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Seawater Desalination uses hydrophobic membranes. Many techniques have been developed to improve membrane hydrophobicity by depositing particles on the membrane surface. In this study, a nanocomposite membrane utilizing Triethylenetetramine (TETA) is suggested. The membrane incorporates Multiwalled Carbon Nanotubes (MWCNT) in conjunction with Triethylenetetramine Dihydrochloride (TETA-DH). In water bath, different percentages of TETA-DH films are formed. Electro-spin fabrication of MWCNTs using TETA-DH yields smooth, low-pore membranes. Membranes and their characteristics are identified by contact angle, layer thickness, and conductivity measurements. Membrane performance is examined for heat flux and salt rejection. Compared to commercial membranes, the proposed membrane exhibits superior antifouling and anti-wetting features. The membrane exhibited permeation and rejection ratio of 46 Kg m2 h−1 and 99.99% respectively which is superior as compared with other membranes. The hydrophilic Surface Modifying Macromolecules percentage (LSMM%) is evaluated because it depends on average pore size, hydrophobicity, surface porosity, and shape. The tests demonstrate the excellent performance of the proposed membrane for controlling membrane fouling.

Today, various applications in different domains increasingly rely on high-performance computing (HPC) to accomplish computations swiftly. Integrating one or more programming models alongside the used programming language enhances system parallelism, thereby improving its performance. However, this integration can introduce runtime errors such as race conditions, deadlocks, or livelocks. Some of these errors may go undetected using conventional testing techniques, necessitating the exploration of additional methods for enhanced reliability. Formal methods, such as temporal logic, can be useful for detecting runtime errors since they have been widely used in real-time systems. Additionally, many software systems must adhere to temporal properties to ensure correct functionality. Temporal logics indeed serve as a formal frame that takes into account the temporal aspect when describing changes in elements or states over time. This paper proposes a temporal-logic-based testing tool utilizing instrumentation techniques designed for a dual-level programming model, namely, Message Passing Interface (MPI) and Open Multi-Processing (OpenMP), integrated with the C++ programming language. After a comprehensive study of temporal logic types, we found and proved that linear temporal logic is well suited as the foundation for our tool. Notably, while the tool is currently in development, our approach is poised to effectively address the highlighted examples of runtime errors by the proposed solution. This paper thoroughly explores various types and operators of temporal logic to inform the design of the testing tool based on temporal properties, aiming for a robust and reliable system. [ABSTRACT FROM AUTHOR]

Blind image deblurring aims to recover an output latent image and a blur kernel from a given blurred image. Kernel estimation is a significant step in blind image deblurring and requires a regularization technique to minimize the cost function and the edges of objects to generate a sharp image in a better way. This study proposes a new image regularization technique called Weber's Law Regularization (WLR) based on the Weber law phenomenon. The Weber ratio was used to preserve the edges of small salient objects and to minimize the cost function to obtain a sharp image while minimizing the ringing effect. To validate the WLR, experiments were conducted on benchmark synthetic and real word images and compared with existing state-of-the-art methods. The experimental results showed that WLR can effectively and efficiently deblur images even in the absence of prior knowledge. [ABSTRACT FROM AUTHOR]

This paper presents a skin cancer classification model that combines a pre-trained Convolutional Neural Network (CNN) with a nature-inspired feature optimization algorithm. A custom dataset comprising both malignant and benign skin cancer microscopic illustrations is derived from the ISIC dataset of dermoscopic images. Several preprocessing steps are performed on the input pictures, such as histogram equalization, gamma correction, and white balance adjustment, to improve visibility, quality, and make color corrections. Deep feature extraction and pattern recognition are conducted on both enhanced and original dataset images using the pre-trained CNN model EfficientNetB0. As a result of fusing these features, the model can capture rich details from both dataset versions at the same time. Ant Colony Optimization (ACO), a nature-inspired feature selection algorithm is applied to perform model optimization by keeping the most relevant features and discarding the unnecessary ones. The optimized feature vector is then used with various SVM classifier kernels for the skin cancer classification task. The maximum achieved accuracy of the proposed model exceeded 98% through CB-SVM while maintaining an excellent prediction speed and reduced training time. [ABSTRACT FROM AUTHOR]