ACHENY: A standard Chenopodiaceae image dataset for deep learning models.

This paper contains datasets related to the "Efficient Deep Learning Models for Categorizing Chenopodiaceae in the wild" (Heidary-Sharifabad et al., 2021). There are about 1500 species of Chenopodiaceae that are spread worldwide and often are ecologically important. Biodiversity conservation of these species is critical due to the destructive effects of human activities on them. For this purpose, identification and surveillance of Chenopodiaceae species in their natural habitat are necessary and can be facilitated by deep learning. The feasibility of applying deep learning algorithms to identify Chenopodiaceae species depends on access to the appropriate relevant dataset. Therefore, ACHENY dataset was collected from natural habitats of different bushes of Chenopodiaceae species, in real-world conditions from desert and semi-desert areas of the Yazd province of IRAN. This imbalanced dataset is compiled of 27,030 RGB color images from 30 Chenopodiaceae species, each species 300-1461 images. Imaging is performed from multiple bushes for each species, with different camera-to-target distances, viewpoints, angles, and natural sunlight in November and December. The collected images are not pre-processed, only are resized to 224 × 224 dimensions which can be used on some of the successful deep learning models and then were grouped into their respective class. The images in each class are separated by 10% for testing, 18% for validation, and 72% for training. Test images are often manually selected from plant bushes different from the training set. Then training and validation images are randomly separated from the remaining images in each category. The small-sized images with 64 × 64 dimensions also are included in ACHENY which can be used on some other deep models.

QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic algorithm.

Service function chaining (SFC) is a mechanism that allows service providers to combine various service functions and exploit the available virtual infrastructure. The best selection of virtual services in the network is essential for meeting user requirements and constraints. This paper proposes a novel approach to generate the optimal composition of the service functions. To this end, a genetic algorithm based on context-free grammar (CFG) that adheres to the Internet Engineering Task Force (IETF) standard and Skyline was developed to use in SFC. The IETF uses cases of the data center, security, and mobile network filtered out the invalid service chains, which resulted in reduced search space. The proposed genetic algorithm found the Skyline service chain instance with the highest quality. The genetic operations were defined to ensure that the service function chains generated in the algorithm process were standard. The experimental results showed that the proposed service composition method outperformed the other methods regarding the quality of service (QoS), running time, and time complexity metrics. Ultimately, the proposed CFG could be generalized to other SFC use cases.

Development of Managerial Key Performance Indicators for A Hospital Pharmacy Digital Dashboard.

Pharmaceutical performance is a critical factor in the hospital operation. Hospital pharmacy activities require retrieving, processing, comparing, and updating the information. Dashboards are new tools that can track key performance indicators by displaying information to managers in order to improve the performance of the hospital pharmacy. We conducted this study to determine the performance indicators of hospital pharmacies in Iran. This qualitative research was conducted in 2016. The participants were hospital pharmacists and hospital managers. A semi-structured questionnaire was constructed to determine key performance indicators of the hospital pharmacy department. The questionnaire was used in face-to-face interviews and focus groups. The data were analyzed using Framework analysis. The indicators comprised three domains, including managerial indicators (satisfaction, education, staffing, and department management), clinical indicators (patient safety), and financial indicators (income, costs, and financial utilization). Traditionally, pharmacy services included provision and distribution of drugs in the hospital; however, today, with an increase in the complexity and diversity of the drugs, hospital pharmacy services include diverse fields beyond clinical affairs. It could be concluded that pharmaceutical performance has a vital role in successful hospital management. Hospital pharmacy management is not possible without monitoring performance indicators.