An improved ISM method based on GRA for hierarchical analyzing the influencing factors of food safety

Research on Risk Prediction of Condiments Based on Gray Correlation Analysis - Deep Neural Networks

Food safety is directly related to the health of the public, and the safety of condiments is also of great significance. In this study, a risk assessment model for condiments based on gray correlation analysis was established by using publicly available sampling data of soy sauce and vinegar in China. Risk indicator screening and data preprocessing were performed first, and the weight of each indicator was calculated by gray correlation analysis to formulate a comprehensive risk value label. Then, three machine learning models, Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), were used to predict the comprehensive risk values. Finally, the fuzzy synthesis analysis was utilized to classify the risk level of the composite risk value. In this study, based on the analysis of 282 sets of soy sauce and 704 sets of vinegar samples, the trained DNN model has the optimal prediction performance, which can basically predict the comprehensive risk value and risk level of a sample by inputting the detection indexes of that sample. This method can provide a more reasonable basis for relevant departments to formulate risk control strategies.

A Study of the Developmental Mechanisms of Inter-Team Conflict Processes Within Multi-Team Systems - An Exploratory Analysis Based on a Collaborative R&D Context

Purpose

The analysis of the pivotal determinants that impact the progression of inter-team conflict processes in multi-team systems, as well as their underlying mechanisms, serves to explicate the developmental framework of said conflict processes.

Methodology

This study adopts a vantage point centered on the evolution of inter-team conflict in multi-team systems, with a specific focus on the sequential progression including "conflict latency → conflict perception → conflict management → conflict outcome → conflict feedback.

Results

By transmuting qualitative data into quantitative data through the discernment of inter-conceptual relationships' directionality and quantity, this study distills the key chain of relationships between categories. Employing the explanatory structure model, the developmental mechanism of inter-team conflict processes in multi-team systems is unveiled. Notable sources of conflict include team goal identification, team role multiplicity, inter-team relationships, and team competence. Factors that exert a significant influence on conflict management comprise inter-team conflict types, inter-team relationships, team competence, inter-team heterogeneity, team affiliation, and system goals. Reviewing the genuine motivations underlying conflict management behavior, as well as adopting a lengthier temporal perspective, emerges as a crucial consideration when analyzing the implications of conflict management on both the system and the team for evaluative purposes. Inter-team communication emerges as a pivotal influence on the efficacy of conflict management, which, in turn, is influenced by boundary managers, inter-team heterogeneity, and the inter-team interactive memory system.

Conclusion

Through an in-depth analysis of the hierarchical interrelationships among factors that influence conflicts within teams, we have established a model for the conflict development process. This model is instrumental in comprehensively understanding the dynamics of conflict evolution within teams. It serves as a reference point for formulating more precise and effective conflict management strategies. Moreover, this model not only offers practical guidance for resolving conflicts within a multi-team framework but also enhances inter-team collaboration. Therefore, it contributes significantly to achieving the objectives of the multi-team system.

Microbial production of vitamin B5: current status and prospects

Vitamin B5, also called D-pantothenic acid (D-PA), is a necessary micronutrient that plays an essential role in maintaining the physiological function of an organism. It is widely used in: food, medicine, feed, cosmetics, and other fields. Currently, the production of D-PA in industry heavily relies on chemical processes and enzymatic catalysis. With an increasing demand on the market, replacing chemical-based production of D-PA with microbial fermentation utilizing renewable resources is necessary. In this review, the physiological role and applications of D-PA were firstly introduced, after which the biosynthesis pathways and enzymes will be summarized. Subsequently, a series of cell factory development strategies for excessive D-PA production are analyzed and discussed. Finally, the prospect of microbial production of D-PA production has been prospected.

Prediction and Visual Analysis of Food Safety Risk Based on TabNet-GRA

Food safety risk prediction is crucial for timely hazard detection and effective control. This study proposes a novel risk prediction method for food safety called TabNet-GRA, which combines a specialized deep learning architecture for tabular data (TabNet) with a grey relational analysis (GRA) to predict food safety risk. Initially, this study employed a GRA to derive comprehensive risk values from fused detection data. Subsequently, a food safety risk prediction model was constructed based on TabNet, and training was performed using the detection data as inputs and the comprehensive risk values calculated via the GRA as the expected outputs. Comparative experiments with six typical models demonstrated the superior fitting ability of the TabNet-based prediction model. Moreover, a food safety risk prediction and visualization system (FSRvis system) was designed and implemented based on TabNet-GRA to facilitate risk prediction and visual analysis. A case study in which our method was applied to a dataset of cooked meat products from a Chinese province further validated the effectiveness of the TabNet-GRA method and the FSRvis system. The method can be applied to targeted risk assessment, hazard identification, and early warning systems to strengthen decision making and safeguard public health by proactively addressing food safety risks.

Security risk assessment and visualization study of key nodes of sea lanes: case studies on the Tsugaru Strait and the Makassar Strait

Key nodes of sea lanes are important hubs for global trade and cargo transportation and play important roles in ensuring the safety of maritime transportation and maintaining the stability of the global supply chain. The safety guarantee of key nodes of sea lanes is facing more risks and higher requirements currently because the global shipping industry is gradually recovering. This paper focuses on key nodes of sea lanes, conducting regional security risk assessment and risk spatial scale visualization. A set of security risk assessment and visualization study methods for key nodes of sea lanes is constructed, which includes constructing a security risk assessment index system of key nodes of sea lanes with 25 indicators selected from three risk categories (hazard, vulnerability and exposure, and mitigation capacity) and using geospatial analysis to form the multi-criteria spatial mapping layers and then creating comprehensive risk layers to realize the risk visualization in the strait area by weighted overlaying based on the combined weights calculated by Analytic Hierarchy Process and Grey Relational Analysis. After taking the Tsugaru Strait and Makassar Strait as case studies, the results show that the comprehensive risk layers can effectively present the spatial distribution of security risks of key nodes of sea lanes, reflecting the spatial changes of risk levels (i.e., very low, low, medium, high and very high) and the methods can precisely identify and analyze crucial factors affecting the security risk of key nodes. These findings may strengthen the risk prevention and improve the safety of the navigation environment in the strait to ensure the safety and stability of maritime trade.

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

Nitrogen Fertilizer Levels Affect the Growth and Quality Parameters of Astragalus mongolica

Owing to overexploitation, wild resources of Astragalus mongolica, a Chinese herbal plant that is widely distributed in the arid and semi-arid areas of Northern China, have gradually become exhausted, and therefore, commercial cultivation is increasingly important to meet the growing demand for astragalus and reduce the pressure on wild populations. Nitrogen level is an important factor that affects the yield and quality of A. mongolica. However, uniform standards for fertilization among production areas have not yet been determined. In this study, the effect of nitrogen fertilizer treatment on the yield and quality of A. mongolica in the Qinghai-Tibet Plateau was explored using a control treatment (no added nitrogen, N0) and five different nutrient levels: 37.5 kg/ha (N1), 75 kg/ha (N2), 112.5 kg/ha (N3), 150 kg/ha (N4), and 187.5 kg/ha (N5). According to grey relational analysis, the optimal nitrogen fertilizer treatment was the N4 level followed by the N5 and N2 levels. Nitrogen fertilizer significantly increased the root biomass, plant height, root length, and root diameter. However, nitrogen fertilization had no significant effect on the content of Astragaloside IV and mullein isoflavone glucoside. The content of ononin and calycosin continually accumulated throughout the growing period. The results showed that the ononin and calycosin content under N4 and N2 is higher than other levels and there is not significantly different between different nitrogen fertilizer levels about them. The content of formononetin decreased gradually with the progression of the growing season. The optimal nitrogen fertilizer treatment for A. mongolica is recommended to be 150 kg/ha and the content of active compounds and yield were observed to reach the maximum in October.

Environmental influences on quality features of Oviductus Ranae in the Changbai Mountains

This work studied the influences of environmental factors on the quality features of Oviductus Ranae. Oviductus Ranae is mainly produced in the Changbai Mountains. The samples of Oviductus Ranae were collected from 24 different locations, which covered the main producing areas. The environmental parameters were assessed using a digital raingauge, GPS, a thermometer, and an atmospheric pressure-altimeter. The quality features including expansion degree, ethanol extract, total water, total ash, and five steroid components, of the collected Oviductus Ranae samples were quantified using high-performance liquid chromatography. The results showed that the cholesterol content in the samples collected from the Yanbian Korean region was slightly higher than the others. Samples collected from the Huadian area exhibited much higher contents of 7-hydroxycholesterol and 7-dehydrocholesterol than the rest of the producing areas. The highest content of cholest-4-en-3-one came from the samples collected from Dandong. The contents of 7-keto-cholesterol in samples from different regions were very close. The highest ethanol extract was from the samples in Tonghua. The correlations between the quality features and environmental factors were analyzed by SPSS (version 25.0, SPSS Inc., Chicago, IL, USA). The results showed that the content of cholest-4-en-3-one was related to the annual average temperature. The total water was correlated with the annual precipitation. 7-Hydroxycholesterol and expansion degree were related to the altitude. The results indicated that environmental factors have certain influences on the quality features.

Using the FAHP, ISM, and MICMAC Approaches to Study the Sustainability Influencing Factors of the Last Mile Delivery of Rural E-Commerce Logistics

The development of rural e-commerce has rapidly driven the development of rural logistics in China. Improving the service quality of the last mile delivery is an important measure to promote the sustainable development of rural e-commerce logistics. However, such work is challenging because the current rural last mile delivery is inefficient and unsustainable and is influenced by a set of interacting factors. It is necessary to explore the relationships among the sustainability influencing factors of rural last mile delivery. A total of 15 sustainability influencing factors are selected. The improved fuzzy analytic hierarchy process (FAHP) is used to assign the weights of the factors and then the interpretative structural model (ISM) is used to determine the hierarchical structure of each factor. The driving force-dependency quadrant graph is constructed by cross-impact matrix multiplication (MICMAC). The research results show that four factors, including “convenience of returning goods”, “integrity of goods”, “advance reservation of goods pickup”, and “delivery costs”, are the most basic factors affecting the sustainability of rural last mile delivery and are the deepest and most indispensable factors. This research provides valuable information for decision makers to develop proactive strategies and reinforcement policies to improve the service quality of rural last mile delivery, which could promote the sustainable development of rural logistics.

Identification of Key Influencing Factors of Sustainable Development for Traditional Power Generation Groups in a Market by Applying an Extended MCDM Model

With the deepening reform of the power market, the external environment of China’s power industry is going through a huge change. China’s traditional power generation groups (TPGGs), with assets all over the country, are, due to a lack of market awareness about energy policies, facing serious challenges in developing competitive advantages, improving power transaction modes, optimizing profit models, and even realizing basic corporate strategies. In this study, we focus on identifying the key factors influencing sustainable development in an unprecedented market environment for TPGGs, so as to achieve overall sustainable development for the whole power generation sector in China. A hybrid framework based on Multiple-Criteria Decision-Making (MCDM) was proposed to recognize the key influencing factors under vague rule conditions. We developed a novel method combining three different MCDM methods with triangular fuzzy numbers (TFNs), fuzzy Delphi, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Analytic Network Process (ANP), to cover uncertainty and make the problem-solving approach closer to the actual problem. A series of analyses indicate that the final 14 factors covering the five dimensions are considered to be important factors in the sustainable development of TPGGs. Based on the results, it can be said that “Gross energy margin” and “Pricing bidding strategy” dominate the impacts of TPGG’s sustainable development. Finally, we give some advice relating to practical measures to help TPGGs achieve sustainable development in the market-oriented industry environment.