Rashad A. R. Bantan

King Abdulaziz University

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Mohammed Elgarhy

Beni-Suef University

Nidal H. Abu‐Hamdeh

King Abdulaziz University

Farrukh Jamal

Islamia University of Bahawalpur

Christophe Chesneau

Université de Caen Normandie

Ramadan H. Abu‐Zied

King Abdulaziz University

F.M. van der Zwan

King Abdullah University of Science and Technology

Nico Augustin

GEOMAR Helmholtz Centre for Ocean Research Kiel

Muhammad Aslam

King Abdulaziz University

Colin W. Devey

GEOMAR Helmholtz Centre for Ocean Research Kiel

Nasrullah Khan

University of the Punjab

Cooperative Institutions

King Abdulaziz University

GEOMAR Helmholtz Centre for Ocean Research Kiel

Islamia University of Bahawalpur

Kiel University

Cairo University

Mansoura University

Abdul Wali Khan University Mardan

University of Wollongong

King Abdul Aziz University Hospital

Leibniz Association

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Sources and pathways of carbon and nitrogen of macrophytes and sediments using stable isotopes in Al-Kharrar Lagoon, eastern Red Sea coast, Saudi Arabia

PLoS ONE (2024)

Ramadan H. Abu‐Zied Mohammed I. Orif Rashad A. R. Bantan Radwan Al-Farawati Mohammed A. Ghandourah

Elemental ratios (δ13C, δ15N and C/N) and carbon and nitrogen concentrations in macrophytes, sediments and sponges of the hypersaline Al-Kharrar Lagoon (KL), central eastern Red Sea coast, were measured to distinguish their sources, pathways and see how they have been influenced by biogeochemical processes and terrestrial inputs. The mangroves and halophytes showed the most depleted δ13C values of -27.07±0.2 ‰ and -28.34±0.4 ‰, respectively, indicating their preferential 12C uptake, similar to C3-photosynthetic plants, except for the halophytes Atriplex sp. and Suaeda vermiculata which showed δ13C of -14.31±0.6 ‰, similar to C4-plants. Macroalgae were divided into A and B groups based on their δ13C values. The δ13C of macroalgae A averaged -15.41±0.4 ‰, whereas macroalgae B and seagrasses showed values of -7.41±0.8 ‰ and -7.98 ‰, suggesting uptake of HCO3- as a source for CO2 during photosynthesis. The δ13C of sponges was -10.7±0.3 ‰, suggesting that macroalgae and seagrasses are their main favoured diets. Substrates of all these taxa showed δ13C of -15.52±0.8 ‰, suggesting the KL is at present a macroalgae-dominated lagoon. The δ15N in taxa/sediments averaged 1.68 ‰, suggesting that atmospheric N2-fixation is the main source of nitrogen in/around the lagoon. The heaviest δ15N (10.58 ‰) in halophytes growing in algal mats and sabkha is possibly due to denitrification and ammonia evaporation. The macrophytes in the KL showed high C %, N %, and C/N ratios, but this is not indicated in their substrates due possibly to a rapid turnover of dense, hypersaline waters carrying most of the detached organic materials out into the Red Sea. The δ13C allowed separation of subaerial from aquatic macrophytes, a proxy that could be used when interpreting paleo-sea level or paleoclimatic changes from the coastal marine sediments.

Biogeochemical Cycle

Isotopes of nitrogen

Avicennia marina

10.1371/journal.pone.0299562

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Citations (1)

Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine learning techniques

Mathematical Biosciences & Engineering (2022)

Rashad A. R. Bantan Zubair Ahmad Faridoon Khan Mohammed Elgarhy Zahra Almaspoor

<abstract><p>Statistical modeling and forecasting of time-to-events data are crucial in every applied sector. For the modeling and forecasting of such data sets, several statistical methods have been introduced and implemented. This paper has two aims, i.e., (i) statistical modeling and (ii) forecasting. For modeling time-to-events data, we introduce a new statistical model by combining the flexible Weibull model with the <italic>Z</italic>-family approach. The new model is called the <italic>Z</italic> flexible Weibull extension (Z-FWE) model, where the characterizations of the Z-FWE model are obtained. The maximum likelihood estimators of the Z-FWE distribution are obtained. The evaluation of the estimators of the Z-FWE model is assessed in a simulation study. The Z-FWE distribution is applied to analyze the mortality rate of COVID-19 patients. Finally, for forecasting the COVID-19 data set, we use machine learning (ML) techniques i.e., artificial neural network (ANN) and group method of data handling (GMDH) with the autoregressive integrated moving average model (ARIMA). Based on our findings, it is observed that ML techniques are more robust in terms of forecasting than the ARIMA model.</p></abstract>

Data set

10.3934/mbe.2023134

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Citations (10)

Generalized Marshall Olkin Inverse Lindley Distribution with Applications

Computers, materials & continua/Computers, materials & continua (Print) (2020)

Rashad A. R. Bantan Amal S. Hassan Mahmoud Elsehetry

In this article, a new generalization of the inverse Lindley distribution is introduced based on Marshall-Olkin family of distributions. We call the new distribution, the generalized Marshall-Olkin inverse Lindley distrib... | Find, read and cite all the research you need on Tech Science Press

Inverse distribution

10.32604/cmc.2020.010887

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Citations (9)

Influence of upstream angled ramp on fuel mixing of hydrogen jet at supersonic cross flow

Aerospace Science and Technology (2021)

Yu Jiang Nidal H. Abu-Hamdeh Rashad A. R. Bantan Zhixiong Li

Scramjet

Choked flow

10.1016/j.ast.2021.107099

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Citations (9)

Heat transfer optimization through new form of pin type of finned tube heat exchangers using the exergy and energy analysis

International Journal of Refrigeration (2020)

Nidal H. Abu‐Hamdeh Rashad A. R. Bantan Ashkan Alimoradi

Dimensionless quantity

Exergy efficiency

10.1016/j.ijrefrig.2020.03.023

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Citations (16)

Analysis of information measures using generalized type-Ⅰ hybrid censored data

AIMS Mathematics (2023)

Baria A. Helmy Amal S. Hassan Ahmed K. El-Kholy Rashad A. R. Bantan Mohammed Elgarhy

<abstract><p>An entropy measure of uncertainty has a complementary dual function called extropy. In the last six years, this measure of randomness has gotten a lot of attention. It cannot, however, be applied to systems that have survived for some time. As a result, the idea of residual extropy was created. To estimate the extropy and residual extropy, Bayesian and non-Bayesian estimators of unknown parameters of the exponentiated gamma distribution are generated. Bayesian estimators are regarded using balanced loss functions like the balanced squared error, balanced linear exponential and balanced general entropy. We use the Lindley method to get the extropy and residual extropy estimates for the exponentiated gamma distribution based on generalized type-Ⅰ hybrid censored data. To test the effectiveness of the proposed methodologies, a simulation experiment was carried out, and the actual data set was studied for illustrative purposes. In summary, the mean squared error values decrease as the number of failures increases, according to the results obtained. The Bayesian estimates of residual extropy under the balanced linear exponential loss function perform well compared to the other estimates. Alternatively, the Bayesian estimates of the extropy perform well under a balanced general entropy loss function in the majority of situations.</p></abstract>

Bayes estimator

10.3934/math.20231034

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Citations (3)

Carbon dioxide reduction using passive technique of incorporating phase change materials into envelopes: A case study of climate data from Saudi Arabia

Journal of Energy Storage (2022)

Nidal H. Abu‐Hamdeh Rashad A. R. Bantan Randa I. Hatamleh

Set point

Phase-change material

10.1016/j.est.2022.104153

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Citations (13)

Analysis of the thermal and hydraulic performance of the sector-by-sector helically coiled tube heat exchangers as a new type of heat exchangers

International Journal of Thermal Sciences (2019)

Nidal H. Abu‐Hamdeh Rashad A. R. Bantan Iskander Tlili

10.1016/j.ijthermalsci.2019.106229

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Citations (32)

Improving geological mapping of the Farasan Islands using remote sensing and ground-truth data

Journal of Maps (2017)

Khalid A. Almalki Rashad A. R. Bantan Hasham I. Hashem Oumar A. Loni Moustafa A. Ali

We integrated satellite imagery (Landsat-8) with ground-truth data to produce a detailed and complete geological map of the Farasan Islands, off the Red Sea coast of Saudi Arabia at a scale of 1:100,000. This new map improves upon past efforts by expanding the mapped lithologies on the islands into four categories. We used different techniques to enhance this lithological differentiation, including band combination with ratio stretching and supervised classification techniques based on direct field validation. The former was used to distinguish differences in reflectance values across sets of bands to create a classification image from typical reflectance patterns. The geological feature boundaries were constrained by open-source high-resolution satellite imagery (WorldView-2) as well as field observations. The resulting map clearly distinguishes between different geomorphic and geologic features, including lineaments and lithologies. As the Farasan Islands are relatively remote and not easily accessible, with an area of 739 km2, these imagery-analysis techniques were an effective tool for using remote sensing data to produce new and better mapping products of this important area.

Ground truth

Geologic map

Lineament

Lithology

Geological survey

Feature (linguistics)

10.1080/17445647.2017.1401492

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Citations (8)

Seismicity and major geologic structures of Tiran and Sanafir islands and their surroundings in the Red Sea

Environmental Earth Sciences (2017)

Hamdy A. Aboulela Essam Aboud Rashad A. R. Bantan