The proliferation of the e-commerce market has posed challenges to staff safety, product quality, and operational efficiency, especially for cold chain logistics (CCL). Recently, the logistics of vaccine supply under the worldwide COVID-19 pandemic rearouses public attention and calls for innovative solutions to tackle the challenges remaining in CCL. Accordingly, this study proposes a cyber-physical platform framework applying the Internet of Everything (IoE) and Digital Twin (DT) technologies to promote information integration and provide smart services for different stakeholders in the CCL. In the platform, reams of data are generated, gathered, and leveraged to interconnect and digitalize physical things, people, and processes in cyberspace, paving the way for digital servitization. Deep learning techniques are used for accident identification and indoor localization based on Bluetooth Low Energy (BLE) to actualize real-time staff safety supervision in the cold warehouse. Both algorithms are designed to take advantage of the IoE infrastructure to achieve online self-adapting in response to surrounding evolutions. Besides, with the help of mobile and desktop applications, paperless operation for shipment, remote temperature and humidity (T&H) monitoring, anomaly detection and warning, and customer interaction are enabled. Thus, information traceability and visibility are highly fortified in this way. Finally, a real-life case study is conducted in a pharmaceutical distribution center to demonstrate the feasibility and practicality of the proposed platform and methods. The dedicated hardware and software are developed and deployed on site. As a result, the effectiveness of staff safety management, operational informatization, product quality assurance, and stakeholder loyalty maintenance shows a noticeable improvement. The insights and lessons harvested in this study may spark new ideas for researchers and inspire practitioners to meet similar needs in the industry.

In the present era of the pandemic, vaccination is necessary to prevent severe infectious diseases, i.e., COVID-19. Specifically, vaccine safety is strongly linked to global health and security. However, the main concerns regarding vaccine record forgery and counterfeiting of vaccines are still common in the traditional vaccine supply chains. The conventional vaccine supply chains do not have proper authentication among all supply chain entities. Blockchain technology is an excellent contender to resolve the issues mentioned above. Although, blockchain based vaccine supply chains can potentially satisfy the objectives and functions of the next-generation supply chain model. However, its integration with the supply chain model is still constrained by substantial scalability and security issues. So, the current blockchain technology with traditional Proof-of-Work (PoW) consensus is incompatible with the next-generation vaccine supply chain framework. This paper introduces a model named "VaccineChain" - a novel checkpoint-assisted scalable blockchain based secure vaccine supply chain. VaccineChain guarantees the complete integrity and immutability of vaccine supply records to combat counterfeited vaccines over the supply chain. The dynamic consensus algorithm with various validating difficulty levels supports the efficient scalability of VaccineChain. Moreover, VaccineChain includes anonymous authentication among entities to provide selective revocation. This work also consists of a use case example of a secure vaccine supply chain using checkpoint assisted scalable blockchain with customized transaction generation-rule and smart contracts to demonstrate the application of VaccineChain. The comprehensive security analysis with standard theoretical proofs ensures the computational infeasibility of VaccineChain. Further, the detailed performance analysis with test simulations shows the practicability of VaccineChain.

COVID-19 is accelerating industrial information integration (III) for sustainable epidemic prevention and innovation design. It is important to emphasize that this interaction makes it reciprocal. To prevent COVID-19, the III of industrial sectors should be strengthened to encourage innovation for sustainable epidemic prevention. Accordingly, we studied the overall dynamic change trend of industrial sectors' information integration networks (IIN), the characteristics of individual IIN, and their influence on IIN performance. In the study, the gravity model and social network analysis were used to determine the variables of industrial sectors' information distance and quality, and to construct the IIN of industrial sectors. The results show that the overall relevance of the IIN of industrial sectors is low, and the network density fluctuates, with high network efficiency and poor stability. Two-way, strong correlation between industrial sectors is relatively low. The spillover effect of industrial sectors in the upstream of the industrial chain is poor, and it is difficult to have a strong information integration driving effect on the downstream industrial sectors. The interplate linkage of the IIN of industrial sectors is insufficient. Compared with point centrality and closeness, improvement of the betweenness centrality of industrial sectors can significantly improve IIN performance.

Integration of process control with optimization is critical to Smart Manufacturing (SM). Oftentimes, however, the process control solutions from one vendor do not interoperate with the optimization solutions of another. Incompatibilities among the representation and format used by the vendors can impede interoperability. Without this interoperability, it is impossible to achieve the higher level of decision support essential to SM. We believe that an emerging standard, ISO 15746, can facilitate semantic interoperability and enable the integration of process control with optimization. This paper reports the implementation and validation of ISO 15746, Automation systems and integration - Integration of advanced process control and optimization (APC-O) capabilities for manufacturing systems. Guided by the standard, we modelled major components of a typical APC-O system using tools from different vendors, implemented the information models defined in the standard, and integrated key system functions such as process optimization, process control, and user interface. A chemical process case based on the Tennessee-Eastman problem is used to demonstrate the implementation and validation of the standard. We developed a simulation of the chemical process and integrated it with the APC-O system. We discuss the standard validation experience and the findings will be used to guide advance development of the standard.