The microwave-assisted induction heating (MAIH) method—an emerging thermal technique—was studied to heat the prepackaged raw hard clam (Meretrix lusoria). The cooking effects on microbial and physiochemical qualities of clam were investigated. After the heating of the clam meat samples, the aerobic plate count (APC), psychrotrophic bacteria count (PBC), and total volatile basic nitrogen (TVBN) levels decreased with increasing heating time, but the shucking ratio, area shrinkage, and texture (hardness, cohesiveness, and chewiness) increased. In addition, the L* (lightness) and W (whiteness) of the clam meat samples increased significantly at the beginning of the heating period, whereas they decreased significantly with extended heating time. However, a* (redness) had the opposite trend. This study found that when clams were heated for more than 120 s at 130 °C or 150 s at 90 °C, they displayed obvious shrinking and a yellow-brown appearance, indicating that they are overcooked. After heating by MAIH for at least 110 s at 130 °C or 130 s at 90 °C, the samples were cooked well and gains a completely shucking, along with no microbial count detected. Therefore, the results indicated that the optimum heating conditions for prepackaged hard clams subjected to an MAIH machine were 130 °C for 110 s or 90 °C for 130 s.

In the research, we evaluated the effects of high-pressure processing (HPP) on the growth and histamine formation of histamine-forming bacteria (HFB) in yellowfin tuna meat during storage. Tuna meat samples inoculated with the individual HFB species Morganella morganii and Photobacterium phosphoreum were subjected to HPP treatment at 250, 350, 450, and 550 MPa for 5 min, and the changes in bacterial count, total volatile basic nitrogen (TVBN) content, pH, and histamine content during storage at 4 and 15 °C were analyzed. The results indicate that the bacterial counts of the HFB species decreased significantly with increasing pressure, and HFB became undetectable in the samples treated at 450 and 550 MPa. At a storage temperature of 15 °C, the bacterial counts of both HFB species in the control group and samples subjected to HPP treatment at 250 and 350 MPa increased significantly with storage time. The bacterial counts of M. morganii in the samples stored at 4 °C decreased, whereas those of P. phosphoreum increased gradually owing to its psychrophilic nature. HPP treatment (>250 MPa) inhibited the increases in pH and TVBN content of the samples stored at 15 °C and delayed histamine formation in the samples during storage; these effects were more significant as the pressure during HPP treatment was increased.

The emergence of digital technology has compelled the retail industry to develop innovative and sustainable business models to predict and respond to consumer behavior. However, most enterprises are crippled with doubt, lacking frameworks and methods for moving forward. This study establishes a five-step decision-making framework for digital transformation in the retail industry and verifies it using real data from convenience stores in Taiwan. Data from residential type and cultural and educational type convenience stores, which together account for 75% of all stores, underwent a one-year simulation analysis according to the following three decision models for promotions: the shelf-life extended scrap model (SES), the fixed remaining duration model (FRD), and the dynamic promotion decision model (DPD). The results indicated that the DPD model reduced scrap in residential type stores by 12.88% and increased profit by 15.43%. In cultural and educational stores, the DPD model reduced scrap by 10.78% and increased profit by 7.63%. The implementation of the DPD model in convenience stores can bring additional revenue to operators, and at the same time address the problem of food waste. With the full use of resources, sustainable operation can be turned into a concrete and feasible management decision-making plan.

A second generation and scaled-up equipment, named Aligo-2^TM (microwave-assisted induction heating, MAIH) with a sample capacity of approximately 1.0 L was designed and invented by Bottle Top Machinery Co., Ltd., Taiwan. Pre-packaged raw shrimps were heated in a scaled-up system using heating temperatures of 130 °C and 90 °C for 80 to 150 s, and the physicochemical and microbial qualities were evaluated. The total plate count, psychrotrophic bacteria count, and coliform levels decreased as heating time increased, whereas the cooking loss, color (L*, a*, and W) value, and texture increased. When shrimps were heated for the longest time of 120 s at 130 °C or 150 s at 90 °C, they displayed obvious overcooked, shrunken, and dry appearance. To obtain samples that showed a red color, cooked well, and had no microbial count, better heating conditions for the processing of pre-packaged shrimps via the MAIH scale-up system were 130 °C for 100 s or 90 °C for 130 s. This novel and scaled-up MAIH equipment provides shrimp to be cooked after being packed, thereof avoiding the post-contamination problem.

Electronic health record (EHR) implementation not only to facilitate doctor-patient communication reduces paper consumption but also allows the rapid exchange of medical records, integrating patients’ medical information from different locations. However, the costs of establishing massive and repetitive systems, constructing databases, and maintaining and exchanging data, as well as the energy consumption underscoring such an operation, represent substantial costs for a medical institution. Therefore, it is important to develop a cloud solution for EHRs and to provide a platform where resources are truly shareable. This study investigates the feasibility of cloud EHR services provided by trusted third parties. However, not only do medical records stored in an access environment with multiple users potentially endanger patient privacy, but also, without well-designed access control, such an environment may beget excessive unnecessary data access, which is costly and hinders cloud computing. To address the dual challenge of protecting patient privacy and allowing cloud computing, this study proposes the doctor–patient workflow and implements a web-based system. This mechanism ensures patients’ data security and addresses the demand of EHR cloud sharing, i.e., controlling EHR access authorities. The proposed method can protect the privacy of patients’ medical records on the cloud and grant users with minimum granularity access, thereby creating a system with the advantages of data security and cloud computing. This study proposes the doctor–patient workflow as the access control mechanism of cloud medical records, which minimizes the granularity of access. In addition, the access authority of the workflow dynamically changes with the environment, which ensures patients’ access to their medical records and defines the appropriate timing of cloud data access operations, thereby preventing unnecessary energy consumption. In practice, considerable contributions can be made to the establishment of access control and promotion of the cloud environment for medical records.

This study addresses the challenge of planogram compliance in convenience stores by proposing a scalable, automated shelf monitoring system deployed across over 7,000 7-Eleven stores in Taiwan. Traditional manual audits are labor-intensive, error-prone, and costly, creating a growing need for reliable, automated solutions. To address this challenge, the proposed system integrates computer vision and deep learning techniques into a unified pipeline capable of detecting shelves, recognizing products, and comparing shelf layouts against digital planograms through a customized alignment algorithm. The system further incorporates multi-image stitching to overcome spatial constraints and construct virtual shelves that closely replicate real-world environments, improving adaptability and accuracy. Three large-scale datasets were developed to support model training and validation: 15,232 images for shelf detection, 99,135 images for product detection, and 471 product categories averaging 210 images each for classification. Automated labeling and clustering processes were introduced to substantially reduce manual annotation time. Experimental results demonstrate that the YOLOv8-based detection models achieve exceptional precision and recall across all stages. For shelf detection, the model achieved 99.23% precision, 98.93% recall, and 99.41% mAP@50, while product detection reached 94.61% precision, 93.02% recall, and 95.7% mAP@50—both surpassing transformer-based alternatives such as Deformable DETR. ResNet101 and FAN-based Transformer models achieved 99.86% accuracy on real-world retail datasets, indicating strong model stability. In the few-shot experiments, the FAN-based model showed strong adaptability and generalization, maintaining high accuracy with only five samples per class and achieving 98.39% Top-1 and 99.48% Top-5 accuracy on unseen products, demonstrating excellent transfer learning and real-time recognition capability. The system offers high accuracy, scalability, and real-time efficiency, making it a strong alternative to manual audits and a driver of smart retail innovation.