The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.

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Astrom et al. have proposed a new Smith predictor to control the process with an integrator and a long time delay. Though the method provides significant performance improvement, it fails to give effective tuning rules even when the integral constant is equal to 1. In this paper the method is extended to the general integrator/time delay process. A clearer and more logical design procedure is formulated, and simple tuning rules are developed. It shows that there is a minimum-order compensator for the process. Illustrative examples show the superiority of the proposed scheme in terms of both closed-loop performance and robustness.

Blockchain as a new technique has attracted attentions from industry and academics for sharing data across organizations. Many blockchain-based data sharing applications, such as Internet of Things devices management, need privacypreserving access services over encrypted data with dual capabilities. On one hand, they need to keep the sensitive data private such that others cannot trace and infer sensitive data stored in the block. On the other hand, they need to support fine-grained access control both from time and users' attributes. However, to the best of our knowledge, no blockchain systems can support time-bound and attributes-based access with high efficiency. In this article, we propose a privacy-preserving Internet of Things devices management scheme based on blockchain, which provides efficient timebound and attribute-based access and supports key automatic revocation. The analysis and experiments show that our scheme is quite efficient and deployable.