Prediction of critical total drawdown in sand production from gas wells: Machine learning approach

Sand production is a critical issue in petroleum wells. The critical total drawdown (CTD) is an essential indicator of the onset of sand production. Although some models are available for CTD prediction, most of them are proven to lack accuracy or use commercial software. Furthermore, the previous correlations have not studied the trend analysis to verify the correct relationships between the parameters. Therefore, this study aims to build accurate and robust models for predicting CTD using response surface methodology (RSM) and support vector machine (SVM). The RSM is utilized to obtain the equation without using any software. The SVM model is an alternative method to predict the CTD with higher accuracy. This study used 23 datasets to develop the proposed models. The CTD is a strong function of the total vertical depth, cohesive strength, effective overburden vertical stress, and transit time with correlation coefficients (R) of 0.968, 0.963, 0.918, and −0.813. Different statistical methods, that is, analysis of variance (ANOVA), F-statistics test, fit statistics, and diagnostics plots, have shown that the RSM correlation has high accuracy and is more robust than correlations reported in the literature. Moreover, trend analysis has proven that the proposed models ideally follow the correct trend. The RSM correlation decreased the average absolute percent relative error (AAPRE) by 12.7% compared to all published correlations' AAPRE of 22.6%–30.4%. The SVM model has shown the lowest AAPRE of 6.1%, with the highest R of 0.995. The effects of all independent variables on the CTD are displayed in three-dimensional plots and showed significant interactions.     

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.     

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.     

A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet

In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents. The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA). Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach. The SAMMZWA approach embeds and detects the watermark logically without altering the original text document. The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, SAMMZWA has been implemented and validated with attacked Arabic text. Experiments were performed on four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks. The experimental results show that our method is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.     

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.     

Destabilisation of homogeneous bubbly flow in an annular gap bubble column

Experimental results are presented to show that there are very significant differences in the mean gas void fractions measured in an open tube and a annular gap bubble column, when operated at the same gas superficial velocity, using a porous sparger. Measurements were carried out in a vertical 0.102 m internal diameter column, with a range of concentric inner tubes to form an annular gap, giving diameter ratios from 0.25 to 0.69; gas superficial velocities in the range 0.014–0.200 m/s were investigated. The mean gas void fraction decreases with increasing ratio of the inner to outer diameter of the annular gap column and the transition to heterogeneous flow occurs at lower gas superficial velocities and lower void fractions. Two reasons are proposed and validated by experimental investigations: (1) the presence of the inner tube causes large bubbles to form near the sparger, which destabilise the homogeneous bubbly flow and reduce the mean void fraction; this was confirmed by deliberately injecting large bubbles into a homogeneous dispersion of smaller bubbles, and (2) the shape of the void fraction profiles changes with gap geometry and this affects the distribution parameter in the drift‐flux model. Both of these effects serve to reduce the mean gas void fraction in an annular gap bubble column compared to an open tube at the same gas superficial velocity.     

The effect of interfacial chemistry on coating adhesion and performance: A mechanistic study using aminobutylphosphonic acid

Failure of adhesion of organic coating (paints) to steel and other metallic substrates is one of the key mechanisms for local coating failure and for consequent restriction of coating lifetime. This study thus focuses on modification to the chemistry of the metal interface in order to promote enhanced (dry and wet) coating adhesion. The work uses an appropriate bi-functional amino alkyl phosphonate to provide the desired interfacial properties. The study uses X-ray photoelectron spectroscopy and water contact angle measurement to examine changes in the interfacial surface chemistry and electrochemical impedance spectroscopy and cathodic disbonding to determine improvements to the anti-corrosion performance. Choosing 4-amino-butyl-phosphonic acid as the surface modifier a significant influence on its efficiency as an adhesion promoter was observed as a function of pH. Thus, when the amino group was protonated at a pH of 5.3, the molecule demonstrated attachment to carbon steel at both ends with no significant improvement in performance. However at pH 8 the molecule demonstrated greatly improved surface packing density with the amino group outwards from the surface in the preferred orientation. In this condition, an epoxy coating demonstrated substantial resistance to interfacial hydrolysis with overall improved adhesion and reduced cathodic disbondment rate.     

Survival of Salmonella Typhi and Shigella dysenteriae in dehydrated infant formula

Abstract: Powdered infant formula has previously been linked to the transmission of various bacterial pathogens in infants resulting in life‐threatening disease and death. Survival studies of 2 common foodborne pathogens, Salmonella enterica serovar Typhi and Shigella dysenteriae, in powdered infant formula have not been previously studied despite the potentially devastating consequences from ingestion of these organisms, particularly by newborns, in case of a natural or deliberate contamination event. Therefore, to better predict the risk of S. Typhi and S. dysenteriae infection from consumption of infant formula, the present study was undertaken to determine survival of these microorganisms in dry infant formula under varying atmospheric conditions. A 2‐strain cocktail of S. Typhi and a 3‐strain cocktail of S. dysenteriae were stored for up to 12 wk in dehydrated infant formula in an ambient air or nitrogen atmosphere. Viable counts of S. Typhi at 12 wk in infant formula revealed a 2.9‐ and 1.69‐log decrease in ambient air and nitrogen atmosphere, respectively. Viable counts of S. dysenteriae at 12 wk in infant formula revealed a 0.81‐ and 0.42‐log decrease in ambient air and nitrogen atmosphere, respectively. These results show that S. Typhi and S. dysenteriae can remain viable for prolonged periods of time in powdered infant formula, and the presence of nitrogen enhances survival. Practical Application: Our goal in this work was to study the survival of S. Typhi and S. dysenteriae in dehydrated storage conditions in infant formula. This interest is partially generated by the possibility of using these 2 microorganisms to deliberately contaminate the food supply. The outcome of this study will help us to have a better idea how to respond and react to the risk of deliberate food contamination.     

Short Chain Fatty Acid Acetate Increases TNFα-Induced MCP-1 Production in Monocytic Cells via ACSL1/MAPK/NF-κB Axis

Short-chain fatty acid (SCFA) acetate, a byproduct of dietary fiber metabolism by gut bacteria, has multiple immunomodulatory functions. The anti-inflammatory role of acetate is well documented; however, its effect on monocyte chemoattractant protein-1 (MCP-1) production is unknown. Similarly, the comparative effect of SCFA on MCP-1 expression in monocytes and macrophages remains unclear. We investigated whether acetate modulates TNFα-mediated MCP-1/CCL2 production in monocytes/macrophages and, if so, by which mechanism(s). Monocytic cells were exposed to acetate with/without TNFα for 24 h, and MCP-1 expression was measured. Monocytes treated with acetate in combination with TNFα resulted in significantly greater MCP-1 production compared to TNFα treatment alone, indicating a synergistic effect. On the contrary, treatment with acetate in combination with TNFα suppressed MCP-1 production in macrophages. The synergistic upregulation of MCP-1 was mediated through the activation of long-chain fatty acyl-CoA synthetase 1 (ACSL1). However, the inhibition of other bioactive lipid enzymes [carnitine palmitoyltransferase I (CPT I) or serine palmitoyltransferase (SPT)] did not affect this synergy. Moreover, MCP-1 expression was significantly reduced by the inhibition of p38 MAPK, ERK1/2, and NF-κB signaling. The inhibition of ACSL1 attenuated the acetate/TNFα-mediated phosphorylation of p38 MAPK, ERK1/2, and NF-κB. Increased NF-κB/AP-1 activity, resulting from acetate/TNFα co-stimulation, was decreased by ACSL1 inhibition. In conclusion, this study demonstrates the proinflammatory effects of acetate on TNF-α-mediated MCP-1 production via the ACSL1/MAPK/NF-κB axis in monocytic cells, while a paradoxical effect was observed in THP-1-derived macrophages.