The risk of supply chain disruption is usually related to daily disturbances in supply chain operations (e.g., demand fluctuations) and some emergency risks, such as earthquakes and epidemic outbreaks. During a crisis, companies need agility to quickly find new suppliers and open auxiliary sales channels to meet customer needs and remain competitive. However, identifying “event” is one of the most difficult challenges of current decision support systems. If the system encounters an emergency, it is usually unable to promptly notify users of the warning to avoid risks. A sensible solution is to incorporate the real-time event-monitoring system into SCM (i.e., supply chain management) in order to share emergency information in the early stage for preemptive management in the supply chain. On the other hand, in order to process confidential supply chain data with other members, the SCM infrastructure requires secure data sharing. The blockchain-based SCM system can improve the transparency of traceability to ensure that the supply chain system provides high-quality products and protects data privacy and security. The view is taken; therefore, in this work, we combined a method of real-time event detection using collected Twitter data and blockchain technology for event monitoring to improve the visibility of the supply chain system and take preemptive measures for risk avoidance. The experiments show some interesting results and potentials for future work in the field of the agile supply chain.

Zero-emission transportation is currently a public priority, especially in big cities. For this reason, the use of electric vehicles (EVs) is receiving much attention. To facilitate the adoption of EVs, a proper charging infrastructure together with energy management is essential. This article proposes a design guideline for a direct current (DC) charging station with bipolar properties. A bipolar system can convert a two-wire system into three wires in a microgrid system with a neutral line. The configuration of the bipolar system supports different loads; therefore, the unbalanced operation is inherent to the system. The proposed bipolar DC charging station (CS) has a three-level balancing converter that reduces the step-down effort chargers. Moreover, this paper proposes the continuous-control-set model predictive control (CCS-MPC)-based balancing strategy that allows the handling of different output loads while keeping the neutral-line voltage efficiently regulated with improved dynamic performance compared to a traditional controller. Stability and parameter robustness analyses are also performed for the control parameter selection. To ensure the performance of the proposed method, both simulation and experimental results are presented and compared with those obtained from the traditional methods.

Aroma and taste have long been considered important indicators of quality coffee. Specialty coffee, that is, coffee from a single estate, farm, or village in a coffee-growing region, in particular, has a unique aroma that reflects the coffee-producing region. In order to enable the traceability of coffee origin, in this study we have developed an e-nose system to discriminate the aroma of freshly roasted coffee in different production regions. In the case study, we employed the e-nose system to experiment with various machine learning models for recognizing several collected coffee beans such as coffees from Yirgacheffe and Kona. Additionally, our contribution also includes the development of a method to create an aromatic digital fingerprint of a specific coffee bean to identify its origin. The experimental results show that the developed e-nose system achieves good recognition performance for coffee aroma recognition. The extracted digital fingerprints have great potential to be stored in an extensible coffee aroma database similar to a comprehensive library of specific coffee bean aroma characteristics, for traceability and reconfirmation of their origin.

Grid faults are found to be one of the major issues in renewable energy systems, particularly in wind energy conversion systems (WECS) connected to the grid via back-to-back (BTB) converters. Under such faulty grid conditions, the system requires an effective regulation of the active (P) and reactive (Q) power to accomplish low voltage ride through (LVRT) operation in accordance with the grid codes. In this paper, an improved finite-control-set model predictive control (FCS-MPC) scheme is proposed for a PMSG based WECS to achieve LVRT ability under symmetrical and asymmetrical grid faults, including mitigation of DC-link voltage fluctuation. With proposed predictive control, optimized switching states for cost function minimization with weighing factor (WF) selection guidelines are established for robust BTB converter control and reduced cross-coupling amid P and Q during transient conditions. Besides, grid voltage support is provided by grid side inverter control to inject reactive power during voltage dips. The effectiveness of the FCS-MPC method is compared with the conventional proportional-integral (PI) controller in case of symmetrical and asymmetrical grid faults. The simulation and experimental results endorse the superiority of the developed FCS-MPC scheme to diminish the fault effect quickly with lower overshoot and better damping performance than the traditional controller.

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of code-mixed training instances is essential for the model’s performance. Furthermore, the LLM-based deidentification method was a feasible and appealing approach that can be controlled and enhanced through natural language prompts. Conclusions: The study contributes to understanding the underlying mechanism of PLMs in addressing the deidentification process in the code-mixed context and highlights the significance of incorporating code-mixed training instances into the model training phase. To support the advancement of research, we created a manipulated subset of the resynthesized data set available for research purposes. Based on the compiled data set, we found that the LLM-based deidentification method is a feasible approach, but carefully crafted prompts are essential to avoid unwanted output. However, the use of such methods in the hospital setting requires careful consideration of data security and privacy concerns. Further research could explore the augmentation of PLMs and LLMs with external knowledge to improve their strength in recognizing rare PHI.