Addressing big data variety using an automated approach for data characterization

The creation of new knowledge from manipulating and analysing existing knowledge is one of the primary objectives of any cognitive system. Most of the effort on Big Data research has been focussed upon Volume and Velocity, while Variety, “the ugly duckling” of Big Data, is often neglected and difficult to solve. A principal challenge with Variety is being able to understand and comprehend the data. This paper proposes and evaluates an automated approach for metadata identification and enrichment in describing Big Data. The paper focuses on the use of self-learning systems that will enable automatic compliance of data against regulatory requirements along with the capability of generating valuable and readily usable metadata towards data classification. Two experiments towards data confidentiality and data identification were conducted in evaluating the feasibility of the approach. The focus of the experiments was to confirm that repetitive manual tasks can be automated, thus reducing the focus of a Data Scientist on data identification and thereby providing more focus towards the extraction and analysis of the data itself. The origin of the datasets used were Private/Business and Public/Governmental and exhibited diverse characteristics in relation to the number of files and size of the files. The experimental work confirmed that: (a) the use of algorithmic techniques attributed to the substantial decrease in false positives regarding the identification of confidential information; (b) evidence that the use of a fraction of a data set along with statistical analysis and supervised learning is sufficient in identifying the structure of information within it. With this approach, the issues of understanding the nature of data can be mitigated, enabling a greater focus on meaningful interpretation of the heterogeneous data.

An Introduction to the Federated Learning Standard

With the growing concern on data privacy and security, it is undesirable to collect data from all users to perform machine learning tasks. Federated learning, a decentralized learning framework, was proposed to construct a shared prediction model while keeping owners' data on their own devices. This paper presents an introduction to the emerging federated learning standard and discusses its various aspects, including i) an overview of federated learning, ii) types of federated learning, iii) major concerns and the performance evaluation criteria of federated learning, and iv) associated regulatory requirements. The purpose of this paper is to provide an understanding of the standard and facilitate its usage in model building across organizations while meeting privacy and security concerns.

Recent Advances in the Excipients Used in Modified Release Vaginal Formulations

The formulation of an ideal vaginal drug delivery system (DDS), with the requisite properties, with respect to safety, efficacy, patient compliance, aesthetics, harmonization with the regulatory requirements, and cost, requires a meticulous selection of the active ingredients and the excipients used. Novel excipients defined by diversity and multifunctionality are used in order to ameliorate drug delivery attributes. Synthetic and natural polymers are broadly used in pharmaceutical vaginal formulations (solid, semi-solid dosage forms, implantable devices, and nanomedicines) with a promising perspective in improving stability and compatibility issues when administered topically or systemically. Moreover, the use of biopolymers is aiming towards formulating novel bioactive, biocompatible, and biodegradable DDSs with a controllable drug release rate. Overviewing vaginal microenvironment, which is described by variable and perplexed features, a perceptive choice of excipients is essential. This review summarizes the recent advances on the excipients used in modified vaginal drug delivery formulations, in an attempt to aid the formulation scientist in selecting the optimal excipients for the preparation of vaginal products.

A Pragmatic Regulatory Framework for Artificial Intelligence

The application of AI technology in different sectors can intrude on the data subjects' privacy rights. While the data protection laws attempt to regulate the use and processing of personal data, these laws obstruct the growth and development of AI technology. Current regulations are unable to cope with the AI revolution due to the pacing problem and Collingridge dilemma. In view of the regulatory gaps and the complexity of technology, there is a strong justification to regulate AI technology. It is increasingly important to safeguard privacy without encumbering AI technology with regulatory requirements that will hinder its progress. With the convergence of AI and blockchain technology, privacy challenges are exacerbated. In this chapter, several types of regulations will be analysed to decipher a suitable regulatory framework for AI. This is to ensure effective regulation of AI and to allow AI to flourish with the use and application of blockchain features.

Regulatory Requirements of Quality Assurance Program in Nuclear Medicine – Review of the Procedures

The quality assurance program ensures that the entire radiological system and associated equipment are functioning properly and optimally. To this end, it is essential that a quality assurance program be in place in each medical facility where ionizing radiation sources are used, to verify the proper functioning of these instruments as well as the radionuclides measured in nuclear medicine. In addition, the procedures of the quality assurance program must comply with regulatory requirements and international recommendations. The method of this study is to review the regulatory requirements adopted by different countries regarding the quality assurance program procedures as well as various recent scientific works and those published by the International Atomic Energy Agency. In addition, to compare the radiation protection requirements of the procedures of the mentioned works; exposure justification and optimization, quality control, registration system, professional training and audit system and to suggest improvements. The result of the review study, add a procedure to the quality assurance program, so that the quality assurance attempts to cover all procedures involving sources of ionizing radiation, thus ensuring compliance with the standards of radiological safety in nuclear medicine facilities.

Standardization of sterilization regimes for textile materials under pandemic conditions (COVID-19) by the method of ionizing radiation

Methods for sterilizing textile materials in a pandemic (COVID-19) and the disadvantages of these methods are presented. A number of modern scientific works related to the sterilization of textile materials in a pandemic are considered, aimed at developing a technology for sterilizing protective medical masks and medical suits by radiation methods using gamma radiation. As a result of the analysis, it was found that the use of gamma radiation is a very dangerous technological process since natural sources are used - gamma rays, radiation technologies with gamma radiation are difficult when disposing of spent energy sources and are not easy to maintain. For sterilization of textile materials, the method of ionizing radiation is proposed. The essence of the method is that the textile material is sterilized by accelerated electrons. The expediency of carrying out theoretical and experimental research has been determined. It was found that the main criterion for sterilization of textile materials is the absorbed dose. The absorbed dose is determined experimentally, but such a procedure is time-consuming and resource-intensive, and it is not always possible to carry it out. Therefore, to calculate the absorbed dose, it is proposed to apply the mathematical formula of the absorbed dose of medical textile materials, depending on the frequency of passage of pulses of the accelerated electron beam, conveyor speed and geometric parameters of textile materials, the mathematical formula will allow finding the optimal technological modes of the sterilization process. Using the mathematical model of the absorbed dose of radiation by the material with the proposed technology, taking into account the properties of materials, it is possible to calculate the modes of irradiation of various textile materials that differ in size, shape, and physical properties, which will make it possible to develop a system of normative modes for the technology of radiation-physical sterilization and to ensure the legislative and regulatory requirements of hygiene in conditions of a pandemic.