Mengyuan Xu, Jingxuan Han, Shangheng Yang, Guiyang Mo, Wen Li |
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| Research status and development trend of simulation experiments on the formation of underground crack sealing layer | ||||||
This paper provides an overview of the issue of drilling fluid leakage and strategies to address it. The focus is on
preventing borehole wall collapse and ensuring well wall stability in complex geological environments through
the use of advanced plugging technologies and materials. The research analyses drilling and plugging materials
suitable for high-temperature formations, adaptive leakage prevention and plugging technologies, and
innovative applications of micro and nano technologies to enhance the effectiveness of drilling fluid plugging. It
also examines the unique effects of different plugging technologies in forming a plugging layer. The research
results suggest that numerical simulation is crucial in studying oilfield fracture plugging technology............
plugging technology, drilling fluids, plug layers, plugging particles, wellbore, plugging agents
[1]. XU Tongtai, LIU Yujie, SHIN Wei. Leakage Prevention and Plugging Technology in Drilling Engineering [M]. Beijing: Petroleum Industry Press, 1997. [2]. Sun Jinsheng, Wang Shiguo, Zhang Yi, et al. Research on film-forming technology of water-based drilling fluids[J]. Drilling and Completion Fluids, 2003, 20(6): 6-10. [3]. Al-saba M., Nygaard R., Saasen A., et al. Laboratory evaluation of sealing wide fractures using conventional lost circulation materials[C]. SPE170576, 2014. [4]. Alshubbar G., Nygaard R., Jeennakorn M. The effect of wellbore circulation on building an LCM bridge at the fracture aperture[J]. Journal of Petroleum Science and Engineering, 2018, 165: 550-556. [5]. Jeennakorn M., Nygaard R., Nes O., et al. Testing conditions make a difference when testing LCM[J]. Journal of Natural Gas Science and Engineering, 2017, 46: 375-386.
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N Manasa, D Sravya, S Rakesh |
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| Deep Convoluational Neural Networks for Malaria Cell Identification | ||||||
Malaria is one of the deadliest diseases across the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe. Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting plasmodium parasites. The custom convoluational neural network is primarily designed to distinguish between healthy and infected blood samples........
Malaria Erythrocyte, Peripheral blood smear, Digital image processing, Deep learning Convoluational neural networks.
[1]. World malaria report 2019. Geneva: World Health Organization; 2019.
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Julio Cézar de Almeida, Leomar Gomes Júnior |
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| The Cauchy Stress Tensor – Calculation Parameters Via Matlab -A Review | ||||||
The presence of 2nd order tensors in problems in the broad area of Continuum Mechanics corresponds to a very common practice. In a general context, a solid body when subjected to external loads presents corresponding deformations, and the relationship between the acting stresses and the generated deformations depends on the constitutive relationship of the material considered. Metallic materials, in general, can be classified and grouped as isotropic materials. Within this entire context, the Cauchy stress tensor stands out, from which various calculations and analyzes can be developed. The main objective of this work is to present a general review of the calculation parameters that can be carried out using the tensor. Also noteworthy is the development of a simplified computational code, in Matlab language, which allows obtaining and analyzing the main calculation parameters to be considered based on the Cauchy stress tensor.
....
[1]. Boresi, A. P., Chong, K. P., 2000. Elasticity in Engineering Mechanics, Prentice Hall, Inc.
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Mr. Nilesh D. Mhaiskar, Veeramalla Srikanth, Chattala Teja Varsha, Jambula Archana Reddy |
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| Malware Detection Systems on Android Platforms Using Genetic Algorithm | ||||||
This study presents an innovative approach for enhancing Android malware detection through a Genetic Algorithm (GA)-based optimized feature selection coupled with machine learning techniques. Leveraging the evolutionary principles of GA, the proposed method effectively identifies a subset of features from a large pool, maximizing the discriminative power while minimizing computational complexity. By integrating this feature selection mechanism with machine learning classifiers, the system achieves superior performance in distinguishing between, benign and malicious Android applications.......
Genetic Algorithm, Machine Learning, Android Malware, Feature Selection Mechanism, Accuracy.
[1]. H. Rathore, A. Nandanwar, S. K. Sahay, and M. Sewak, ''Adversarialsuperiority in Android malware detection: Lessons from reinforcementlearning based evasion attacks and defenses,'' Forensic Sci. Int., Digit. Invest., vol. 44, Mar. 2023, Art. no. 301511. [2]. H. Wang, W. Zhang, and H. He, ''You are what the permissions told me! Android malware detection based on hybrid tactics,'' J. Inf. Secur. Appl., vol. 66, May 2022, Art. no. 103159. [3]. A. Taha and O. Barukab, ''Android malwareclassification using optimizedensemble learning based on genetic algorithms,'' Sustainability, vol. 14,no. 21, p. 14406, Nov. 2022. [4]. O. N. Elayan and A. M. Mustafa, ''Android malware detectionusing deep learning,'' Proc. Comput. Sci., vol. 184, pp. 847–852,Jan. 2021. [5]. J. Kim, Y. Ban, E. Ko, H. Cho, and J. H. Yi, ''MAPAS: A practical deep learning-based Android malware detection system,'' Int. J. Inf. Secur. Vol. 21, no. 4, pp. 725–738, Aug. 2022
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