| 1. |
- Asaf, Sajjad, et al.
(författare)
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The complete chloroplast genome of wild rice (Oryza minuta) and its comparison to related species
- 2017
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Ingår i: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Oryza minuta, a tetraploid wild relative of cultivated rice (family Poaceae), possesses a BBCC genome and contains genes that confer resistance to bacterial blight (BB) and white-backed (WBPH) and brown (BPH) plant hoppers. Based on the importance of this wild species, this study aimed to understand the phylogenetic relationships of O. minuta with other Oryza species through an in-depth analysis of the composition and diversity of the chloroplast (cp) genome. The analysis revealed a cp genome size of 135,094 bp with a typical quadripartite structure and consisting of a pair of inverted repeats separated by small and large single copies, 139 representative genes, and 419 randomly distributed microsatellites. The genomic organization, gene order, GC content and codon usage are similar to those of typical angiosperm cp genomes. Approximately 30 forward, 28 tandem and 20 palindromic repeats were detected in the O. minuta cp genome. Comparison of the complete O. minuta cp genome with another eleven Oryza species showed a high degree of sequence similarity and relatively high divergence of intergenic spacers. Phylogenetic analyses were conducted based on the complete genome sequence, 65 shared genes and matK gene showed same topologies and O. minuta forms a single clade with parental O. punctata. Thus, the complete O. minuta cp genome provides interesting insights and valuable information that can be used to identify related species and reconstruct its phylogeny.
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| 2. |
- Kamran, Muhammad, et al.
(författare)
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Hydroquinone; a novel bioactive compound from plant-derived smoke can cue seed germination of lettuce
- 2017
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Ingår i: Frontiers in Chemistry. - : Frontiers Media S.A.. - 2296-2646. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- Plant-derived smoke has been known to play an important role in distribution and growth of vegetation. Using a proficiently designed furnace, we extracted smoke from the leaves of four plant viz. Helianthus annuus,Aloe vera,Ginkgo biloba, and Cymbopogon jwarancusa. Smoke dilutions obtained from these plants were obtained in different concentrations to identify potential lettuce growth promoting smoke solution. Results revealed that smoke obtained from G. biloba significantly enhanced the lettuce seed germination. This solution was then partitioned into ethyl acetate, dichloromethane, n-hexane, chloroform and ether fractions. Ethyl acetate fraction was found to be potent to enhance seed germination. This fraction was subjected to column chromatography and spectroscopic techniques to obtain compound 1. This compound was identified as hydroquinone using 1D and 2D NMR techniques. At low concentrations (5, 10, and 20 ppm), compound 1 enhanced the lettuce seed germination; however, higher concentrations inhibited its growth as compared to control.
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| 3. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(författare)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.
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| 4. |
- Shamshad, Fahad, et al.
(författare)
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Transformers in medical imaging: A survey
- 2023
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 88
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Tidskriftsartikel (refereegranskat)abstract
- Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the fields current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.
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| 5. |
- Nizami, Abdul-Sattar, et al.
(författare)
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Waste biorefineries : enabling circular economies in developing countries
- 2017
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Ingår i: Bioresource Technology. - : Elsevier. - 0960-8524 .- 1873-2976. ; 241, s. 1101-1117
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims to examine the potential of waste biorefineries in developing countries as a solution to current waste disposal problems and as facilities to produce fuels, power, heat, and value-added products. The waste in developing countries represents a significant source of biomass, recycled materials, chemicals, energy, and revenue if wisely managed and used as a potential feedstock in various biorefinery technologies such as fermentation, anaerobic digestion (AD), pyrolysis, incineration, and gasification. However, the selection or integration of biorefinery technologies in any developing country should be based on its waste characterization. Waste biorefineries if developed in developing countries could provide energy generation, land savings, new businesses and consequent job creation, savings of landfills costs, GHG emissions reduction, and savings of natural resources of land, soil, and groundwater. The challenges in route to successful implementation of biorefinery concept in the developing countries are also presented using life cycle assessment (LCA) studies. (C) 2017 Elsevier Ltd. All rights reserved.
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| 6. |
- Sahil, Mehran, et al.
(författare)
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Seismic performance evaluation of exterior reinforced concrete beam-column connections retrofitted with economical perforated steel haunches
- 2024
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Ingår i: Results in Engineering (RINENG). - : Elsevier. - 2590-1230. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- The exterior beam-column joint (BCJ) within reinforced concrete (RC) frame structures is acknowledged as a vulnerable component prone to seismic failure. This article proposes a practical and economical strengthening method for exterior BCJs using a perforated steel haunch system. This method is designed to mitigate damage in BCJs and improve the seismic performance of the structure. Employing finite element modeling (FEM) techniques, the study evaluates the impact of perforated steel haunches on the BCJs’ behavior and performance. The investigation involves creating nine distinct models, each representing a BCJ with a steel haunch system. These models include a control model without any perforations and eight variations with different levels of perforation (ranging from 10% to 50%) within the steel haunch system. Furthermore, the study analyzes the influence of perforation shapes on the connections’ performance, considering square, circular, hexagonal, and triangular shapes. The results reveal that utilizing a steel haunch without perforations significantly increases the load-carrying capacity of a BCJ by about 89%. Additionally, circular or square-shaped perforations, up to 30–35% within the steel haunch, effectively prevent the joints’ failure and promote the ductile behavior. These findings hold the potential to advance the design methodology for RC joints subjected to seismic loads, thereby enhancing the structural resilience in earthquake-prone regions.
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