scholarly journals Complex System Governance as a Framework for Asset Management

2021 ◽  
Vol 13 (15) ◽  
pp. 8502
Author(s):  
Polinpapilinho F. Katina ◽  
James C. Pyne ◽  
Charles B. Keating ◽  
Dragan Komljenovic
Keyword(s):  
Complex System ◽  
Performance Improvement ◽  
Asset Management ◽  
System Performance ◽  
Future Research ◽  
Complex Organizations ◽  
Potential Relationship ◽  
Structured Approach

Complex system governance (CSG) is an emerging field encompassing a framework for system performance improvement through the purposeful design, execution, and evolution of essential metasystem functions. The goal of this study was to understand how the domain of asset management (AsM) can leverage the capabilities of CSG. AsM emerged from engineering as a structured approach to organizing complex organizations to realize the value of assets while balancing performance, risks, costs, and other opportunities. However, there remains a scarcity of literature discussing the potential relationship between AsM and CSG. To initiate the closure of this gap, this research reviews the basics of AsM and the methods associated with realizing the value of assets. Then, the basics of CSG are provided along with how CSG might be leveraged to support AsM. We conclude the research with the implications for AsM and suggested future research.

Applying personalized medicine to adult severe asthma

2021 ◽  
Vol 42 (1) ◽  
pp. e8-e16 ◽  
Author(s):  
Angelica Tiotiu
Keyword(s):  
Personalized Medicine ◽  
Severe Asthma ◽  
Negative Impact ◽  
Target Therapy ◽  
Asthma Management ◽  
Future Research ◽  
Biologic Therapies ◽  
Eosinophilic Asthma ◽  
Definition Of ◽  
Structured Approach

Background: Severe asthma is a heterogeneous disease that consists of various phenotypes driven by different pathways. Associated with significant morbidity, an important negative impact on the quality of life of patients, and increased health care costs, severe asthma represents a challenge for the clinician. With the introduction of various antibodies that target type 2 inflammation (T2) pathways, severe asthma therapy is gradually moving to a personalized medicine approach. Objective: The purpose of this review was to emphasize the important role of personalized medicine in adult severe asthma management. Methods: An extensive research was conducted in medical literature data bases by applying terms such as “severe asthma” associated with “structured approach,” “comorbidities,” “biomarkers,” “phenotypes/endotypes,” and “biologic therapies.” Results: The management of severe asthma starts with a structured approach to confirm the diagnosis, assess the adherence to medications and identify confounding factors and comorbidities. The definition of phenotypes or endotypes (phenotypes defined by mechanisms and identified through biomarkers) is an important step toward the use of personalized medicine in asthma. Severe allergic and nonallergic eosinophilic asthma are two defined T2 phenotypes for which there are efficacious targeted biologic therapies currently available. Non-T2 phenotype remains to be characterized, and less efficient target therapy exists. Conclusion: Despite important progress in applying personalized medicine to severe asthma, especially in T2 inflammatory phenotypes, future research is needed to find valid biomarkers predictive for the response to available biologic therapies to develop more effective therapies in non-T2 phenotype.

BER performance analysis of FSO using hybrid-SIM technique with APD receiver over weak and strong turbulence channels

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Rajat Kumar Giri
Keyword(s):  
Performance Improvement ◽  
System Performance ◽  
Avalanche Photodiode ◽  
Free Space Optical ◽  
Error Performance ◽  
Strong Turbulence ◽  
Meijer G Function ◽  
Hermite Approximation ◽  
Ber Performance ◽  
Better Than

Abstract In this paper, a hybrid-subcarrier-intensity-modulation (hybrid-SIM) technique for the performance improvement of free-space-optical (FSO) communication system has been proposed. Subsequently, for further error performance improvement, avalanche photodiode (APD) based receiver is used in the proposed system. The system performance is analyzed at various atmospheric turbulence levels over weak and strong turbulence channels. The bit error rate (BER) is theoretically derived using Gauss–Hermite approximation and Meijer-G function and it is simulated in the MATLAB environment. The simulation result shows that the BER performance of hybrid-SIM is better than BPSK-SIM technique irrespective of the channel types and also the significant BER performance improvement is observed by APD receiver.

scholarly journals Internet of Things in Asset Management

2018 ◽  
Vol 9 (2) ◽  
pp. 104-119 ◽  
Author(s):  
Sini-Kaisu Kinnunen ◽  
Antti Ylä-Kujala ◽  
Salla Marttonen-Arola ◽  
Timo Kärri ◽  
David Baglee
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Radio Frequency Identification ◽  
Asset Management ◽  
Spare Parts ◽  
Future Research ◽  
Frequency Identification ◽  
Preliminary Study ◽  
Smart Factories ◽  
Factories Of The Future

The emerging Internet of Things (IoT) technologies could rationalize data processes from acquisition to decision making if future research is focused on the exact needs of industry. This article contributes to this field by examining and categorizing the applications available through IoT technologies in the management of industrial asset groups. Previous literature and a number of industrial professionals and academic experts are used to identify the feasibility of IoT technologies in asset management. This article describes a preliminary study, which highlights the research potential of specific IoT technologies, for further research related to smart factories of the future. Based on the results of literature review and empirical panels IoT technologies have significant potential to be applied widely in the management of different asset groups. For example, RFID (Radio Frequency Identification) technologies are recognized to be potential in the management of inventories, sensor technologies in the management of machinery, equipment and buildings, and the naming technologies are potential in the management of spare parts.

Research on the Impact of Shadow on the Performance of Building Photovoltaic System

2014 ◽  
Vol 694 ◽  
pp. 163-168
Author(s):  
Liang Guo ◽  
Yun Liang ◽  
Xu Zhang ◽  
Xiao Tian Yang
Keyword(s):  
System Performance ◽  
Rapid Development ◽  
Photovoltaic System ◽  
Future Research ◽  
Photovoltaic Module ◽  
Pv Module ◽  
New Energy ◽  
Generation Capacity ◽  
The Impact ◽  
Support Cost

With the rapid development of world economy, the energy crisis has become one of the urgent problems to be solved. Photovoltaic technology is a green new energy industry, no pollution is widely used all over the world. Typically, for photovoltaic component installation, only considering the utilization of components support cost and area, and the arrangement of components have not given enough attention. Photovoltaic module in use process will inevitably encounter the shadow, the shadow changes to make appropriate adjustments to the PV module arrangement can enhance the power generation capacity. Effect of the shadow on the photovoltaic system performance can be effectively used for photovoltaic component to bring help, is of positive significance. This study analyzed the villa model typical, and the rectangular shadow is modeling, in order to analyze the influence on the photovoltaic component. Through the conclusion of this study can determine the horizontal and vertical components of photovoltaic components which caused little damage, and provide a reference for future research of shadow and photovoltaic system performance.

scholarly journals Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Data ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 28 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Asset Management ◽  
Network Architecture ◽  
Crack Detection ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Pavement Distress ◽  
Learning Software

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

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