scholarly journals A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
G. Ramkumar ◽  
Satyajeet Sahoo ◽  
T. M. Amirthalakshmi ◽  
S. Ramesh ◽  
R. Thandaiah Prabu ◽  
...  
Keyword(s):  
Solar Energy ◽  
Learning Algorithm ◽  
Negative Impact ◽  
Prediction Interval ◽  
Continuous Optimization ◽  
Energy Conversion Efficiency ◽  
Solar Photovoltaic ◽  
Short Term ◽  
Power Estimate ◽  
Interval Model

Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

THE VALUE OF SKELETAL MUSCLE MASS IN PREDICTING SHORT-TERM OUTCOMES OF SURGICAL TREATMENT FOR KLATSKIN TUMOR

2018 ◽  
Vol 64 (5) ◽  
pp. 564-569
Author(s):  
Yuriy Zharikov ◽  
Tatyana Zharikova ◽  
Vladimir Nikolenko
Keyword(s):  
Skeletal Muscle ◽  
Surgical Treatment ◽  
Muscle Mass ◽  
Skeletal Muscle Mass ◽  
Negative Impact ◽  
Treatment Algorithm ◽  
Preoperative Treatment ◽  
Klatskin Tumor ◽  
Short Term ◽  
Oncological Patients

The objective of this review study was to analyze the relationship between skeletal muscle mass and postoperative short-term outcomes morbidity in patients with Klatskin tumor who underwent surgical treatment. Low index skeletal muscle mass had a negative impact factor on postoperative morbidity following resection of Klatskin tumor and should therefore be considered as preoperative risk assessment. The further study of body composition in oncological patients allowed revealing the group of patients with high probability of postoperative complications and this factor needed to be added to future models predictive scale of short-term outcomes with the aim of making the most rational preoperative treatment algorithm.

scholarly journals The interplay of gender, social context, and long-term unemployment effects on subjective health trajectories

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Laura Altweck ◽  
Stefanie Hahm ◽  
Holger Muehlan ◽  
Tobias Gfesser ◽  
Christine Ulke ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Social Context ◽  
Negative Impact ◽  
Subjective Health ◽  
Short Term ◽  
Self Rated Health ◽  
Health Trajectories ◽  
Lower Baseline ◽  
Baseline Values

Abstract Background While a strong negative impact of unemployment on health has been established, the present research examined the lesser studied interplay of gender, social context and job loss on health trajectories. Methods Data from the German Socio-Economic Panel was used, which provided a representative sample of 6838 participants. Using latent growth modelling the effects of gender, social context (East vs. West Germans), unemployment (none, short-term or long-term), and their interactions were examined on health (single item measures of self-rated health and life satisfaction respectively). Results Social context in general significantly predicted the trajectories of self-rated health and life satisfaction. Most notably, data analysis revealed that West German women reported significantly lower baseline values of self-rated health following unemployment and did not recover to the levels of their East German counterparts. Only long-term, not short-term unemployment was related to lower baseline values of self-rated health, whereas, in relation to baseline values of life satisfaction, both types of unemployment had a similar negative effect. Conclusions In an economic crisis, individuals who already carry a higher burden, and not only those most directly affected economically, may show the greatest health effects.

scholarly journals Design and simulation of a 1-GWp solar photovoltaic power station in Sudan

Clean Energy ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 57-78
Author(s):  
Sohaib Nasr Mohamed Abdalla ◽  
Hakan Özcan
Keyword(s):  
Solar Energy ◽  
Net Present Value ◽  
Solar Power ◽  
Northern Region ◽  
Solar Photovoltaic ◽  
Power Station ◽  
Levelized Cost Of Electricity ◽  
Solar Plant ◽  
Performance Rate ◽  
The City

Abstract Developing nations have a critical need to increase electricity supply. Sudan has much unrealized potential for generating solar energy, particularly in the northern region. This research study focuses on designing a 1-GW solar power station in northern Sudan using the PVsyst7.0 software program. To determine the appropriate location for the solar-energy station, 14 criteria were evaluated. This process is generic and suitable for use in any other country. The method for conducting cash-flow estimates and return on investment is illustrated in the economic evaluation. The city of Dongola, the capital of the northern state, was selected because of its high annual irradiance on a horizontal surface at ~2333.2 kWh/m2. The simulation results show that the annual optimum tilt angle of inclination for photovoltaic (PV) modules is 30°, the energy production is 1 979 259 MWh/yr and the average annual performance rate is 0.810. In addition, the electric power consumption per capita in Sudan is 269 kWh/yr, so the proposed solar power plant with 1 979 259 MWh/yr can provide energy to 7.4 million people per year annually and reduce carbon emissions by ~18 million tons of carbon dioxide per year. Economic calculations show that the levelized cost of electricity (LCOE) is $0.06/kWh, the discounted payback period is ~11 years and the net present value is $635 291 000. As a result, the proposed grid-connected PV solar plant is considered economically, technically and environmentally feasible in Sudan.

scholarly journals The impact of the COVID-19 outbreak on Chinese-listed tourism stocks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wenmin Wu ◽  
Chien-Chiang Lee ◽  
Wenwu Xing ◽  
Shan-Ju Ho
Keyword(s):  
Stock Returns ◽  
Cultural Differences ◽  
Stock Price ◽  
Negative Impact ◽  
Tourism Industry ◽  
Regression Analyses ◽  
Short Term ◽  
Tourism Sector ◽  
Event Study Method ◽  
The Impact

AbstractThis research explored the effects of the coronavirus disease (COVID-19) outbreak on stock price movements of China’s tourism industry by using an event study method. The results showed that the crisis negatively impacted tourism sector stocks. Further quantile regression analyses supported the non-linear relationship between the government’s responses and stock returns. The results present that the resurgence of the virus in Beijing did bring about a short-term negative impact on the tourism industry. The empirical results can be used for future researchers to conduct a comparative study of cultural differences concerning government responses to the COVID-19.

scholarly journals Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1217
Author(s):  
Nicolò Bellin ◽  
Erica Racchetti ◽  
Catia Maurone ◽  
Marco Bartoli ◽  
Valeria Rossi
Keyword(s):  
Machine Learning ◽  
Fuzzy Sets ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Fuzzy C Means ◽  
Air Temperatures ◽  
Physico Chemical ◽  
Shallow Ponds

Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences.

scholarly journals Impact of Thermotherapy and Short-Term Storage on Quercus robur L. Acorn Mycobiota and Germination

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 528
Author(s):  
Jelena Kranjec Orlović ◽  
Damir Drvodelić ◽  
Marko Vukelić ◽  
Matea Rukavina ◽  
Danko Diminić ◽  
...  
Keyword(s):  
Natural Regeneration ◽  
Fungal Diversity ◽  
Quercus Robur ◽  
Negative Impact ◽  
Storage Conditions ◽  
Short Term ◽  
Isolation Frequency ◽  
Short Term Storage ◽  
Penicillium Spp ◽  
Term Storage

When natural regeneration of Quercus robur stands is hampered by an insufficient acorn yield, human assisted sowing of acorns collected in non-affected stands and stored for some period of time is performed. To inhibit the development of fungi and acorn deterioration during storage, thermotherapy is usually applied by submerging acorns for 2.5 h in water heated to 41 °C. This research aimed to test the effect of four thermotherapy treatments of different durations and/or applied temperatures as well as short-term storage at −1 °C or 3 °C on acorn internal mycobiota and germination. Fungal presence in cotyledons was analyzed in 450 acorns by isolation of mycelia on artificial media, followed by a DNA-based identification. Germination of 2000 acorns was monitored in an open field trial. Thermotherapy significantly decreased fungal diversity, while storage at 3 °C increased the isolation frequency of several fungi, mainly Penicillium spp. The most frequently isolated fungi did not show a negative impact on acorn germination after short-term storage. The study confirmed the efficiency of thermotherapy in the eradication of a part of acorn internal mycobiota, but also its effect on the proliferation of fast-colonizing fungi during storage. However, the latter showed to be more stimulated by storage conditions, specifically by storage at 3 °C.

THREATS TO NATIONAL SECURITY OF RUSSIA IN THE CONTEXT OF A HYBRID WAR

2021 ◽  
Vol 16 (3) ◽  
pp. 109-130
Author(s):  
A.S. MAKSIMOV ◽  
Keyword(s):  
Synergistic Effect ◽  
National Security ◽  
Negative Impact ◽  
Short Term ◽  
Economic Information ◽  
Hybrid Threats ◽  
Hybrid War ◽  
Political Economic ◽  
Verbal Model

This article is devoted to identifying and characterizing the threat to national security of Russian Federation in the context of a hybrid war. The main aim of the study is to assume that the huge problem for national security of Russia today is the threat of a hybrid nature. This paper proposes the author's classification of hybrid threats, which made it possible to distinguish five functional groups of threats («triads») ‒ in the spiritual and socio-cultural, military-political, economic, information and international legal spheres. The specificity of the «triads» is that each of the three elements of the «triad» is capable of producing the appearance of the second and third elements of the «triad» and maintaining their activity. A variant of ranking «triads» according to the level of their threat intensity is presented, the rates of their intensification in the short term were determined. According to the author's conclusions, the synchronous activity of the «triads» creates a synergistic effect, exerting a complicated negative impact on the state of national security of Russia. The novelty of the research, the results of which are presented in the article, are the classification of hybrid threats and the verbal model of the functioning of the «triads» of threats. The findings of the study can contribute to the development of effective techniques and strategies for countering hybrid threats to national security of Russia.

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