Health data in cloud environments

The process of provisioning healthcare involves massive healthcare data which exists in different forms on disparate data sources and in different formats. Consequently, health information systems encounter interoperability problems at many levels. Integrating these disparate systems requires the support at all levels of a very expensive infrastructures. Cloud computing dramatically reduces the expense and complexity of managing IT systems. Business customers do not need to invest in their own costly IT infrastructure, but can delegate and deploy their services effectively to Cloud vendors and service providers. It is inevitable that electronic health records (EHRs) and healthcare-related services will be deployed on cloud platforms to reduce the cost and complexity of handling and integrating medical records while improving efficiency and accuracy. The paper presents a review of EHR including definitions, EHR file formats, structures leading to the discussion of interoperability and security issues. The paper also presents challenges that have to be addressed for realizing Cloudbased healthcare systems: data protection and big health data management. Finally, the paper presents an active data model for housing and protecting EHRs in a Cloud environment

Centralizers in semisimple algebras, and descent spectrum in Banach algebras

AbstractWe prove that semisimple algebras containing some algebraic element whose centralizer is semiperfect are artinian. As a consequence, semisimple complex Banach algebras containing some element whose centralizer is algebraic are finite-dimensional. This answers affirmatively a question raised in Burgos et al. (2006) [4], and is applied to show that an element a in a semisimple complex Banach algebra A does not perturb the descent spectrum of every element commuting with a if and only if some of power of a lies in the socle of A. This becomes a Banach algebra version of a theorem in Burgos et al. (2006) [4], Kaashoek and Lay (1972) [9] for bounded linear operators on complex Banach spaces

A formal proof of the Kepler conjecture

This article describes a formal proof of the Kepler conjecture on dense sphere packings in a combination of the HOL Light and Isabelle proof assistants. This paper constitutes the official published account of the now completed Flyspeck project

Detection and monitoring of cancers with biosensors in Vietnam

Biosensors are able to provide fast, accurate and reliable detec-tions and monitoring of cancer cells, as well as to determine the effectiveness of anticancer chemotherapy agents in cancer treatments. These have attracted a great attention of research communities, especially in the capabilities of detecting the path-ogens, viruses and cancer cells in narrow scale that the conven-tional apparatus and techniques do not have. This paper pre-sents technologies and applications of biosensors for detections of cancer cells and related diseases, with the focus on the cur-rent research and technology development about biosensors in Vietnam, a typical developing country with a very high number of patients diagnosed with cancers in recent years, but having a very low cancer survival rate. The role of biosensors in early detections of diseases, cancer screening, diagnosis and treat-ment, is more and more important; especially it is estimated that by 2020, 60-70% new cases of cancers and nearly 70% of cancer deaths will be in economically disadvantaged countries. The paper is also aimed to open channels for the potential R&D collaborations with partners in Vietnam in the areas of innovative design and development of biosensors in particular and medical technology devices in general

High levels of contamination and antimicrobial-resistant non-typhoidal Salmonella serovars on pig and poultry farms in the Mekong Delta of Vietnam.

We investigated the prevalence, diversity, and antimicrobial resistance (AMR) profiles of non-typhoidal Salmonella (NTS) and associated risk factors on 341 pig, chicken, and duck farms in Dong Thap province (Mekong Delta, Vietnam). Sampling was stratified by species, district (four categories), and farm size (three categories). Pooled faeces, collected using boot swabs, were tested using ISO 6575: 2002 (Annex D). Isolates were serogrouped; group B isolates were tested by polymerase chain reaction to detect S. Typhimurium and (monophasic) serovar 4,[5],12:i:- variants. The farm-level adjusted NTS prevalence was 64·7%, 94·3% and 91·3% for chicken, duck and pig farms, respectively. Factors independently associated with NTS were duck farms [odds ratio (OR) 21·2], farm with >50 pigs (OR 11·9), pig farm with 5-50 pigs (OR 4·88) (vs. chickens), and frequent rodent sightings (OR 2·3). Both S. Typhimurium and monophasic S. Typhimurium were more common in duck farms. Isolates had a high prevalence of resistance (77·6%) against tetracycline, moderate resistance (20-30%) against chloramphenicol, sulfamethoxazole-trimethoprim, ampicillin and nalidixic acid, and low resistance (<5%) against ciprofloxacin and third-generation cephalosporins. Multidrug resistance (resistance against ⩾3 classes of antimicrobial) was independently associated with monophasic S. Typhimurium and other group B isolates (excluding S. Typhimurium) and pig farms. The unusually high prevalence of NTS on Mekong Delta farms poses formidable challenges for control

A joint scheduling and content caching scheme for energy harvesting access points with multicast

© 2017 IEEE. In this work, we investigate a system where users are served by an access point that is equipped with energy harvesting and caching mechanism. Focusing on the design of an efficient content delivery scheduling, we propose a joint scheduling and caching scheme. The scheduling problem is formulated as a Markov decision process and solved by an on-line learning algorithm. To deal with large state space, we apply the linear approximation method to the state-Action value functions, which significantly reduces the memory space for storing the function values. In addition, the preference learning is incorporated to speed up the convergence when dealing with the requests from users that have obvious content preferences. Simulation results confirm that the proposed scheme outperforms the baseline scheme in terms of convergence and system throughput, especially when the personal preference is concentrated to one or two contents

Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach

© 2019 IEEE. Practical and efficient network slicing often faces real-time dynamics of network resources and uncertain customer demands. This work provides an optimal and fast resource slicing solution under such dynamics by leveraging the latest advances in deep learning. Specifically, we first introduce a novel system model which allows the network provider to effectively allocate its combinatorial resources, i.e., spectrum, computing, and storage, to various classes of users. To allocate resources to users while taking into account the dynamic demands of users and resources constraints of the network provider, we employ a semi-Markov decision process framework. To obtain the optimal resource allocation policy for the network provider without requiring environment parameters, e.g., uncertain service time and resource demands, a Q-learning algorithm is adopted. Although this algorithm can maximize the revenue of the network provider, its convergence to the optimal policy is particularly slow, especially for problems with large state/action spaces. To overcome this challenge, we propose a novel approach using an advanced deep Q-learning technique, called deep dueling that can achieve the optimal policy at few thousand times faster than that of the conventional Q-learning algorithm. Simulation results show that our proposed framework can improve the long-term average return of the network provider up to 40% compared with other current approaches

Deep Learning-Aided Signal Enumeration for Lens Antenna Array

This work investigates a data-driven approach to detect the number of incoming signals for a lens antenna array (LAA). First, the energy-focusing property of an electromagnetic (EM) lens is utilized to generate an input spectrum, which can be used to enumerate both the multipath and independent signals. Next, we present the deep learning (DL)-assisted sharp peak recognition method referred to as the power spectrum-based convolutional neural network (PSCNet). Unlike classical techniques, such as constant false alarm rate (CFAR) detection, this data-driven detector can count received signals adaptively based on the LAA power spectrum without requiring any initial configurations. In addition, the PSCNet outperforms other state-of-the-art subspace-based detectors, even under challenging conditions, such as a low signal-to-noise ratio (SNR), a small observation size, and angular ambiguity. For the training phase, we propose a pretrained-model reusing strategy and an input pre-processing approach referred to as the power spectrum shortening (PSS) to alleviate the training burden and achieve lower complexity compared to fully retraining all isolated networks. The simulation results demonstrate that our proposed sharp peak-recognition algorithm not only accomplishes the improved signal enumeration performance but also requires lower computational resources than other subspace-based approaches

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, especially for biological entities like bacteria, presents substantial challenges. These challenges stem from the complexities of data extraction, transmission, and computation, along with the necessity for a specialized Internet of Things (IoT) infrastructure. To address these challenges, this article proposes a novel framework that leverages bio-network technologies, including the Internet of Bio-Nano Things (IoBNT), and decentralized deep learning algorithms such as federated learning (FL) and convolutional neural networks (CNN). The methodology involves using CNNs for robust pattern recognition and FL to reduce bandwidth consumption while enhancing security. IoBNT devices are utilized for precise microscopic data acquisition and transmission, which ensures minimal error rates. The results demonstrate a multi-class classification accuracy of 98.7% across 33 bacteria categories, achieving over 99% bandwidth savings. Additionally, IoBNT integration reduces biological data transfer errors by up to 98%, even under worst-case conditions. This framework is further supported by an adaptable, user-friendly dashboard, expanding its applicability across pharmaceutical and biotechnology industries