. A formal ontology can support semantic mediation, data integration, and data exploration, thus improving the intelligence and reliability of trace and track process. The Industrial Ontologies Foundry (IOF) procedures and principles are employed in the development of the supply chain traceability ontology. Basic Formal Ontology (BFO) is selected as the top-level ontology. A bottom-up approach is also adopted in a sense that a real use case related to the bulk grain domain is selected to be used for requirements definition and ontology validation. A software tool for visualization of the traceability graph is developed to validate the developed ontology based on simulated data. The test and validation results indicate that the developed ontology has the expressivity needed to represent the semantics of traceability data models within the scope of the selected use case. Also, it was observed that the developed supply chain traceability tool can effectively facilitate the track and trace process through visualizing the Resource Description Framework (RDF) triples, thus eliminating the need to formulate complex SPARQL queries for information retrievals.

Analysis of Cross-Border E-Commerce Commodities in Internet of Things Based on Semantic Traceability Algorithm

With the advancement of economic globalization and the continuous development of “Internet +” economy, cross-border e-commerce has become an important part of participating in international economic exchanges and developing foreign trade. As the direct participation of consumers, cross-border online shopping has gradually become an important way for people to obtain overseas commodities. The phenomenon of fake products and parallel goods is serious in e-commerce online shopping. How to use the existing Internet of Things to achieve product traceability is an important research topic. For different modes of cross-border online shopping, this paper combines the traceability algorithm with the idea of semantic routing, proposes a Semantic Web-based traceability algorithm, and constructs a quality safety traceability system. In order for consumers to accurately know the product quality of sellers, the product traceability model of cross-border e-commerce relying on Semantic Web is constructed to prevent false information from recording product information from the product source. Through the tracing query algorithm, the simulation results show that the hop number of routes between nodes is 1, and the tracing algorithm can quickly determine the identity of online goods. The network delay is mostly distributed between 0 and 0.05 s, which meets the real-time requirements of the Internet of Things.

A Conceptual Model for Blockchain-Based Agriculture Food Supply Chain System

In agriculture supply chain management, traceability is a crucial aspect to ensure food safety for increasing customer loyalty and satisfaction. Lack of quality assurance in centralized data storage makes us move towards a new approach based on a decentralized system in which transparency and quality assurance is guaranteed throughout the supply chain from producer to consumer. The current supply chain model has some disadvantages like a communication gap between the entities of the supply chain and no information about the travel history and origin of the product. The use of technology improves the communication and relation between various farmers and stakeholders. Blockchain technology acquires transparency and traceability in the supply chain, provides transaction records traceability, and enhances security for the whole supply chain. In this paper, we present a blockchain-based, fully decentralized traceability model that ensures the integrity and transparency of the system. This new model eliminated most of the disadvantages of the traditional supply chain. For the coordination of all transactions in the supply chain, we proposed a decentralized supply chain model along with a smart contract.

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)

BackgroundLarge uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.ResultsResults showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.ConclusionsThe output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty.

C-LLAMA 1.0: a traceable model for food, agriculture, and land use

Abstract. We present C-LLAMA 1.0 (Country-level Land Availability Model for Agriculture), a statistical–empirical model of the global food and agriculture system. C-LLAMA uses simplistic and highly traceable methods to provide an open and transparent approach to modelling the sensitivity of future agricultural land use to drivers such as diet, crop yields, and food-system efficiency. C-LLAMA uses publicly available FAOSTAT food supply, food production, and crop yield data to make linear projections of diet, food-system, and agricultural efficiencies, as well as land use at a national level, aiming to capture aspects of food systems in both developing and developed nations. In this paper we describe the structure and processes within the model, outline an anchor scenario, and perform sensitivity analyses of key components. The model land use output behaves as anticipated during sensitivity tests and under a scenario with a prescribed reduction in animal product consumption, in which land use for agriculture is reduced by 1.8 Gha in 2050 when compared with the anchor scenario.

Modeling Product Traceability Process Based On Petri Nets

Around the world, more and more attention has been paid to product traceability, and many different traceability models have been established. In response to the existing traceability models with low universality, poor scalability, and incomplete traceability, this paper analyzes and integrates processes in the supply chain, simplifying it by solving unclear hierarchy and high complexity problems. Then three essential process model elements are abstracted using Petri nets, which can build a traceability model applicable to most products and processes. In order to verify the availability of the model, an example of the Petri net traceability model is constructed. The incidence matrix analysis method is used to analyze the characteristics of the model, and the Petri net modeling and simulation tool PIPE is used to verify the properties and simulation. The results of the two methods prove that the model is practical and feasible.

Track and Trace: Integrating static and dynamic data in a hybrid graph-based traceability model

In smart manufacturing, an increasing amount of heterogeneous data inputs need to be integrated into a holistic traceability model. One key challenge is the coherent mapping of dynamic and event-based sensor data for real-time tracking purposes with static data, which are mostly needed for retrospective tracing purposes. In this research, we develop a graph-based traceability model, which integrates static and dynamic data sub-models in one connected traceability graph. For an automotive use case, the novel traceability model is implemented and tested using a hybrid architecture based on the graph database Neo4j and the event engine Apache Kafka.

Tooled approach to interoperability monitoring with traceability for message-driven systems

Message-driven information systems allow for asynchronous data exchange of messages via a publish/subscribe mechanism. This is achieved by setting up data exchanges in form of a message-oriented-middleware (MOM) between the collaborating systems. Traceability provides a method for the persistence of the message intermediate states across the message lifecycle allowing for retransmission. This research proposes a model for traceability and metrics generation, not only from the message broker but from the producer and consumer clients interacting via HTTP. A messaging metamodel is proposed for aggregating interaction metadata. We develop this model using RabbitMQ an open-source message broker and Spring Framework enhanced to include traceability interfaces. A web application-compatible tool was designed, prototyped, and implemented based on this metamodel. This approach helps not only for monitoring interactions but for the detection of potential interoperability-related issues and improving overall message throughput. A case study was used to demonstrate the feasibility of this approach.

Research on reverse identification algorithm of indoor pollution source based on machine learning

Indoor sudden pollutant leakage brings environmental pollution and occupational exposure, so it is more and more important to obtain the location and identification of leakage sources. Through the forward method based on machine learning, this paper establishes a reverse traceability model for indoor multiple pollution sources. The POD method is used to obtain a large number of intermediate working condition data. The data pre-processing strategy of first normalization and then random forest feature screening can effectively improve the accuracy and generalization ability of the model. Based on a real environmental room case, model verification and sensor deployment optimization are carried out. The results show that the four sensors deployed in a specific location can achieve more than 95% positioning accuracy. In addition, the leakage possibility ranking component embedded in the model can effectively guide the staff to check the leakage points in turn, and the efficiency of three checks is as high as 99.91%.

Intelligent Agriculture Technology Based on Internet of Things

Although the application of agricultural product traceability technology is a key point to realize Modern Agricultural IoT, it has still encountered various food safety problems. For example, immature environmental monitoring technology of agricultural products, weak product traceability and imperfect product monitoring equipment. For this reason, this paper studies and compares several emerging technologies of the things Internet, then it analyzes the functional diversity and practicability of the Modern Agricultural IoT. It builds the experimental environment based on the agricultural product traceability technology, so as to realize the monitoring of crop growth environment and traceability. The result plays a positive role in popularizing the traceability technology of agricultural products and has a certain impact on the development of intelligent agriculture. It has designed and developed a set of crop growth monitoring terminal equipment through experimental debugging. Then it could obtain temperature and humidity, illumination, CO2 concentration, soil data, and other crop growth environment parameters in real time. The relevant data has provided strong support for the traceability model to realize the multi-point monitoring, intelligent control, and automatic operation of crop growing environment. Then it can be sorted and analyzed through the application layer. This system can promote the intelligent processing, improve the utilization efficiency of agricultural resources, promote the development of modern agriculture, save agricultural production costs, and increase the production and marketing. It is an innovative application of the Internet of things technology applied to agricultural production.

Research on financial information disclosure risk management method based on internet of things

In order to overcome the problem that the traditional risk management method has difficulty achieving the expected effect due to the careless classification of risk levels, this paper designs the financial information disclosure risk management method based on the internet of things. Based on the network financial information disclosure traceability model, the risk assessment index system is built, and the risk events are classified by ranking the risk events. The evaluation result is divided into five categories and the risk warning level is set. Then, the wireless sensor technology in the internet of things is used to design data fusion algorithm and distributed routing algorithm to achieve risk management. Simulation results show that the recall rate of the risk assessment index is above 94%, and the time to issue risk warning is always less than 6 min, which fully proves the effectiveness of the method.

Toward a Formal Traceability Model for Efficient Security Validation

A formal traceability model is presented with a real-world application of a gray-box penetration test. Such formal traceability improves test effectiveness and efficiency, thus underlining the applicability and value of formal methods in an industry context.

Traceability model system as originality assurance method for honey product, Indonesia

Honey is a hype product as immunity booster in the health shock covid-19. Unbalance of supply and demand has raised the price and made the consumer could not afford it. Besides that, global issue about unoriginal honey which spreads in society has made the consumer has negative stigma on SMEs honey product because it does not have complete food safety certification as commercial company. Objective of the research was to increase the consumer trust through originality assurance for honey product from traceability system. The research is a case study with FGD method and then followed by Interpretive Structural Modeling (ISM) analysis and Business Process Notation as solution. Result of the research showed that the distribution aspect has great power to affect the stakeholder and logistic aspects, due to success of both aspects are determined by distribution aspect. While the stakeholder aspect does not have any power to affect other aspect because if the business system has correct SOP, then both aspects will have appropriate supply chain management. Therefore, the solution is business process improvisation, so that to increase the trust local brand for honey product, it must be optimized through recording system of customer journey, so that the producer could trace the barrier and enabler factors about perspective of the honey originality

A Proposed of Halal Food Supply Chain Traceability Model

The rise in Halal Food Supply Chain (HFSC) issues and the ongoing 2019 global coronavirus disease (COVID-19) pandemic accelerate the need for HFSC process compliance and the adoption of technologies to validate and verify the authenticity and integrity of the HFSC activity. However, the issues and factors in HFSC traceability in this context have yet to be specifically published in Malaysia. The absence of this study suggests an impending need for a study to be conducted to find the issues of HFSC traceability. Thus, this study aims to propose the Halal Food Supply Chain Traceability Model by identifying the factors for HFSC traceability. The halal food industry was considered as one of the case examples in the study. This study was undertaken using content analysis by identifying the issues and factors for HFSC traceability from previous researchers' twenty-three (23) studies. The study identified the contributing factors to HFSC traceability and proposed the HFSC traceability model. The benefits of knowledge gained could help the clarity and expectations of the solutions to the HFSC industry and as references or basic guidelines on HFSC traceability.

APTM: A Model for Pervasive Traceability of Agrochemicals

As the world population increases and the need for food monoculture farms are using more and more agrochemicals, there is also an increase in the possibility of theft, misuse, environmental damage, piracy of products, and health problems. This article addresses these issues by introducing the agrochemical pervasive traceability model (APTM), which integrates machine learning, sensors, microcontrollers, gamification, and two blockchains. It contributes in two dimensions: (I) the study of the environmental, product piracy and regulatory of agrochemical control; (II) the technological dimension: application of an adequate set of sensors collecting multiple data; modeling and implementation of a system via machine learning for analyzing and predicting the behavior and use of agrochemicals; development of a scoring system via gamification for reverse use of agrochemicals; and presenting a record of transactions in a consortium of two blockchains, simultaneously. Its main advantage is to be a flexible, adaptable, and expansive model. Results indicated that the model has positive aspects, from detecting the agrochemical, its handling, and disposal, recording of transactions, and data visualization along the reverse supply chain. This study obtained a round trip time of 0.510 ms on average; data transfers between layer one and its persistence in the database were between 4 to 5 s. Thus, blockchain nodes consumed only 34 to 38% of CPU and recorded transactions between 2 to 4 s. These results point to a horizon of applicability in real situations within agricultural farms.

Membrane Targeting of C2GAP1 Enables Dictyostelium discoideum to Sense Chemoattractant Gradient at a Higher Concentration Range.

Chemotaxis, which is G protein-coupled receptor (GPCR)-mediated directional cell migration, plays pivotal roles in diverse human diseases, including recruitment of leukocytes to inflammation sites and metastasis of cancer. It is still not fully understood how eukaryotes sense and chemotax in response to chemoattractants with an enormous concentration range. A genetically traceable model organism, Dictyostelium discoideum, is the best-studied organism for GPCR-mediated chemotaxis. Recently, we have shown that C2GAP1 controls G protein coupled receptor-mediated Ras adaptation and chemotaxis. Here, we investigated the molecular mechanism and the biological function of C2GAP1 membrane targeting for chemotaxis. We show that calcium and phospholipids on the plasma membrane play critical roles in membrane targeting of C2GAP1. Cells lacking C2GAP1 (c2gapA–) displayed an improved chemotaxis in response to chemoattractant gradients at subsensitive or low concentrations (<100 nM), while exhibiting impaired chemotaxis in response to gradients at high concentrations (>1 μM). Taken together, our results demonstrate that the membrane targeting of C2GAP1 enables Dictyostelium to sense chemoattractant gradients at a higher concentration range. This mechanism is likely an evolutionarily conserved molecular mechanism of Ras regulation in the adaptation and chemotaxis of eukaryotes.

A preclinical pipeline to evaluate migrastatics as therapeutic agents in metastatic melanoma

BackgroundMetastasis is a hallmark of cancer and responsible for most cancer deaths. Migrastatics were defined as drugs interfering with all modes of cancer cell invasion and thus cancers’ ability to metastasise. First anti-metastatic treatments have recently been approved.MethodsWe used bioinformatic analyses of publicly available melanoma databases. Experimentally, we performed in vitro target validation (including 2.5D cell morphology analysis and mass spectrometric analysis of RhoA binding partners), developed a new traceable spontaneously metastasising murine melanoma model for in vivo validation, and employed histology (haematoxylin/eosin and phospho-myosin II staining) to confirm drug action in harvested tumour tissues.ResultsUnbiased and targeted bioinformatic analyses identified the Rho kinase (ROCK)-myosin II pathway and its various components as potentially relevant targets in melanoma. In vitro validation demonstrated redundancy of several RhoGEFs upstream of RhoA and confirmed ROCK as a druggable target downstream of RhoA. The anti-metastatic effects of two ROCK inhibitors were demonstrated through in vivo melanoma metastasis tracking and inhibitor effects also confirmed ex vivo by digital pathology.ConclusionsWe proposed a migrastatic drug development pipeline. As part of the pipeline, we provide a new traceable spontaneous melanoma metastasis model for in vivo quantification of metastasis and anti-metastatic effects by non-invasive imaging.

Traceability System on Mangosteen Supply Chain Management Using Blockchain Technology: A Model Design

This study aims to recommend a model of traceability system in mangosteen supply chain management using blockchain technology. The traceability system model is designed to ensure the acceptance of safe and quality mangosteen by consumers because consumers can search for information about mangosteen supply chain management. In this study, blockchain technology was chosen because with blockchain technology, traceability and capacity to share information about production processes will be made easier and trustworthy. Traceability takes center stage in organizations supply chain; furthermore, it is a tool in fighting product counterfeiting and protecting brands. Implementing blockchain can revolutionize the way a supply chain works.This research consists of three stages. The first is to identify the mangosteen export supply chain. Second, identify the form of data needed and used in mangosteen traceability. Third, the formulation of mangosteen traceability models or designs. The results of this study describe a model design how mangosteen traceability is carried out using blockchain technology.

Data continuity and traceability in complex manufacturing systems: a graph-based modeling approach

ABSTRACT Modern industrial production systems are facing the challenge of interconnecting and exploiting the increasing amount of data from smart products, software systems, and IIoT devices. One of the main tasks in this context is the implementation of a data-continuous traceability system, which interconnects the heterogeneous data objects over the entire production flow. While traceability maturity is already high in regulated and process-driven industries, the established traceability solutions cannot be successfully applied to customized and volatile industries. In this research, a graph-based traceability modeling methodology is presented that allows the systematic development of data continuity and integration in manufacturing. Based on the traceability methodology, the graph data model and architecture are specified, which are aligned to the requirements of customized and complex manufacturing systems. The model is implemented and validated based on a Neo4j graph database for the use case of the manufacturing process of automotive electrical systems. This research overcomes the shortcomings of state-of-the-art traceability models by shifting the focus to the relationships between traceability-relevant data objects. The proposed graph-based traceability model is able to capture multi-hierarchical product structures and is less dependent on physical object identification, making it more applicable to customized and complex manufacturing.

Tracing the Origins of Blueberries by Their Mineral Element Contents and 87Sr/86Sr Ratios.

This study investigated the feasibility of using the mineral element contents and 87Sr/86Sr ratios of blueberries to trace their origins. The contents of 28 mineral elements, including K, Al, and Mg, were determined in 104 blueberry samples from three blueberry-producing areas in Guizhou Province, China. Also determined were both the 87Sr/86Sr ratios in 48 blueberry samples as well as the type of soil in which the blueberries were grown. Cluster analysis of 87Sr/86Sr ratios, stepwise discriminant analysis of mineral element contents, and combined discriminant analyses of 87Sr/86Sr ratios and mineral element contents were done. The results show that ten elements (Ca, Cr, Cs, Mg, Mn, P, Rb, Sb, Th, and Y) were selected by linear discriminant analysis, which could be used to establish the provenance traceability model of blueberries in Guizhou. The original accuracy of linear discriminant analysis was 89.4%, and the accuracy of cross-validation was 83.6%. When 87Sr/86Sr ratios were used for tracing, the accuracies of both the original discrimination and the cross-validation were 81.3% as determined by linear discriminant analysis, and the accuracy rate of origin discrimination was 81.25% by cluster analysis. The results of combined discrimination were the best: the accuracy of the original discrimination was 95.8%, and the accuracy of cross-validation was 93.8%. Mineral element contents and 87Sr/86Sr ratios can be used to trace the origins of blueberries, and combining them can significantly improve the discrimination accuracy. Fisher linear discriminant analysis had the greatest accuracy followed by cluster analysis, while principal component analysis was relatively poor in the research of Guizhou blueberry origin traceability.