600 research outputs found
Modeling and forecasting of milk production in the SAARC countries and China
No full textThis study uses yearly data from 1961 to 2018 to forecast milk production in South Asian countries (including China) using ARIMA/GARCH models and Holt’s Linear approach. It is revealed that not all the methods are equally effective in forecasting. Comparison of mean absolute percentage errors between ARIMA and Holt’s Linear model shows that Holt’s approach reveals higher errors.ARIMA forecasting results show that India will be the country with the highest milk production, followed by Pakistan and China while GARCH model fits better to Bangladesh. This paper has policy implications as it can be used for the proper planning of dairy products in the South-Asian counties to safeguard nutritional security
Interview with Mostafa Moharram
No full textهذه المقابلة مع المؤلف والسيناريست المصري مصطفى محرم. يستعرض كتاباته وأفلامه ، ويؤكد على أهمية السيناريو والكتابات المتخصصة . يشرح دور كاتب السيناريو وهو المسؤول عن خلق عمل فعال وجيد ، وكذلك التعامل بطريقة جيدة مع فريق التمثيل . أجرت المقابلة درية شرف الدينIn this interview, Egyptian author and screenwriter Mostafa Moharram speaks about his movies and the importance of scenarios in creating good work. The interview was conducted by Dorreya Sharaf al-Din
Detection and Analysis of Epilepsy Biomarkers in Electrocorticography
No full textAuthor Mostafa MohammadpourDissertation Johannes Kepler Universität Linz 202
Detection and Analysis of Epilepsy Biomarkers in Electrocorticography
No full textAuthor Mostafa MohammadpourDissertation Johannes Kepler Universität Linz 202
GLDM Algorithm for Big Data (SCADA) Wind Speed Modelling
No full textThis study enhances wind speed forecasting by implementing the second-order Generalized Least Deviation Method (GLDM), focusing on wind turbines in Turkey. The research aims to improve predictive accuracy and operational efficiency in renewable energy systems through advanced mathematical modeling in meteorology. The GLDM, utilizing a quasilinear recurrence equation, addresses the inherent non-linearity and variability of wind speed data. By applying the method to extensive SCADA data, this study minimizes residuals in nonlinear big data environments, integrating both linear and nonlinear components to refine predictions. A critical aspect of this research is the comparison between the second-order GLDM and traditional forecasting models, including statistical methods and machine learning approaches. The results demonstrate the superior performance of GLDM, as indicated by lower prediction errors and greater accuracy across key metrics. The study also underscores the importance of GLDM coefficients, , in improving predictive capabilities. The findings advocate for the adoption of GLDM in wind speed forecasting, highlighting its potential to significantly enhance wind energy management through increased accuracy. This study also sets a precedent for broader applications of advanced mathematical models in environmental science, illustrating the effectiveness of GLDM in optimizing renewable energy resources
L’année de Bacchus d’El Mostafa Bouignane entre devoir de la mémoire et exaltation de la vie
No full textThis study will discuss the approach to the structure and narrative composition of the novel entitled L\u27année de Bacchus by Mostafa Bouignane, published by Virgule Editions in 2020. This text adds to a series of stories of which Bouignane constructs a narrative universe to reveal the nature of man who, even submissive and reduced, remains capable of regaining his freedom and leading a peaceful life. Thus, our study will propose an analysis of the ideological and historical dimension of this text representative of the literary experience of the author, then at the end the questioning of his human and moral values
Assessment of Antiviral and Photodynamic Inactivation Activity of Different Compounds Against Hepatitis A Virus
No full textFood contamination from hepatitis A virus (HAV) is a great concern to food producers worldwide. Finding an innovative approach to inactivate HAV on food contact surfaces and on different produce remains a challenge. Using chemical disinfectants (e.g. chlorine) is an effective way to inactivate HAV on fomites, but it maybe unfavorable for food products. While heat inactivation of HAV remains the most efficient way to inactivate HAV when present in foods, most foodborne outbreaks of HAV are related to ready-to-eat (RTE) foods including produce which do not undergo further heating. Therefore, finding compounds with effective anti-HAV activities will be of great benefit to the food sector. In our study, oleanolic acid (OA) and ursolic acid (UA) have been investigated for their anti-HAV properties. OA at 600 μg/ml and UA at 360 μg/ml showed 2.27±0.67 and 1.33±0.35 log PFU/ml reduction after a 1 h treatment, respectively. Furthermore, to increase virus inactivation, photodynamic inactivation (PDI) was applied, which uses oxygen, light and a photosensitizer to produce reactive oxygen species (ROS). Grape seed extract (GSE) and oleanolic acid with known antiviral properties were tested as photosensitizers. Conditions using UV light at 254 nm with a distance of 72 cm and doses (energy density) of 0.012±0.000, 0.020±0.001, 0.040±0.001, 0.061±0.002, 0.081±0.002 and 0.121±0.003 J/cm² for 3, 5, 10, 15, 20 and 30 min exposure times, respectively were applied for the PDI experiments. However, the acquired viral reductions by GSE and OA mediated PDI were attributed to UV light more than ROS production. Future work may include the use of different light sources for illumination, and the use of UA as a potential photosensitizer compound
Comparison Between Two Systems for Forecasting Covid-19 Infected Cases
No full textInternational audienceBuilding a system to forecast Covid-19 infected cases is of great importance at the present time, so in this article, we present two systems to forecast cumulative Covid-19 infected cases. The first system (DLM-System) is based on deep learning models, which include both long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and Gated recurrent unit (GRU). The second system is a (TS-System) based on time series models and neural networks, with a Prioritizer for models and weights for time series models acting as an ensemble between them. We did a comparison between them in order to choose the best system to forecast cumulative Covid-19 infected cases, using the example of 7 countries. As some of them have finished the second wave and others have finished the third wave of infections (Russia, the United States of America, France, Poland, Turkey, Italy, and Spain). The criterion for choosing the best model is MAPE. It is a percentage, not an absolute value. It was concluded that an ensemble method gave the smallest errors compared to the errors of the models in the (TS-System)
System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models
No full textCOVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation
Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors
No full textThe use of wireless technology in common marine engineering applications as a medium for data transaction in measurement and control systems, is not as popular as it should be. This article aims to demonstrate the advantages of using wireless technology in maritime engineering applications through a proposed Wi-Fi based wireless system dedicated to performance and safety monitoring in marine cargo cranes. The system is based on some concepts that were suggested in the earlier literature to perform authenticated data transmission from multiple sensors through using both the ESP-NOW protocol and the WebSerial remote serial monitor. The introduced system will be integrated with an already installed system in order to render the means for implementing effective principles in automation and control engineering, such as functional safety and predictive maintenance. Additionally, this article will highlight the economic efficiency of adopting wireless technology instead of cabling as a medium for data transaction in measurement and control systems in marine engineering applications such as cargo cranes
- …
