| 1. |
- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
- Hameed, Zeeshan, et al.
(författare)
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A Comprehensive Review on Thermal Coconversion of Biomass, Sludge, Coal, and Their Blends Using Thermogravimetric Analysis
- 2020
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Ingår i: Journal of Chemistry. - : Hindawi Publishing Corporation. - 2090-9063 .- 2090-9071. ; 2020
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Forskningsöversikt (refereegranskat)abstract
- Lignocellulosic biomass is a vital resource for providing clean future energy with a sustainable environment. Besides lignocellulosic residues, nonlignocellulosic residues such as sewage sludge from industrial and municipal wastes are gained much attention due to its large quantities and ability to produce cheap and clean energy to potentially replace fossil fuels. These cheap and abundantly resources can reduce global warming owing to their less polluting nature. The low-quality biomass and high ash content of sewage sludge-based thermal conversion processes face several disadvantages towards its commercialization. Therefore, it is necessary to utilize these residues in combination with coal for improvement in energy conversion processes. As per author information, no concrete study is available to discuss the synergy and decomposition mechanism of residues blending. The objective of this study is to present the state-of-the-art review based on the thermal coconversion of biomass/sewage sludge, coal/biomass, and coal/sewage sludge blends through thermogravimetric analysis (TGA) to explore the synergistic effects of the composition, thermal conversion, and blending for bioenergy production. This paper will also contribute to detailing the operating conditions (heating rate, temperature, and residence time) of copyrolysis and cocombustion processes, properties, and chemical composition that may affect these processes and will provide a basis to improve the yield of biofuels from biomass/sewage sludge, coal/sewage sludge, and coal/biomass blends in thermal coconversion through thermogravimetric technique. Furthermore, the influencing factors and the possible decomposition mechanism are elaborated and discussed in detail. This study will provide recent development and future prospects for cothermal conversion of biomass, sewage, coal, and their blends.
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| 3. |
- Ahmad, Ikhlas, et al.
(författare)
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Highly Compact GCPW-Fed Multi-Branch Structure Multi-Band Antenna for Wireless Applications
- 2022
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Ingår i: International Journal of Antennas and Propagation. - : Hindawi Limited. - 1687-5869 .- 1687-5877. ; 2022, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we present a highly compact multi-branch structure multi-band antenna with a grounded coplanar waveguide (GCPW)-fed structure printed on 26 x 13 x 1.6 mm(3) sized FR-4 substrate having dielectric constant epsilon r of 4.3 and loss tangent delta of 0.02. In the proposed antenna, five branches are extended from the main radiator to provide multi-band behavior. Two branches are introduced at the upper end of the main radiator, effectively covering the lower bands, while the other three branches are introduced near the center of the main radiator to extend operation to higher bands. The designed antenna covers five different bands: 2.4 GHz, 4.5 GHz, 5.5 GHz, 6.5 GHz, and 7.8 GHz, with respective gain values of 1.34, 1.60, 1.83, 1.80, and 3.50 dBi and respective radiation efficiency values of 90, 88, 84, 75, and 89%. The antenna shows a good impedance bandwidth, ranging from 170 MHz to 3070 MHz. The proposed antenna is simulated in CST Microwave Studio, while its performance is experimentally validated by the fabrication and testing process. The antenna has potential applications for IoT, sub-6 GHz 5G and WLAN (both enablers for IoT), C-band, and X-band services.
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| 4. |
- Alizadehsani, Roohallah, et al.
(författare)
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Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 12, s. 35796-35812
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Forskningsöversikt (refereegranskat)abstract
- The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.
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| 5. |
- Hussain, Matloob, et al.
(författare)
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Changes in gravitational parameters inferred from time-variable GRACE data- A case study for October 2005 Kashmir Earthquake
- 2016
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Ingår i: Journal of Applied Geophysics. - Amsterdam : Elsevier BV. - 0926-9851 .- 1879-1859. ; 132, s. 174-183
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Tidskriftsartikel (refereegranskat)abstract
- The earth's gravity changes are attributed to the redistribution of masses within and/or on the surface of the earth, which are due to the frictional sliding, tensile cracking and/or cataclastic flow of rocks along the faults and detectable by earthquake events. Inversely, the gravity changes are useful to describe the earthquake seismicity over the active orogenic belts. The time variable gravimetric data are hardly available to the public domain. However, Gravity Recovery and Climatic Experiment (GRACE) is the only satellite mission dedicated to model the variation of the gravity field and an available source to the science community. Here, we have tried to envisage gravity changes in terms of gravity anomaly (Δg), geoid (N) and the gravity gradients over the Indo-Pak plate with emphasis upon Kashmir earthquake of October 2005. For this purpose, we engaged the spherical harmonic coefficients of monthly gravity solutions from the GRACE satellite mission, which have good coverage over the entire globe with unprecedented accuracy. We have analysed numerically the solutions after removing the hydrological signals, during August to November 2005, in terms of corresponding monthly differentials of gravity anomaly, geoid and the gradients. The regional structures like Main Mantle Thrust (MMT), Main Karakoram Thrust (MKT), Herat and Chaman faults are in closed association with topography and with gravity parameters from the GRACE gravimetry and EGM2008 model. The monthly differentials of these quantities indicate the stress accumulation in the northeast direction in the study area. Our numerical results show that the horizontal gravity gradients seem to be in good agreement with tectonic boundaries and differentials of the gravitational elements are subtle to the redistribution of rock masses and topography caused by 2005 Kashmir earthquake. Moreover, the gradients are rather more helpful for extracting the coseismic gravity signatures caused by seismicity over the area. Higher positive values of gravity components having higher terrain elevations are more vulnerable to the seismicity and lower risk of diastrophism otherwise.
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| 6. |
- Hussain, Syed Asad, et al.
(författare)
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Dissimilarity-driven ensemble model-based real-time optimization for control of building HVAC systems
- 2022
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Ingår i: Journal of Building Engineering. - : Elsevier BV. - 2352-7102. ; 52
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Tidskriftsartikel (refereegranskat)abstract
- Model-based real-time optimization (MRTO) is proven as an effective tool that can capture the complex dynamics of heating, ventilation, and air conditioning (HVAC) systems and improve its energy performance. Despite the energy benefits offered by MRTO, these approaches are rarely implemented in actual buildings. This is due to the reason that these approaches are very difficult to implement because they require the synthesis of a reliable and accurate performance model of the system. The reliability of decision-making with MRTO is directly related to the accuracy of these performance models. In addition, the model has to be computationally efficient for practical implementation. The development of such a model requires the most effort and is a major challenge in the implementation of MRTO. Several HVAC performance models are already available in the literature, and these can be classified as semiphysical models and data-driven models. The semiphysical models are generalized models with simplification assumptions that can provide consistent performance, however, with reduced accuracy. Contrastingly, the data-driven models can offer better accuracy; however, they lack robustness in terms of operational ranges. These factors affect the energy performance of MRTO, and an improper parametrized model could result in performance that is even worse than the conventional fixed setpoint or rule-based approaches. A dissimilarity-driven ensemble model-based real-time optimization (DEMRTO) approach is presented in this study that incorporates a dissimilarity-driven ensemble model in the framework of real-time optimization. The dissimilarity-driven ensemble model combines semiphysical models and data-driven models in a systematic manner to use one's strengths to address others' weaknesses, rather than developing a new form of a model. The performance of the proposed integrated approach was examined using case studies over three weather seasons in Hong Kong. The results showed as compared to the fixed setpoint approach the DEMRTO approach can provide significant energy savings up to 11.085% setpoint, and around 2.785% reduction in energy use as compared with the conventional MRTO approach. It was demonstrated that the proposed approach can capture diversity in load conditions and provide consistency in model prediction to improve reliability in decision-making with real-time optimization.
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| 7. |
- Joloudari, Javad Hassannataj, et al.
(författare)
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Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:6
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Tidskriftsartikel (refereegranskat)abstract
- Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.
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