ECG Simulation using Fourier Series: From Personal Computers to Mobile Devices

No Abstrac

Najzapadniji nalaz vrste Terapon jarbua (Forsskål, 1775.) u Sredozemnom moru: nova nezavičajna vrsta ribe u Libiji

The occurrence of the Indo-Pacific fish Terapon jarbua (Forsskål, 1775) is documented for the first time from Libya. This record represents the third and westernmost observation of the species in the Mediterranean Sea. Introduction pathway for this fish in Libyan waters and in the Mediterranean Sea in general is discussed.Nalaz indo-pacifičke vrste Terapon jarbua (Forsskål, 1775.) po prvi put je dokumentiran na području Libije. Ovaj nalaz predstavlja treće i najzapadnije opažanje ove vrste u Sredozemnom moru. U radu se raspravlja o razlozima pojavljivanja ove ribe u libijskim vodama i Sredozemnom moru općenito

First record of the northern brown shrimp Penaeus aztecus Ives, 1891 (Crustacea, Decapoda, Penaeidae) from Libyan waters

The first record of the northern brown shrimp, Penaeus aztecus Ives, 1891, from Libyan nearshore waters is hereby documented. Thirteen individuals of the species were caught by artisanal fishers using a mixture of gill and trammel nets in September 2020 within the Umm-Hufayn Lagoon. This lagoon is situated within the Gulf of Bomba along the Libyan Cyrenaica coast, and this discovery extends the known Mediterranean distribution of this western Atlantic species.peer-reviewe

Occurrence and sources of natural and anthropogenic lipid tracers in surface soils from arid urban areas of Saudi Arabia

Soil particles contain a variety of natural and anthropogenic organic components, and in urban areas can be considered as local collectors of pollutants. Surface soil samples were taken from ten urban areas in Riyadh during early winter of 2007. They were extracted with dichloromethane-methanol mixture and the extracts were analyzed by gas chromatography-mass spectrometry. The major compounds were unresolved complex mixture (UCM), plasticizers, n-alkanes, carbohydrates, n-alkanoic acids, hopanes, n-alkanols, and sterols. Vegetation detritus was the major natural source of organic compounds (24.0 ± 15.7%) in samples from areas with less human activities and included n-alkanes, n-alkanoic acids, n-alkanols, sterols and carbohydrates. Vehicular emission products and discarded plastics were the major anthropogenic sources in the soil particles (53.3 ± 21.3% and 22.7 ± 10.7%, respectively). The anthropogenic tracers were UCM, plasticizers, n-alkanes, hopanes and traces of steranes. Vegetation and human activities control the occurrence and distribution of natural and anthropogenic extractable organic matter in this arid urban area.Keywords: Petroleum residues, Biomarkers, Soils, Lipids, Plasticizer

Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.

BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas

Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants