scholarly journals Using Persistence Spectrum for Realistic Exposure Assessment to Time Varying Radiofrequency Signals

2019 ◽  
Vol 25 (3) ◽  
pp. 57-62
Author(s):  
Annamaria Sârbu ◽  
Simona Miclăuş ◽  
Paul Bechet ◽  
Iulian Bouleanu
Keyword(s):  
Exposure Assessment ◽  
Real Time ◽  
Time Spectrum ◽  
Measurement Procedure ◽  
Statistical Parameters ◽  
Lte Advanced ◽  
Time Usage ◽  
Close Proximity ◽  
New Generation

Abstract In this article we propose the use of the persistence spectrum to accurately assess the user’s exposure to electromagnetic field emitted by new generation of communication terminals. Persistent display view available within a Real Time Spectrum Analyzer (RTSA) enables a graphical observation of the main statistical parameters of a signal. A simple case study of exposure assessment was conducted by measuring the electric field strength in the close proximity of a mobile phone operated in a LTE-Advanced mobile communication network. The proposed measurement procedure is presented and then applied to a limited number of operating situations (VoIP, Video streaming, File upload, File download). We made use of the persistence spectrum capabilities to directly access the signal strength statistics in time. By this procedure we demonstrate a supplementary facility that enables direct visual tracing of exposure dynamics during real time usage of a specific mobile application.

scholarly journals Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

2018 ◽  
Vol 319 ◽  
pp. 012016 ◽  
Author(s):  
Herman Budi Harja ◽  
Tri Prakosa ◽  
Sri Raharno ◽  
Yatna Yuwana Martawirya ◽  
Indra Nurhadi ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Machine Tools ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Usage Phase ◽  
Time Usage

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

Ethnic Enterprises, Class Resources and Market Conditions: Indian owned SMEs in Malaysia

2018 ◽  
Vol 35 (2) ◽  
pp. 5-29
Author(s):  
JESRINA ANN XAVIER ◽  
EDMUND TERENCE GOMEZ
Keyword(s):  
Food Industry ◽  
Case Study Method ◽  
Market Conditions ◽  
New Generation ◽  
Insight Into

This article investigates changes in the conduct of ethnic enterprises followingthe emergence of a new generation of owners with varying class resources andas market conditions transform. The case study method is used to examinethe impact of changing class resources and market conditions on ethnicallybasedenterprises, exploring the effects of generational transitions among smallIndian owned companies in the food industry in Malaysia. The results providean insight into key changes in the evolution of Indian owned enterprises. Theyindicate that changes in class resources and market conditions have enabledIndian owned food-based companies to alter their products to fit a largermarket, while responding to the demands of a rapidly modernizing society.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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