4,040 research outputs found
Enhancing healthcare services through cloud service: a systematic review
Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare
Design and Validation of Cyber-Physical Systems Through Co-Simulation: The Voronoi Tessellation Use Case
This paper reports on the use of co-simulation techniques to build prototypes of co-operative autonomous robotic cyber-physical systems. Designing such systems involves a mission-specific planner algorithm, a control algorithm to drive an agent performing its task; and the plant model to simulate the agent dynamics. An application aimed at positioning a swarm of unmanned aerial vehicles (drones) in a bounded area, exploiting a Voronoi tessellation algorithm developed in this work, is taken as a case study. The paper shows how co-simulation allows testing the complex system at the design phase using models created with different languages and tools. The paper then reports on how the adopted co-simulation platform enables control parameters calibration, by exploiting design space exploration technology. The INTO-CPS co-simulation platform, compliant with the Functional Mock-up Interface standard to exchange dynamic simulation models using various languages, was used in this work. The different software modules were written in Modelica, C, and Python. In particular, the latter was used to implement an original variant of the Voronoi algorithm to tesselate a convex polygonal region, by means of dummy points added at appropriate positions outside the bounding polygon. A key contribution of this case study is that it demonstrates how an accurate simulation of a cooperative drone swarm requires modeling the physical plant together with the high-level coordination algorithm. The coupling of co-simulation and design space exploration has been demonstrated to support control parameter calibration to optimize energy consumption and convergence time to the target positions of the drone swarm. From a practical point of view, this makes it possible to test the ability of the swarm to self-deploy in space in order to achieve optimal detection coverage and allow unmanned aerial vehicles in a swarm to coordinate with each other
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
The most essential component of every Distributed Ledger Technology (DLT) is
the Consensus Algorithm (CA), which enables users to reach a consensus in a
decentralized and distributed manner. Numerous CA exist, but their viability
for particular applications varies, making their trade-offs a crucial factor to
consider when implementing DLT in a specific field. This article provided a
comprehensive analysis of the various consensus algorithms used in distributed
ledger technologies (DLT) and blockchain networks. We cover an extensive array
of thirty consensus algorithms. Eleven attributes including hardware
requirements, pre-trust level, tolerance level, and more, were used to generate
a series of comparison tables evaluating these consensus algorithms. In
addition, we discuss DLT classifications, the categories of certain consensus
algorithms, and provide examples of authentication-focused and
data-storage-focused DLTs. In addition, we analyze the pros and cons of
particular consensus algorithms, such as Nominated Proof of Stake (NPoS),
Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the
applicability of these consensus algorithms to various Cyber Physical System
(CPS) use cases, including supply chain management, intelligent transportation
systems, and smart healthcare.Comment: 50 pages, 20 figure
Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features
The use of IoT technology is rapidly increasing in healthcare development and smart
healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency
of monitoring, various studies have been conducted in this field to achieve improved precision.
The architecture proposed herein is based on IoT integrated with a cloud system in which power
absorption and accuracy are major concerns. We discuss and analyze development in this domain
to improve the performance of IoT systems related to health care. Standards of communication for
IoT data transmission and reception can help to understand the exact power absorption in different
devices to achieve improved performance for healthcare development. We also systematically analyze
the use of IoT in healthcare systems using cloud features, as well as the performance and limitations
of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of
various healthcare issues in elderly people and limitations of an existing system in terms of resources,
power absorption and security when implemented in different devices as per requirements. Blood
pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications
of NB-IoT (narrowband IoT), technology that supports widespread communication with a very
low data cost and minimum processing complexity and battery lifespan. This article also focuses
on analysis of the performance of narrowband IoT in terms of delay and throughput using singleand
multinode approaches. We performed analysis using the message queuing telemetry transport
protocol (MQTTP), which was found to be efficient compared to the limited application protocol
(LAP) in sending information from sensors.Ministerio Español de Ciencia e Innovación under project
number PID2020-115570GB-C22 (DemocratAI::UGR)Cátedra de Empresa Tecnología para
las Personas (UGR-Fujitsu
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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