The improvement strategy of fresh produce supply chain resilience based on extenics.

Nowadays, the world is in turmoil, climate and environmental problems are prominent, the import and export of fresh agricultural products are restricted, etc. The impact of the growing demand for fresh agricultural products and healthy lifestyle choices, and fresh agricultural products are essential for people's daily life, which are perishable, fragile, seasonal, and other unstable factors. Therefore, when the fresh produce supply chain faces various pressures and difficulties, how to enhance the resilience of the supply chain against various problems and risks with flexible and multi-dimensional strategies and methods has become the focus of extensive attention. This kind of problem is a typical contradictory problem, and previous studies have failed to achieve good results. In this paper, based on extenics, we are able to one-dimensionalise the multi-dimensional contradictory problems and multi-dimensionalise the one-dimensional contradictory problems to solve such problems in a scientific and effective way. Firstly, taking fresh agricultural products supply chain enterprise M as the research object, we constructed the fresh agricultural products supply chain enterprise toughness system and identified the toughness state of each index. Secondly, we found the low-evaluation toughness indexes that need to be solved and constructed a extension model of incompatible problems of enterprise toughness. Thirdly, we analysed the objectives and conditions of toughness incompatible problems of fresh agricultural products supply chain enterprises numerically and quantitatively, and then, with the objective of toughness improvement, we analyzed the correlation of the condition basic-elements of incompatible problems and carried out extension transformations. Again, the objectives and conditions of the incompatible problems of fresh produce supply chain enterprises are analysed numerically and quantitatively, and with toughness enhancement as the objective, the correlation analysis and extension transformation of the condition basic-elements of the incompatible problems are implemented to generate the set of toughness enhancement strategies that can solve the incompatible problems in a multidimensional and scientific way. Finally, the optimal toughness enhancement strategies are selected through the superiority evaluation and composed into a new strategy to enhance the toughness of the fresh produce supply chain. Combined with extenics calculations and screening, a new strategy for supply chain resilience enhancement of fresh agricultural products was finally formed. The existing problems are solved from six aspects: product supply type, product demand, product supply efficiency, human resource quantity, production and processing equipment, and logistics guarantee ability. It provides a certain reference significance for the fresh agricultural products supply chain toughness enhancement, and helps enterprises to strengthen their competitiveness and sustainability through the enhancement of toughness.

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model.

How to evaluate the resilience level and change trend of supply chain is an important research direction in current supply chain management practice. This paper proposes a new method of supply chain resilience assessment based on hesitant fuzzy linguistic term set (HFLTS) and matter element extension theory. Firstly, based on the research status quo at home and abroad, a low-carbon enterprise supply chain resilience assessment index system is established, which includes six first-level indicators and corresponding 21 second-level indicators of product supply resilience, resource resilience, partner resilience, information response resilience, financial resilience and knowledge resilience. Secondly, HFLTS was used to collect expert opinions and Ordered Weighted Arithmetic (OWA) to calculate the expert composite language, by which the fuzzy evaluation matrix of supply chain resilience assessment indicators was obtained. Once again, the resilience indicator weights are determined based on a game-theoretic portfolio assignment method combining the best-worst method (BWM) and the CRITIC method. Finally, the nearness degree function is combined with the extension comprehensive evaluation method to improve the matter element extension model, and the supply chain resilience assessment model of low-carbon enterprises based on the game theory combination assignment-improved matter element extension is established. Taking X low-carbon enterprise as an example, the evaluation results show that the supply chain resilience level of this enterprise is II, and the eigenvalue of the grade variable is 2.69, and the supply chain resilience is shifting to III, and the supply chain resilience is shifting to III, which indicates that the supply chain resilience of this enterprise is being enhanced. Therefore, the improved matter element extension not only ensures the accuracy of the evaluation results, but also has higher prediction accuracy.

Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence.

Introduction: Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection. Methods: Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment. Results and Discussion: The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.

Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays.

We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011) and Chen and Cao (2002).

A Geospatial Information Grid Framework for Geological Survey.

The use of digital information in geological fields is becoming very important. Thus, informatization in geological surveys should not stagnate as a result of the level of data accumulation. The integration and sharing of distributed, multi-source, heterogeneous geological information is an open problem in geological domains. Applications and services use geological spatial data with many features, including being cross-region and cross-domain and requiring real-time updating. As a result of these features, desktop and web-based geographic information systems (GISs) experience difficulties in meeting the demand for geological spatial information. To facilitate the real-time sharing of data and services in distributed environments, a GIS platform that is open, integrative, reconfigurable, reusable and elastic would represent an indispensable tool. The purpose of this paper is to develop a geological cloud-computing platform for integrating and sharing geological information based on a cloud architecture. Thus, the geological cloud-computing platform defines geological ontology semantics; designs a standard geological information framework and a standard resource integration model; builds a peer-to-peer node management mechanism; achieves the description, organization, discovery, computing and integration of the distributed resources; and provides the distributed spatial meta service, the spatial information catalog service, the multi-mode geological data service and the spatial data interoperation service. The geological survey information cloud-computing platform has been implemented, and based on the platform, some geological data services and geological processing services were developed. Furthermore, an iron mine resource forecast and an evaluation service is introduced in this paper.

Note: Axially pull-up electrochemical etching method for fabricating tungsten nanoprobes with controllable aspect ratio.

A mathematical model representing the relation between pulling up speed, time and aspect ratio is reported, accordingly the axially pull-up electrochemical etching method for fabricating nanoprobes is proposed. The tungsten probes with predetermined shape and aspect ratio according to the model were successfully produced with this method. Then the probes were installed inside a micromanipulation system to manipulate the carbon nanotubes and measure their current-voltage (I-V) characteristics. The probe fabrication and application experiments demonstrated the reasonability and reliability of the model and method developed in this note.