Emotion-cognitive reasoning integrated BERT for sentiment analysis of online public opinions on emergencies

Emotion-cognitive reasoning integrated BERT for sentiment analysis of online public opinions on emergencie

VM performance-aware virtual machine migration method based on ant colony optimization in cloud environment

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency

Applications and future directions for optical coherence tomography in dermatology.

Optical coherence tomography (OCT) is a non-invasive optical imaging method that can generate high-resolution en-face and cross-sectional images of the skin in vivo to a maximum depth of 2mm. Whilst OCT holds considerable potential for non-invasive diagnosis and disease monitoring, it is poorly understood by many dermatologists. Here, we aim to equip the practicing dermatologist with an understanding of the principles of skin OCT and the potential clinical indications. We begin with an introduction to the technology and discuss the different modalities of OCT including angiographic (dynamic) OCT, which can image cutaneous blood vessels at high resolution. Next we review clinical applications. OCT has been most extensively investigated in the diagnosis of keratinocyte carcinomas, particularly basal cell carcinoma (BCC). To date, OCT has not proven sufficiently accurate for the robust diagnosis of malignant melanoma, however the evaluation of abnormal vasculature with angiographic OCT is an area of active investigation. OCT and in particular angiographic OCT also show promise in monitoring the response of inflammatory dermatoses, such as psoriasis and connective tissues disease to therapy. We additionally discuss a potential role for artificial intelligence in improving the accuracy of interpretation of OCT imaging data

Strategies in Anti-Mycobacterium tuberculosis Drug Discovery based on Phenotypic Screening

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Diaza-anthracene Antibiotics from a Freshwater-Derived Actinomycete with Selective Antibacterial Activity toward Mycobacterium tuberculosis

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