Study and Application of the Mid-Long Term Energy early Warning System Based on SGM Model

2012 ◽  
Vol 512-515 ◽  
pp. 2519-2525
Author(s):  
Yi Xiang Deng ◽  
Si Qiang Wang
Keyword(s):  
Sustainable Development ◽  
Economic Development ◽  
Early Warning ◽  
Early Warning System ◽  
Energy Demand ◽  
Warning System ◽  
Living Standard ◽  
Demand Forecast ◽  
Term Energy

With the rapid economic development and improvement of the people’s living standard, the energy demand is increasing very fast in China. The national comprehensive energy early warning system is studied is this paper to adapt to the fundamental task of energy construction in China. On the energy demand forecast based on SGM model, the compreshensive energy warning system is built in this paper and put into the mid-long term enrgy early warning in China. For the BaU scenario, the comprehensive indices are dangerous both in 2020 and 2030; for the SD scenario, the comprehensive indices are attentive both in 2020 and 2030. So it can be concluded that the energy saving and sustainable development should be insisted to cope with the energy problems in China.

scholarly journals Early warning and rapid public health response to prevent COVID-19 outbreaks in long-term care facilities (LTCF) by monitoring SARS-CoV-2 RNA in LTCF site-specific sewage samples and assessment of antibodies response in this population: prospective study protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e052282
Author(s):  
Bonita E Lee ◽  
Christopher Sikora ◽  
Douglas Faulder ◽  
Eleanor Risling ◽  
Lorie A Little ◽  
...  
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Early Warning System ◽  
Long Term Care ◽  
Warning System ◽  
Control Measures ◽  
Term Care ◽  
Site Specific ◽  
Care Facilities

IntroductionThe COVID-19 pandemic has an excessive impact on residents in long-term care facilities (LTCF), causing high morbidity and mortality. Early detection of presymptomatic and asymptomatic COVID-19 cases supports the timely implementation of effective outbreak control measures but repetitive screening of residents and staff incurs costs and discomfort. Administration of vaccines is key to controlling the pandemic but the robustness and longevity of the antibody response, correlation of neutralising antibodies with commercial antibody assays, and the efficacy of current vaccines for emerging COVID-19 variants require further study. We propose to monitor SARS-CoV-2 in site-specific sewage as an early warning system for COVID-19 in LTCF and to study the immune response of the staff and residents in LTCF to COVID-19 vaccines.Methods and analysisThe study includes two parts: (1) detection and quantification of SARS-CoV-2 in LTCF site-specific sewage samples using a molecular assay followed by notification of Public Health within 24 hours as an early warning system for appropriate outbreak investigation and control measures and cost–benefit analyses of the system and (2) testing for SARS-CoV-2 antibodies among staff and residents in LTCF at various time points before and after COVID-19 vaccination using commercial assays and neutralising antibody testing performed at a reference laboratory.Ethics and disseminationEthics approval was obtained from the University of Alberta Health Research Ethics Board with considerations to minimise risk and discomforts for the participants. Early recognition of a COVID-19 case in an LTCF might prevent further transmission in residents and staff. There was no direct benefit identified to the participants of the immunity study. Anticipated dissemination of information includes a summary report to the immunity study participants, sharing of study data with the scientific community through the Canadian COVID-19 Immunity Task Force, and prompt dissemination of study results in meeting abstracts and manuscripts in peer-reviewed journals.

The Research on Long-Term Financial Early-Warning System of Listed Companies

2013 ◽  
Vol 670 ◽  
pp. 216-221 ◽  
Author(s):  
Wei Ming Mou ◽  
Shui Bin Gu
Keyword(s):  
Financial Crisis ◽  
Early Warning ◽  
Early Warning System ◽  
Stock Exchange ◽  
Warning System ◽  
Listed Companies ◽  
Special Treatment ◽  
Warning Model ◽  
First Time

The article takes listed companies as research samples. Firstly, it selects 36 ST or *ST companies listed in Shanghai and Shenzhen Stock Exchange Market, who received special treatment during 2007 to 2009 for the first time and it also chooses another 36 normal companies as paired ones. Then, after using Factor analysis for identifying indexes, the paper go on with utilizing logistic to structure a financial long-term warning model. To verify the effectiveness of the model, the paper selects another 12 financial crisis companies and 12 financial fit companies to test. The results come out to show that establishing an effective long-term financial early-warning system helps enterprises to avoid financial crisis.

Slope Early Warning System Design and Preliminary Practice

2014 ◽  
Vol 580-583 ◽  
pp. 481-485
Author(s):  
Fei Xu ◽  
Wen Xiong Xu ◽  
Ke Wang
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Surface Displacement ◽  
Warning System ◽  
Field Measurements ◽  
Slope Instability ◽  
Hydropower Station ◽  
Left Bank ◽  
Jinping I Hydropower Station

Early warning thresholds for slope instability were discussed in this study by means of rheological tests and field measurements due to availability and effectiveness of the data. Five warning levels were specified to take proper measures in different emergent cases. The left bank slope of Jinping I hydropower station was studied as an instance to implement the strategies introduced in the study. The surface displacement thresholds were obtained by analyzing long-term observed displacements (the former three warning levels) and the rheological tests on the rock mass (the last two levels). The results gave out that the thresholds of surface displacement rate for different warning levels were 0.30mm/d, 1.15mm/d, 3.00mm/d and 5.00mm/d. These results could be potential references for other projects.

Discussion on the Establishment of Enterprise Cost Management Early Warning System

2012 ◽  
Vol 622-623 ◽  
pp. 1860-1863
Author(s):  
Yong Gang Nie ◽  
Pei Jing
Keyword(s):  
Economic Development ◽  
Early Warning ◽  
Early Warning System ◽  
Cost Control ◽  
Cost Management ◽  
Integrated Management ◽  
Warning System ◽  
The Cost ◽  
Cost Risk

With the rapid economic development, and the competition between enterprises increasingly fierce, the rising costs make many businesses difficult, there have been companies into a small profit or even the edge of bankruptcy. Cost risk is inevitable in the course of business for every enterprise. Establishing the cost management early warning system can prevent the risk of cost control effectively, and help enterprises to improve the level of integrated management.

scholarly journals Energy supply in Malawi: Options and issues

2017 ◽  
Vol 26 (2) ◽  
pp. 19 ◽  
Author(s):  
John L. Taulo ◽  
Kenneth Joseph Gondwe ◽  
Adoniya Ben Sebitosi
Keyword(s):  
Energy Supply ◽  
Energy Demand ◽  
Industrial Development ◽  
Current Status ◽  
Potential Contribution ◽  
Annual Growth Rate ◽  
Demand Forecast ◽  
Moderate Growth ◽  
Term Energy

Inadequate energy supply is one of the major problems confronting Malawi and limiting its social, economic and industrial development. This paper reviews the current status of energy supply and demand in Malawi; examines the major sources of energy, current exploitation status and their potential contribution to the electricity supply of the country; discusses key issues facing the energy sector; and identifies broad strategies to be implemented to tackle the energy supply challenges. Using secondary data for its critical analysis, the paper also presents modelling of long-term energy demand forecast in the economic sectors of Malawi using the Model for Analysis of Energy Demand (MAED) for a study period from 2008-2030. Three scenarios namely reference (REF), moderate growth (MGS) and accelerated growth (AGS) were formulated to simulate possible future long-term energy demand based on socio-economic and technological development with the base year of 2008. Results from all scenarios suggest an increased energy demand in consuming sectors with biomass being a dominant energy form in household and industry sectors in the study period. Forecast results reveal that energy demand will increase at an annual growth rate of 1.2% and reach 5160 ktoe in 2030 under REF scenario. The growth rates for MGS and AGS are projected at 1.5% each reaching 4639 ktoe and 5974 ktoe in 2030, respectively. The final electricity demand of about 105 ktoe in the base year will grow annually at average rates of 13.8%, 15.3% and 12.6% for REF, AGS and MGS, respectively. Over the study period 2008-2030 the annual electricity per capita will increase from about 111 kWh to 1062, 1418 and 844 kWh for the REF, AGS and MGS, respectively. The final energy intensity will decrease continuously from about 13.71 kWh/US$ in the base year to 3.88 kWh/US$, 2.98 kWh/US$ and 5.27 kWh/US$ for the REF, AGS and MGS, respectively in the year 2030. In conclusion, the paper outlines strategies that could be utilized to ensure adequate supply of modern energy which is a key ingredient for achieving sustainable social and economic growth.

scholarly journals PENGEMBANGAN EARLY WARNING SYSTEM UNTUK DELISTING SAHAM SYARIAH MENGGUNAKAN SUPPORT VECTOR MACHINE (SVMs)

2020 ◽  
Vol 5 (02) ◽  
pp. 198-208
Author(s):  
Zulnani Tinggi ◽  
Sakum
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sampling Technique ◽  
Stock Index ◽  
Support Vector ◽  
Vector Machines ◽  
Sample Data ◽  
Turn Over

This study aim to produce Early Warning System in predicting the occurrence of delisting in Islamic stocks by using Support Vector Machines (SVM). The sample used in this research are companies listed on the Indonesian Syariah Stock Index (ISSI) for the period of 2012 - 2018. With the variables used in this research: Turn Over Asset, Long Term Debt, Interest Coverage, Debt to Equity, Quick Ratio, ROA, ROE Leverage, Current Ratio, ROIC. The population of this study is 335 Islamic stocks registered with ISSI. There are 102 companies which consists of listed and delisted companies from sharia shares as comparison for the sample data. The Method applied in this study is Purposive Sampling for The sampling technique. From the result found that accuracy rate of the best SVM models is SVM 4 models with 100% accuracy

A downscaling model system for early warning flooding forecast

2021 ◽  
Author(s):  
Francesco Cioffi ◽  
Federico Rosario Conticello ◽  
Mario Giannini ◽  
Tommaso Lapini ◽  
Sergio Pirozzoli ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Tropical Cyclone ◽  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System ◽  
Rainfall Threshold ◽  
Range Data ◽  
Tropical Regions ◽  
Global Circulation Models

<p>A     recent report “The Future is Now: Science for Achieving Sustainable Development” Global Sustainable Development Report 2019 - SDG Summit’      as part of the activity of Agenda 2030 of UN, highlights the opportunity to develop Early warning system for drought, floods and other meteorological events, that by providing timely information can be used by vulnerable countries to build resilience, reduce risks and prepare more effective responses. Following the suggestion,      combining outputs from Global Circulation models, remote sensing, hydraulic models and machine learning tools,       a local scale flooding Early Warning System (EWS) is proposed for the St. Lucia island (     Caribbean). The objective of the EWS is to provide forecasts of potentially dangerous flooding phenomena at different time scale: a) 0-2 hours, nowcasting; b) 24-48 hours, short range; c) 3-10 days, middle to long range. Data used to build the model are: Geopotential Height (GPH) fields at 850 hPa and Integrated Vapor Transport (IVT) fields from European Centre for Medium-range Weather Forecasts (ECMWF) - Reanalysis v5 (ERA5); Tropical Cyclone tracks from NOAA-NHC; 18 weather stations homogeneously distributed in the island; rainfall map data from the weather radar in Saint Lucia. GPH and IVT fields were defined between 110°W - 10°W and 45°N - 10°S. The EWS is constituted by an ensemble of flooding risk forecast subsystems which is potentially applicable to Atlantic tropical and extra-tropical regions. Different approaches are used for each subsystem      to link large scale atmospheric features to local rainfall and flooding: a) Non-homogeneous Hidden Markov and Event Synchronization models to translate IVT and GPH at 850 hPa  fields (from ECMWF-Set II- Atmospheric Model Ensemble) in local      daily rainfall amount and probability of  exceedance of  a prefixed heavy rainfall threshold; b) a physical based cyclone/rainfall  model to convert      Tropical cyclone attributes – position and      maximum wind      velocity       (provided from National Hurricane Center)- in rainfall intensity spatial distribution on the island; c) a surrogate model for a  fast and accurate prediction of flooding events that is obtained from a multi-layer perceptron neural network (MLPNN), which is trained on a high-fidelity dataset relying on solution of the full two-dimensional shallow water equations with direct rainfall application.        Results show an excellent ability of the models to identify the climatic configurations that determine the occurrence of extreme events and the exceeding of threshold values ​​that generate floods. In particular, during the late hurricane season September-October-November, when is highest the probability of flood events, the EWS was able to forecast the occurrence of critical climatic configurations 86% of the times they occurred. The EWS was able to predict the exceeding of the rainfall threshold that generated floods 80% of times.</p>

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