scholarly journals Forecasting of Electricity Demand by Hybrid ANN-PSO under Shadow of the COVID-19 Pandemic

2021 ◽  
Vol 23 (6) ◽  
pp. 433-438
Author(s):  
Mohamed Rahmoune ◽  
Saliha Chettih
Keyword(s):  
Hybrid Method ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Prediction Algorithm ◽  
Economic Losses ◽  
Short Term ◽  
The Future ◽  
Generation Cost ◽  
The Difference ◽  
The Impact

Here in the research paper, we did not use smart methods to predict the future but rather to show the impact of the pandemic, we used the hybrid method using the PSO-ANN algorithm to demonstrate the impact of COVID-19 on electricity consumption and to demonstrate that we used two basic steps. The first step is to demonstrate that the hybrid method is effective for prediction. We showed that the prediction for 2019 was good, and that was before the onset of COVID-19. As for the second step, we applied the same hybrid algorithm after the emergence of COVID-19, i.e. for 2020, to note the difference between the prediction and the current pregnancy, which represents the impact of this epidemic, and this prediction in the short term. A short-term role in the operation of a power system in terms of achieving an economical electrical output and avoiding losses or outages. We've collected four consecutive years of data that is downloaded every quarter-hour of the day. Electricity consumption in Algeria is used as an input to the PSO-ANN learning algorithm. The results of the PSO-ANN pregnancy prediction algorithm have better accuracy than the ANN prediction. In the future but with the emergence of a pandemic that has had a clear difference and represents economic losses in the field of electricity, the epidemic should be viewed as a short-term variable to reduce the level of energy loss and generation cost.

scholarly journals The Impact of Switching from Short-Term Teriparatide Treatment to Denosumab Therapy Compared with Denosumab Alone for Patients with Osteoporotic Hip Fracture: A 1-Year Follow-up Study

2020 ◽  
Author(s):  
Chan Ho Park ◽  
Jun-Il Yoo ◽  
Chang Hyun Choi ◽  
You-Sung Suh
Keyword(s):  
Hip Fracture ◽  
Mineral Density ◽  
Short Term ◽  
Short Term Treatment ◽  
T Score ◽  
The Difference ◽  
Group 2 ◽  
The Impact ◽  
Group 1

Abstract Background: Switching the prescription from bone-forming medication to resorptive agents is reportedly effective for patients with severe osteoporosis. The objective of this study is to determine the impact of implementing short-term teriparatide (TPTD) intervention before denosumab (DMab) therapy compared with DMab therapy alone for 1 year after hip fracture.Methods: TPTD was administered to 24 patients for an average of 12.1 weeks after which the intervention was switched to DMab therapy for 12 months (group 1). DMab alone was administered to 16 patients for 12 months (group 2). Bone mineral density (BMD) was evaluated before and after treatment at the 1-year follow-up. The improvement of BMD and T-score in hip and spine was compared with the levels of bone turnover marker.Results: The difference of hip BMD after osteoporosis treatment was -0.0081±0.03 in group 1 and 0.0074±0.04 in group 2 (p=0.180). The difference of spine BMD was 0.0819±0.04 in group 1 and 0.0145±0.03 in group 2 (p<0.001). BMD and T-score of the spine improved significantly in groups 1 and 2 (p < 0.001). There was no statistical difference in C-terminal telopeptide and osteocalcin level. Conclusion: Short-term TPTD administration followed by DMab alone was effective only in improving spine BMD. Short-term treatment with TPTD caused mild improvement in femur neck BMD compared with DMab alone. However, further research with a longer duration of TPTD treatment is warranted, as our findings lack statistical significance.

Strengthening Experiential Learning: Innovative Virtual Engagement Strategies of Community Education Organizations

2021 ◽  
Vol 36 (2) ◽  
pp. 73-85
Author(s):  
Rachael E. Ayers ◽  
Erik K. Laursen
Keyword(s):  
Experiential Learning ◽  
Community Education ◽  
Short Term ◽  
Engagement Strategies ◽  
The Future ◽  
E Learning ◽  
Virtual Presence ◽  
K 12 ◽  
Virtual Resources ◽  
The Impact

This study focused on the impact of COVID-19 on K-12 access to community education organizations such as museums, theaters, and art studios. Participants from five community education organizations were interviewed to explore and understand their experiences of developing and promoting virtual resources. While each organization responded differently, three approaches for adaptation and innovation were critical: existing virtual presence, collaboration, and responding to e-learning fatigue. Organizations found that the leveraging of technology in the short term may enhance K-12 access to their resources in the future.

scholarly journals District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2122 ◽  
Author(s):  
Guixiang Xue ◽  
Yu Pan ◽  
Tao Lin ◽  
Jiancai Song ◽  
Chengying Qi ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Feature Fusion ◽  
District Heating ◽  
Accurate Prediction ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Short Term ◽  
District Heating System ◽  
Heating Load ◽  
The Impact

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

scholarly journals The Impact of Restrictive Measures on the Price Discovery Function of Stock Index Futures – Evidence From CSI 500 Stock Index Futures

2018 ◽  
Vol 9 (4) ◽  
pp. 117
Author(s):  
Maoguo Wu ◽  
Zhehao Zhu
Keyword(s):  
Price Discovery ◽  
Spot Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Future Market ◽  
Index Futures ◽  
The Future ◽  
The Difference ◽  
Restrictive Measures ◽  
The Impact

Restrictive measures implemented by governments have a great impact on the price discovery function of stock index futures. This study compares the price discovery function of CSI 500 stock index futures and CSI 500 stock index before and after the implementation of restrictive measures based on the reaction speed to new information, the price ratio of new information and the price contribution of both future market and spot market. It also analyzes the difference between the price discovery function of the future market and that of the spot market and thus proposes policy implications accordingly.Utilizing data of CSI 500 stock index futures in the period of the stock market crash, this study compares the price discovery function before and after the implementation of restrictive measures. By means of the VECM model and common factor analysis, it further investigates the difference in the price contribution of the two markets. Contributing to existing literature on the relationship between the future market and the spot market, this study explores the change in the price contribution of the two markets and therein studies the impact of restrictive measures on the price discovery function. Empirical evidence finds that before the implementation of restrictive measures, the price discovery function worked more efficiently, while, however, after the implementation of restrictive measures, the price discovery function did not work. Hence, stock index futures do assist in the price discovery of the spot market. In some special time periods, however, due to the impact of restrictive policies, the price contribution of the spot market exceeded that of the future market, implying that the price discovery function of the CSI 500 stock index future market is unstable.

scholarly journals The Impact of COVID-19 on the China Online Video Industry

2020 ◽  
Vol 9 (2) ◽  
pp. 11
Author(s):  
Qianqian Wu
Keyword(s):  
Capital Market ◽  
Market Research ◽  
Business Models ◽  
Online Video ◽  
Short Term ◽  
The Future ◽  
News Events ◽  
The Impact

This paper is aimed at analyzing the impact of COVID-19 on the Chinese online video industry. The hypothesis is that since people had more time to spend on leisure during the quarantine, the online video industry should be positively affected. Using methods such as collecting data from consulting firm’s research reports, analyzing news events, and summarizing evidence from security firm’s capital market research reports, I concluded that the pandemics did bring a short-term increase in people’s attention to the online video industry, opened up new opportunities for creative business models, yet also posed potential threads shall the virus strike again in the future.

scholarly journals Understanding Residential Electricity Consumption Considering Energy Efficiency Policies and The Impact On the Electricity System

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Meryem Tumbuz ◽  
Hatice Muğlkoç
Keyword(s):  
Energy Efficiency ◽  
Behavioral Change ◽  
Electricity Consumption ◽  
Allocation Problem ◽  
General View ◽  
Electricity Use ◽  
Generation Cost ◽  
Residential Electricity ◽  
Energy Efficiency Policies ◽  
The Impact

Electricity consumption increases substantially over the years where residential use significantly contributes to the overall consumption. The growth in the population and variety of home appliances together with increasing comfort levels of the people results in higher levels of residential electricity use. In fact, nearly one fourth of Turkey's total electricity consumption is due to the domestic use. To achieve global sustainability targets and reduce the overall electricity use, focusing on the domestic consumption is crucial. In this research, the energy consumptions patterns of households are determined to identify the potential electricity savings existing in the residential sector. Moreover, specific policy recommendations, which can promote the behavioral change, are driven by measuring the responsiveness of people to different measures and the combinations of these measures such as information, feedback, rewards, and social influences. A survey was conducted to determine the patterns and the responsiveness of the residential customers. The results obtained from the survey are used to depict a general view of Turkish households towards electricity consumption behaviors and their energy efficiency attitudes. Responses indicate there should be more regulations and improvements in energy policy. An electricity allocation problem is solved in order to see possible impacts of behavioral change measures on the network. Scenarios are defined for each policy and allocation problem is solved to see the possible generation cost reduction. Also, gas emissions for each scenario is recorded to understand the possible effects of policies on the environment. Results show that behavioral change studies seem to be well worth to study. In order to reach residential efficiency, possible policy alternatives are suggested for Turkish households.

scholarly journals Incidencia del capital de trabajo en la rentabilidad de la industria de alimentos Tsáchila “El Gustador”

ECA Sinergia ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 50
Author(s):  
María Enélida Vera Saca ◽  
Evelyn Dayana Cedeño Holguín ◽  
Ximena Leticia García Zambrano
Keyword(s):  
Data Collection ◽  
Food Industry ◽  
Working Capital ◽  
Short Term ◽  
Accounts Receivable ◽  
Palabras Clave ◽  
The Future ◽  
Mixed Approach ◽  
The Impact

  La presente investigación tiene como objetivo analizar el capital de trabajo y el impacto en la rentabilidad de la industria de alimentos Tsáchila “El Gustador”. La metodología utilizada tuvo un enfoque mixto y es de carácter no experimental, descriptivo y explicativo, por lo que se implementaron técnicas como la encuesta, entrevista y observación para la recolección de datos, los mismos que después de haber sido analizados e interpretados mostraron que existen deficiencias respecto a la administración de las cuentas del activo y pasivo a corto plazo, situación que afecta directamente a la liquidez de la industria y la determinación de inversión en el capital de trabajo; identificando que este hecho tiene su origen por una deficiente gestión del inventario y de las cuentas por cobrar pudiendo afectar en un futuro a las ventas de la empresa y por ende a su rentabilidad.   Palabras clave: productividad; liquidez; activo corriente; pasivo corriente.   ABSTRACT The objective of this research is to analyze the working capital and the impact on the profitability of the Tsáchila “El Gustador” food industry. The methodology used had a mixed approach and is non-experimental, descriptive and explanatory, so techniques such as survey, interview and observation were implemented for data collection, which after being analyzed and interpreted showed that there are deficiencies regarding the administration of the short-term asset and liability accounts, a situation that directly affects the liquidity of the industry and the determination of investment in working capital; identifying that this fact originates from poor inventory and accounts receivable management, which may affect the company’s sales in the future and therefore its profitability.   Keywords: productivity; liquidity; current active; current liabilities.

scholarly journals Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1881 ◽  
Author(s):  
Xiaorui Shao ◽  
Chang-Soo Kim ◽  
Palash Sontakke
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Sensor Data ◽  
Short Term ◽  
Multi Scale ◽  
Deep Model ◽  
Cost Of Time ◽  
The Impact

Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) algorithm without additional sensor data, and also considers the corresponding statistics. Different from the conventional stacked CNN–LSTM, in the proposed hybrid model, CNN and LSTM extracted features in parallel, which can obtain more robust features with less loss of original information. Chiefly, CNN extracts multi-scale robust features by various filters at three levels and wide convolution technology. LSTM extracts the features which think about the impact of different time-steps. The features extracted by CNN and LSTM are combined with six statistical components as comprehensive features. Therefore, comprehensive features are the fusion of multi-scale, multi-domain (time and statistic domain) and robust due to the utilization of wide convolution technology. We validate the effectiveness of the proposed method on three natural subsets associated with electricity consumption. The comparative study shows the state-of-the-art performance of the proposed hybrid deep model with good robustness for very short-term, short-term, medium-term, and long-term electricity consumption forecasting.

scholarly journals SUN-296 Acromegaly Significantly Impacts Employees’ Health Benefit Costs and Increases Work Absenteeism

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Kevin C j Yuen ◽  
Kathryn A Munoz ◽  
Richard Alan Brook ◽  
John D Whalen ◽  
Ian A Beren ◽  
...  
Keyword(s):  
Consumer Price Index ◽  
Work Productivity ◽  
Health Benefit ◽  
Indirect Costs ◽  
Pituitary Surgery ◽  
Short Term ◽  
Work Absenteeism ◽  
Productivity Data ◽  
The Difference ◽  
The Impact

Abstract Background: Acromegaly (ACRO) is a rare, chronic disorder of growth hormone hypersecretion associated with increased morbidity that can affect work productivity. Data on ACRO employees’ health costs and work absenteeism are limited. Aims: To assess the impact of ACRO on employees’ health benefit costs and absenteeism. Methods: A US employee database of prescription (Rx) drug, medical claims, and absenteeism (payment and time) from Jan 2010 to Apr 2019 was analyzed. Employees with the diagnosis (Dx) of ACRO were identified based on claims with ICD-9/-10 codes 253.0x/E22.0. A 12 month study period followed each employee’s first ACRO Dx in the database (the index date). ACRO patients in the study had ≥ 2 ACRO Dxs &gt; 30 days apart, or 1 ACRO Dx plus either a pituitary adenoma Dx or a pituitary surgery or radiosurgery claim during the study period. Controls were matched to each ACRO employee on demographic, job-related variables, region, and Charlson comorbidity index (CCI) score. Costs were adjusted using the general Consumer Price Index (CPI), medical CPI, and Rx cost CPI. Outcomes included direct costs (medical and Rx), indirect costs (absence payments by benefit type), and lost time (absences by benefit type). Outcomes were analyzed using two-part regression models (logistic followed by generalized linear) for each outcome, controlling for demographic and job-related variables, region, and CCI scores. Data are shown as likelihood or mean ± standard error. Findings are significant at P &lt; 0.05. Results: Participants were 18–65 yr old with continuous eligibility for medical and Rx benefits for the study period. Forty seven ACRO patients and 940 controls were identified. ACRO employees were similar to the controls in most demographic (age, gender, race) and job-related variables (tenure, full-/part-time status, exempt status, salary), but had a higher CCI (0.60 ± 0.15 vs 0.30 ± 0.03; P = 0.029) and a higher incidence of chronic lung disease (31.9 vs 17.4%; P = 0.012), hyperlipidemia (27.7 vs 16.0%, P = 0.035), arthritis (19.1 vs 3.7%), diabetes (31.9 vs 8.3%), hypertension (40.4 vs 13.6%), and thyroid disease (31.9 vs 8.9%) (P &lt; 0.0001). Patients with ACRO were 64.3% more likely to have undergone an MRI (P &lt; 0.0001).Total indirect costs (including sick leave and disability) were higher for ACRO patients ($10,530 vs $1,157; P &lt; 0.05) with both short-term and long-term disability comprising 96% of the difference. Compared with employees without ACRO, employees with ACRO used more short-term disability (10.9 vs 0.9 days; P = 0.0076) and had more total days absent from work (12.7 vs 3.3 days; P &lt; 0.05). Conclusions: Our findings indicate that ACRO has far-reaching implications on direct and indirect employee health benefit costs and increased work absenteeism. Awareness by employers of ACRO-induced increased absenteeism is important to tailor working conditions and to prevent unrealistic work expectations.

scholarly journals Business risks and their impact on business future concerning the entrepreneur’s experience with business bankruptcy: case of Czech Republic

2020 ◽  
Vol 18 (2) ◽  
pp. 418-430
Author(s):  
Ján Dvorský ◽  
Aleksandr Ključnikov ◽  
Jiří Polách
Keyword(s):  
Czech Republic ◽  
Financial Risk ◽  
Business Failure ◽  
Direct Impact ◽  
Linear Regression Models ◽  
The Czech Republic ◽  
The Future ◽  
The Difference ◽  
The Impact

The article aims to determine the difference in the perception of selected business risks and their impact on the future of business concerning the entrepreneur’s experience with business bankruptcy. The case study involved 73 small and medium-sized enterprises (SMEs) with experience of business bankruptcy and 381 SMEs without the experience of business bankruptcy from the Czech Republic (CR). Linear regression models were used to verify statistically significant causal relationships between selected indicators of the most significant business risks and respondents’ perceptions of the future of business. The results brought interesting findings. The attitudes of entrepreneurs show that personnel, market, and financial risk are among the three most significant business risks. Experience with business failure is not a significant factor in determining the impact of market indicators on the business’s perceived future. The adequacy of sales of services and products has the greatest impact. The experience of the bankruptcy of SMEs is important in financial risk attitudes. According to entrepreneurs who have no experience with bankruptcy, the perception of financial performance has the greatest direct impact on the future of business. Conversely, for entrepreneurs who have experienced bankruptcy, the ability to properly manage financial risk on the company’s future has the greatest direct impact.

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