The new era of distance education opens up a new problem space for researchers to redefine educational needs. The COVID-19 lockdown with its tremendous consequences has affected the way of thinking and acting and has highlighted the merits of distance education. One of the most important of these problems is the inability of the lecturer technically to manage and present remote lectures, which resulted in the student's dissatisfaction with distance education. In this research, we have studied how to facilitate the work of the lecturer, who suffers from many problems, including the technical issues that need technical competencies in the field of information technology and modern technologies used in distance education, including the simulation system and displaying the system via the Internet, studying how to help the student also, in receiving distance education techniques and dealing with the programs used by the lecturer in skillfully presenting educational curricula. For that, the research focused on studying how to use a team to facilitate this work called the E-Learning Facilitator Team. This team plays a vital role in developing and maintaining an online program that is effective, and smooth, and that will support the realization of the planned learning outcomes. Faculty delivering courses online must be more than transmitters of knowledge; they must become facilitators of learning. Some highly seasoned instructors from the traditional on-ground environment will quickly adapt to the online model, while others may find the transition challenging at first [10].

The electrospun yarn for sutures has gained worldwide attention due to its fine fibers that resemble the extracellular matrix and its abundant functional sites. However, the use of a large number of toxic solvents poses safety risks and environmental pollution during production, making it challenging to directly apply electrospun yarn in the biomedical field. In this study, an environmentally friendly and safe method without toxic solvents was proposed for preparing antibacterial PLA ultrafine fiber sutures. This method involves melt electrospinning, hot‐stretching, low‐temperature plasma treatment, and chitosan grafting. The PLA ultrafine fiber sutures exhibit a high tensile strength which is 2.04 N before knotting and 1.57 N after knotting. The suture diameter is 118.7 μm and average fiber diameter is 1.72 μm. Chitosan grafted on the fiber surface provides excellent antibacterial properties for the sutures, with …

An optimized image watermarking technique based on LU factorization and entropy analysis in combination with lifting wavelet transform and discrete cosine transform is presented in this paper. At first, the original image is decomposed by a 2-level lifting wavelet transform for obtaining the coefficients of a high-frequency subband followed by discrete cosine transform. Afterward, non-overlapping blocks are obtained by dividing the coefficients of discrete cosine transform whereas LU factorization is applied to each nonoverlapping blocks based on pseudo-random sequences. Then, the watermark is embedded into the first row, the first column element of the upper triangular matrix of LU factorization. The normalized cross-correlation (NC), and the peak signal-to-noise ratio (PSNR) are used to evaluate the invisibility and robustness of the presented technique. The experimental results have indicated that the presented technique fulfills all watermarking requirements in terms of invisibility, robustness, security, and capacity. The comparison with the existing scheme has shown that the proposed watermarking technique has a superior performance in terms of invisibility than the existing scheme.

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ABSTRACT:- The use of technologies from many fields, such as mass spectrometry, next-generation sequencing, or image processing, is common in experiments in the life sciences. Complex scripts are frequently used to govern data flow, data transformation, and statistical analysis when passing data between such tools. Such scripts not only tend to be platform dependant, but also tend to expand as the experiment goes on and are rarely clearly documented, which makes the experiment harder to reproduce. Workflow systems like KNIME Analytics Platform, which offers a platform for graphically linking tools and ensures the same results across various operating systems, aim to address these issues. systems that are frequently employed in the biological sciences and describe how they compare and contrast with KNIME. KNIME is an open source program that enables programmers and scientists to share their own extensions with the scientific community. The unified data model of KNIME allows for interoperability, and we describe a few additions from the life sciences that make it easier to explore, analyze, and visualize data. In addition, we mention additional workflow. According to the American Cancer Society, skin cancer is the most prevalent form of malignancy in humans. It is typically identified visually, with first clinical screenings, dermoscopic (skin-related) analysis, a biopsy, and histological examinations as potential follow-up steps. Errors (mutations) in the DNA of skin cells are the cause of skin cancer. The cells proliferate out of control and aggregate into a mass of cancer cells as a result of the mutations. In this paper, convolutional neural networks are used to attempt to categorize photos of skin lesions. The deep neural networks demonstrate enormous potential for classifying images while taking into account the extreme environmental heterogeneity. Due to the current state of technology, it is imperative to use machines rather than people to address the widespread problem of skin cancer. One of the best ways to address skin cancer issues is deep learning. Huge data, virtual reality, augmented reality, and miniservices are all used in the new research area of deep learning in contemporary technology. The advent of powerful arithmetic capabilities enabled deep learning applications using Mobile net (CNN) to revolutionize image classification. The various forms of skin cancer can be categorized using deep learning. Transfer Learning was used during the training on many models. The model's best level of accuracy was over 77.333 %. To guarantee the validity and reproducibility of the aforementioned result, the dataset employed is openly accessible.

Keywords: Artificial Intelligence (AI), Convolutional Neural Networks (CNNs), Deep Learning (DL), Skin Cancer, Image Classification, Knime Analytics Platform Software etc.

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