| 1. |
- Ahsan, Hajra, et al.
(författare)
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Photocatalysis and adsorption kinetics of azo dyes by nanoparticles of nickel oxide and copper oxide and their nanocomposite in an aqueous medium
- 2022
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Ingår i: PeerJ. - : PeerJ. - 2167-8359. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Azo dyes are recalcitrant organic pollutants present in textile industry effluents. Conventional treatment methods to remove them come with a range of disadvantages. Nanoparticles and their nanocomposites offer more efficient, less expensive and easy to handle wastewater treatment alternative. Methods. In this study, nanoparticles of nickel oxide (NiO-NPs), copper oxide (CuO-NPs) and their nanocomposite (NiO/CuO-NC) were synthesized using co- precipitation method. The functional groups present on the surface of synthesized nanomaterials were verified using Fourier-transform infrared spectroscopy (FTIR). Surface morphology was assessed using scanning electron microscopy (SEM) whereas purity, shape and size of the crystallite were determined using X-ray diffraction (XRD) technique. The potential of these nanomaterials to degrade three dyes i.e., Reactive Red-2 (RR-2), Reactive Black-5 (RB-5) and Orange II sodium salt (OII) azo dyes, was determined in an aqueous medium under visible light (photocatalysis). The photodegradation effectiveness of all nanomaterials was evaluated under different factors like nanomaterial dose (0.02-0.1 g 10 mL-1), concentration of dyes (20-100 mg L-1), and irradiation time (60-120 min). They were also assessed for their potential to adsorb RR-2 and OII dyes. Results. Results revealed that at optimum concentration (60 mgL-1) of RR-2, RB-5, and OII dyes, NiO-NPs degraded 90, 82 and 83%, CuO-NPs degraded 49, 34, and 44%, whereas the nanocomposite NiO/CuO-NC degraded 92, 93, and 96% of the said dyes respectively. The nanomaterials were categorized as the efficient degraders of the dyes in the order: NiO/CuO-NC > NiO-NPs > CuO-NPs. The highest degradation potential shown by the nanocomposite was attributed to its large surface area, small particles size, and quick reactions which were proved by advance analytical techniques. The equilibrium and kinetic adsorption of RR-2 and OII on NiO-NPs, CuO-NPs, and NiO/CuO-NC were well explained with Langmuir and Pseudo second order model, respectively (R2 ≥0.96). The maximum RR-2 adsorption (103 mg/g) was obtained with NiO/CuO-NC. It is concluded that nanocomposites are more efficient and promising for the dyes degradation from industrial wastewater as compared with dyes adsorption onto individual NPs. Thus, the nanocomposite NiO/CuO-NC can be an excellent candidate for photodegradation as well as the adsorption of the dyes in aqueous media.
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| 2. |
- 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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| 3. |
- Abid, Nosheen, 1993-, et al.
(författare)
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Burnt Forest Estimation from Sentinel-2 Imagery of Australia using Unsupervised Deep Learning
- 2021
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Ingår i: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA). - : IEEE. ; , s. 74-81
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Konferensbidrag (refereegranskat)abstract
- Massive wildfires not only in Australia, but also worldwide are burning millions of hectares of forests and green land affecting the social, ecological, and economical situation. Widely used indices-based threshold methods like Normalized Burned Ratio (NBR) require a huge amount of data preprocessing and are specific to the data capturing source. State-of-the-art deep learning models, on the other hand, are supervised and require domain experts knowledge for labeling the data in huge quantity. These limitations make the existing models difficult to be adaptable to new variations in the data and capturing sources. In this work, we have proposed an unsupervised deep learning based architecture to map the burnt regions of forests by learning features progressively. The model considers small patches of satellite imagery and classifies them into burnt and not burnt. These small patches are concatenated into binary masks to segment out the burnt region of the forests. The proposed system is composed of two modules: 1) a state-of-the-art deep learning architecture for feature extraction and 2) a clustering algorithm for the generation of pseudo labels to train the deep learning architecture. The proposed method is capable of learning the features progressively in an unsupervised fashion from the data with pseudo labels, reducing the exhausting efforts of data labeling that requires expert knowledge. We have used the realtime data of Sentinel-2 for training the model and mapping the burnt regions. The obtained F1-Score of 0.87 demonstrates the effectiveness of the proposed model.
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| 4. |
- Arshed, Muhammad Asad, et al.
(författare)
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Chem2Side : A Deep Learning Model with Ensemble Augmentation (Conventional + Pix2Pix) for COVID-19 Drug Side-Effects Prediction from Chemical Images
- 2023
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Ingår i: Information (Switzerland). - 2078-2489. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- Drug side effects (DSEs) or adverse drug reactions (ADRs) are a major concern in the healthcare industry, accounting for a significant number of annual deaths in Europe alone. Identifying and predicting DSEs early in the drug development process is crucial to mitigate their impact on public health and reduce the time and costs associated with drug development. Objective: In this study, our primary objective is to predict multiple drug side effects using 2D chemical structures, especially for COVID-19, departing from the conventional approach of relying on 1D chemical structures. We aim to develop a novel model for DSE prediction that leverages the CNN-based transfer learning architecture of ResNet152V2. Motivation: The motivation behind this research stems from the need to enhance the efficiency and accuracy of DSE prediction, enabling the pharmaceutical industry to identify potential drug candidates with fewer adverse effects. By utilizing 2D chemical structures and employing data augmentation techniques, we seek to revolutionize the field of drug side-effect prediction. Novelty: This study introduces several novel aspects. The proposed study is the first of its kind to use 2D chemical structures for predicting drug side effects, departing from the conventional 1D approaches. Secondly, we employ data augmentation with both conventional and diffusion-based models (Pix2Pix), a unique strategy in the field. These innovations set the stage for a more advanced and accurate approach to DSE prediction. Results: Our proposed model, named CHEM2SIDE, achieved an impressive average training accuracy of 0.78. Moreover, the average validation and test accuracy, precision, and recall were all at 0.73. When evaluated for COVID-19 drugs, our model exhibited an accuracy of 0.72, a precision of 0.79, a recall of 0.72, and an F1 score of 0.73. Comparative assessments against established transfer learning and machine learning models (VGG16, MobileNetV2, DenseNet121, and KNN) showcased the exceptional performance of CHEM2SIDE, marking a significant advancement in drug side-effect prediction. Conclusions: Our study introduces a groundbreaking approach to predicting drug side effects by using 2D chemical structures and incorporating data augmentation. The CHEM2SIDE model demonstrates remarkable accuracy and outperforms existing models, offering a promising solution to the challenges posed by DSEs in drug development. This research holds great potential for improving drug safety and reducing the associated time and costs.
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| 5. |
- Arshed, Muhammad Asad, et al.
(författare)
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Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model
- 2024
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Ingår i: Computers. - 2073-431X. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16.
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| 6. |
- 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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| 7. |
- Usman, Muhammad, et al.
(författare)
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A Blockchain based Scalable Domain Access Control Framework for Industrial Internet of Things
- 2024
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Ingår i: IEEE Access. - : IEEE. - 2169-3536.
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Tidskriftsartikel (refereegranskat)abstract
- Industrial Internet of Things (IIoT) applications consist of resource constrained interconnected devices that make them vulnerable to data leak and integrity violation challenges. The mobility, dynamism, and complex structure of the network further make this issue more challenging. To control the information flow in such environments, access control is critical to make collaboration and communication safe. To deal with these challenges, recent studies employ attribute-based access control on top of blockchain technology. However, the attribute-based access control frameworks suffer due to high computational overhead. In this paper, we propose an improved role-based access control framework using hyperledger blockchain to deal with IIoT requirements with less computational overhead making the information control process more efficient and real-time. The proposed framework leverages a layered architecture of chaincodes to implement the improved access control framework that handles the permission delegation and conflict management to deal with the dynamism of the IIoT network. The system uses a Policy Contract, Device Contract, and Access Contract to manage the workflow of the whole access control process. Each chaincode in the proposed framework is isolated in terms of its responsibilities to make the design low coupled. The integration of improved access control with blockchain enables the proposed framework to provide a highly scalable solution, tamper-proof, and flexible to manage conflicting scenarios. The proposed system outperforms the recent studies significantly in computational overhead in extensive simulation results. To verify the scalability and efficiency, the proposed is evaluated against a large number of concurrent virtual clients in simulation and statistical analysis proves that the proposed system is promising for further research in this domain.
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| 8. |
- Usman, Muhammad, et al.
(författare)
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Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning
- 2023
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 23:8
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Tidskriftsartikel (refereegranskat)abstract
- The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.
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| 9. |
- Abid, Nosheen, 1993-, et al.
(författare)
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UCL: Unsupervised Curriculum Learning for Water Body Classification from Remote Sensing Imagery
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 105
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.
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| 10. |
- Asim, Muhammad, et al.
(författare)
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Techno-economic assessment of energy and environmental impact of waste-to-energy electricity generation
- 2023
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Ingår i: Energy Reports. - : Elsevier. - 2352-4847. ; 9:Suppl 1, s. 1087-1097
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Tidskriftsartikel (refereegranskat)abstract
- This study explored cumulative 127.5MW waste to energy (WtE) potential in five populous cities of Pakistan based on local waste characterization profiles and global standards. The 50MW WtE plant in Lahore using National electricity regulator codes and practices resulted in an attractive Levelized cost of electricity (LCOE) of US¢ 7.86/kWh over 25 years with a $151.5 million investment cost. The net savings to Lahore Waste Management Company can be $103.4 and $137.7 million respectively with and without tipping fees on account of waste disposal cost, bricks revenue using bottom ash, and waste fee. The project developers can get net savings of $16.9 and $51.5 million respectively with and without tipping fees other than LCOE. Furthermore, the greenhouse gas emissions of 216.6 million tons of CO2eq can be saved throughout plant life against 279 GWh/year energy generation, in terms of grid emission factor and current methane release into the atmosphere from the dumping site.
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