| 1. |
- Shakoor, Awais, et al.
(författare)
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A global meta-analysis of greenhouse gases emission and crop yield under no-tillage as compared to conventional tillage
- 2020
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 1879-1026 .- 0048-9697.
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Tidskriftsartikel (refereegranskat)abstract
- No-tillage (NT) practice is extensively adopted with aims to improve soil physical conditions, carbon (C) sequestration and to alleviate greenhouse gases (GHGs) emissions without compromising crop yield. However, the influences of NT on GHGs emissions and crop yields remains inconsistent. A global meta-analysis was performed by using fifty peer-reviewed publications to assess the effectiveness of soil physicochemical properties, nitrogen (N) fertilization, type and duration of crop, water management and climatic zones on GHGs emissions and crop yields under NT compared to conventional tillage (CT) practices. The outcome reveals that compared to CT, NT increased CO2, N2O, and CH4 emissions by 7.1, 12.0, and 20.8%, respectively. In contrast, NT caused up to 7.6% decline in global warming potential as compared to CT. However, absence of difference in crop yield was observed both under NT and CT practices. Increasing N fertilization rates under NT improved crop yield and GHGs emission up to 23 and 58%, respectively, compared to CT. Further, NT practices caused an increase of 16.1% CO2 and 14.7% N2O emission in the rainfed areas and up to 54.0% CH4 emission under irrigated areas as compared to CT practices. This meta-analysis study provides a scientific basis for evaluating the effects of NT on GHGs emissions and crop yields, and also provides basic information to mitigate the GHGs emissions that are associated with NT practice.
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| 2. |
- Ashraf, Waqar Muhammad, et al.
(författare)
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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
- 2022
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Ingår i: Alexandria Engineering Journal. - 1110-0168 .- 2090-2670. ; 61:3, s. 1864-1880
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Tidskriftsartikel (refereegranskat)abstract
- The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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| 3. |
- Kalsoom, Aasia, et al.
(författare)
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In Vitro Evaluation of Cytotoxic Potential of Caladium lindenii Extracts on Human Hepatocarcinoma HepG2 and Normal HEK293T Cell Lines
- 2022
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Ingår i: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- Data regarding the therapeutic potential of Caladium lindenii (C. lindenii) are insufficient. It becomes more important to explore plants as an alternative or palliative therapeutics in deadly diseases around the globe. The current study was planned to explore C. lindenii for its anticancer activity of ethanolic and hexane extracts of C. lindenii leaves against hepatic carcinoma (HepG2) and human embryonic kidney (HEK293T) cell lines. HepG2 and HEK293T cells were treated with 10, 50, 100, 200, and 400 μg/mL of ethanolic and hexane extracts of C. lindenii and were incubated for 72 h. Antiproliferative activity was measured by 3-(4,5-dimethylthiazol-2yl)-2,5-biphenyl tetrazolium bromide (MTT) assay, and percentage viability were calculated through crystal violet staining and cellular morphology by Floid Cell Imaging Station. The study showed ethanolic extract exhibiting a significantly higher antiproliferative effect on HepG2 (IC50=31 μg/mL) in a concentration-dependent manner, while HEK293T (IC50=241 μg/mL) cells showed no toxicity. Hexane extract exhibited lower cytotoxicity (IC50=150 μg/mL) on HepG2 cells with no effect on HEK293T (IC50=550 μg/mL). On the other hand, the percentage viability of HepG2 cells was recorded as 78%, 67%, 50%, 37%, and 28% by ethanolic extracts, and 88%, 80%, 69%, 59%, and 50% by hexane extracts at tested concentrations of both extracts. Toxicity assay showed significantly safer ranges of percentage viabilities in normal cells (HEK293T), i.e., 95%, 90%, 88%, 76%, and 61% with ethanolic extract and 97%, 95%, 88%, 75%, and 62% with hexane extract. The assay validity revealed 100% viability in the control negative (dimethyl sulfoxide treated) and less than 45% in the control positive (cisplatin) on both HepG2 and HEK293T cells. Morphological studies showed alterations in HepG2 cells upon exposure to >50 μg/mL of ethanolic extracts and ≥400 μg/mL of hexane extracts. HEK293T on the other hand did not change its morphology against any of the extracts compared to the aggressive changes on the HepG2 cell line by both extracts and positive control (cisplatin). In conclusion, extracts of C. lindenii are proved to have significant potential for cytotoxicity-induced apoptosis in human cancer HepG2 cells and are less toxic to normal HEK293T cells. Hence C. lindenii extracts are proposed to be used against hepatocellular carcinoma (HCC) after further validations.
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| 4. |
- Munir, M. Adeel, et al.
(författare)
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Blockchain Adoption for Sustainable Supply Chain Management : Economic, Environmental, and Social Perspectives
- 2022
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Ingår i: Frontiers in Energy Research. - : Frontiers Media S.A.. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network.
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| 5. |
- Ali, Irfan, et al.
(författare)
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A thermal-aware scheduling algorithm for reducing thermal risks in DAG-based applications in cyber-physical systems
- 2023
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Ingår i: Ubiquitous security. - Singapore : Springer. - 9789819902712 - 9789819902729 ; , s. 497-508
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Konferensbidrag (refereegranskat)abstract
- Directed Acyclic Graph (DAG)-based scheduling applications are critical to resource allocation in the Cloud, Edge, and Fog layers of cyber-physical systems (CPS). However, thermal anomalies in DVFS-enabled homogeneous multiprocessor systems (HMSS) may be exploited by malicious applications posing risks to the availability of the underlying CPS. This can negatively affect the trustworthiness of CPS. This paper proposes an algorithm to address the thermal risks in DVFS-enabled HMSS for periodic DAG-based applications. It also improves the current list scheduling-based Depth-First and Breadth-First techniques without violating the timing constraints of the system. We test the algorithm using standard benchmarks and synthetic applications in a simulation setup. The results show a reduction in the temperature peaks by up to 30%, average temperature by up to 22%, temperature variations up to 3 times, and temperature spatial gradients by up to 4 times as compared to the conventional Depth-First Scheduling algorithms.
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| 6. |
- Aslam, Bilal, et al.
(författare)
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A low profile miniature RFID tag antenna dedicated to IoT applications
- 2019
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Ingår i: Electromagnetics. - : Taylor & Francis. - 0272-6343 .- 1532-527X. ; 39:6, s. 393-406
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Tidskriftsartikel (refereegranskat)abstract
- RFID tag antennas with stable performance on the diverse electromagnetic mounting platforms are an integral part of the ubiquitous RFID systems. This research article presents a novel tag antenna design that facilitates the said objective. The proposed antenna consists of a modified H-shaped slot structure that ensures considerable robustness from the application environment through confining the surface current density within the antenna structure. The antenna offers a tunable bandwidth of 40 MHz within the microwave band of (2.4-2.5) GHz. The proposed tag antenna exhibits excellent response on metallic platforms of different sizes and thicknesses with an effective gain of almost four times of that in free space. Furthermore, the designed tag antenna performs adequately well on low-medium permittivity dielectrics (glass, paper, and plastic) and RF absorbers (water). The free space and on-metal performance of the proposed tag antenna are verified by testing a prototype realized on the FR4 substrate.
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| 7. |
- Ehatisham-ul-Haq, Muhammad, et al.
(författare)
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Identifying smartphone users based on their activity patterns via mobile sensing
- 2017
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 113, s. 202-209
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Tidskriftsartikel (refereegranskat)abstract
- Smartphones are ubiquitous devices that enable users to perform many of their routine tasks anytime and anywhere. With the advancement in information technology, smartphones are now equipped with sensing and networking capabilities that provide context-awareness for a wide range of applications. Due to ease of use and access, many users are using smartphones to store their private data, such as personal identifiers and bank account details. This type of sensitive data can be vulnerable if the device gets lost or stolen. The existing methods for securing mobile devices, including passwords, PINs and pattern locks are susceptible to many bouts such as smudge attacks. This paper proposes a novel framework to protect sensitive data on smartphones by identifying smartphone users based on their behavioral traits using smartphone embedded sensors. A series of experiments have been conducted for validating the proposed framework, which demonstrate its effectiveness.
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| 8. |
- Khan, Muhammad Ahsan Iqbal, et al.
(författare)
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An Experimental and Comparative Performance Evaluation of a Hybrid Photovoltaic-Thermoelectric System
- 2021
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- The majority of incident solar irradiance causes thermalization in photovoltaic (PV) cells, attenuating their efficiency. In order to use solar energy on a large scale and reduce carbon emissions, their efficiency must be enhanced. Effective thermal management can be utilized to generate additional electrical power while simultaneously improving photovoltaic efficiency. In this work, an experimental model of a hybrid photovoltaic-thermoelectric generation (PV-TEG) system is developed. Ten bismuth telluride-based thermoelectric modules are attached to the rear side of a 10 W polycrystalline silicon-based photovoltaic module in order to recover and transform waste thermal energy to usable electrical energy, ultimately cooling the PV cells. The experiment was then carried out for 10 days in Lahore, Pakistan, on both a simple PV module and a hybrid PV-TEG system. The findings revealed that a hybrid system has boosted PV module output power and conversion efficiency. The operating temperature of the PV module in the hybrid system is reduced by 5.5%, from 55 degrees C to 52 degrees C. Due to a drop in temperature and the addition of some recovered energy by thermoelectric modules, the total output power and conversion efficiency of the system increased. The hybrid system's cumulative output power increased by 19% from 8.78 to 10.84 W, compared to the simple PV system. Also, the efficiency of the hybrid PV-TEG system increased from 11.6 to 14%, which is an increase of 17% overall. The results of this research could provide consideration for designing commercial hybrid PV-TEG systems.
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| 9. |
- Malik, Shairyar, et al.
(författare)
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An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics
- 2023
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Ingår i: Diagnostics. - : MDPI. - 2075-4418. ; 13:7, s. 1285-1285
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Tidskriftsartikel (refereegranskat)abstract
- The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient results. Many studies resolve the matter partly. However, there exists plenty of room for new research in this field. Recently, many algorithms have been presented to preprocess skin lesions, aiding the segmentation algorithms to generate efficient outcomes. Nature-inspired algorithms and metaheuristics help to estimate the optimal parameter set in the search space. This research article proposes a hybrid metaheuristic preprocessor, BA-ABC, to improve the quality of images by enhancing their contrast and preserving the brightness. The statistical transformation function, which helps to improve the contrast, is based on a parameter set estimated through the proposed hybrid metaheuristic model for every image in the dataset. For experimentation purposes, we have utilised three publicly available datasets, ISIC-2016, 2017 and 2018. The efficacy of the presented model is validated through some state-of-the-art segmentation algorithms. The visual outcomes of the boundary estimation algorithms and performance matrix validate that the proposed model performs well. The proposed model improves the dice coefficient to 94.6% in the results.
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| 10. |
- 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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