Zelalem Meraf

A novel pressurized flow system for circular thin-layer chromatography (PC-TLC) has been successfully established and employed for the separation of amino acids, dyes, and pigments for safe medical imaging applications. In this system, the mobile phase is applied to a regular TLC plate through the tube and needle of an intravenous infusion set. The needle was fused in a hole underneath the center of the plate, while the second side end of the tube was connected to a microburette containing the solvent. This new assembly proved itself better in terms of separation time (within 5minutes) and controlled flow of the solvent and horizontal movement of analyte components over chromatograms with better separation and R_f values (glutamine: 0.26, valine: 0.44, phenylalanine: 0.60, chlorophyll a: 0.52, chlorophyll b: 0.43, xanthophyll: 0.18, carotenoid: 0.97, and pheophytin: 0.60) when a number of samples of amino acids, dyes, and pigments were separated by the developed apparatus and the conventional TLC procedure. The developed method was found distinctly rapid, precise, and eco-friendly (less solvent consuming) as compared to traditional ascending TLC.

Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. Experts usually perform this detection process manually, but there is interest in automating it to speed up the diagnosis and monitoring of this disease. A variety of automatic image segmentation methods have been proposed to quickly detect these lesions. A Gaussian Mixture Model is first constructed to identify outliers in each image. Then, using a set of rules based on expert knowledge of multiple sclerosis lesions, those outliers of the model that do not match the lesions' characteristics are discarded. Furthermore, segmented lesions usually correspond to gray matter-rich brain regions. In some cases, false positives can be detected, but the rules used cannot eliminate all errors without jeopardizing the segmentations quality. The second method involves training a convolutional neural network (CNN) that can segment lesions based on a set of training images. This technique can learn a set of filters that, when applied to small sections of an image called patches, produce a set of characteristics that can be used to classify each voxel of the image as a lesion or healthy tissue. On the other hand, the results show that the networks are capable of producing results in the worked database comparable to those produced by the algorithms in the literature.

The usage of dyes has been tremendously augmented due to industrialization and human’s intrinsic fascination with colors. Owing to their excessive usage in industries like textiles, food, cosmetics, paints, printing etc., it is indisputably a contributing factor in aquatic pollution. Dyes effluents have emerged as a burgeoning challenge. Owing to issues such as toxicity, mutagenicity, and disturbed photosynthesis associated with dye contamination, it is crucial to look for an explication to deal with this challenge. Polypyrrole-based biocomposites have been reported as good adsorbents for textile wastewater treatment. In the last decade, numerous studies have stated the effective removal of dyes via Polypyrrole-based biocomposites. This review concentrates on the implication of different Polypyrrole-based biocomposites for decontamination of dyes and synthesis methods, characteristics, and mechanism of dyes degradation by these biocomposites from wastewater.

Technologies are increasingly independent and play important roles in society. Artificial intelligence (AI) is a branch of science that can improve various environments and processes. The health sector stands out among these contexts, especially ophthalmology and dentistry. Studies evaluating the impact of using these technologies in these contexts are still developing. There are still few studies that assess how AI can impact the decision‐making process of health professionals and how it can improve the quality of care provided to these professionals. In this sense, this study aims to evaluate the perception of the impact of AI on the decision‐making process of health professionals and the quality of patient care from the perspective of ophthalmologists and dentists. The methodological strategy used was the application of an online questionnaire with eighteen professionals in these areas. Based on the respondents’ opinions, we sought to assess how these decision‐making processes are affected by the use of technologies and how they impact the quality of patient care. As a result, it was observed that AI has become essential and a facilitator of the diagnostic processes. However, it presents some challenges related to cost, accessibility, AI x professional responsibility, and incentive of agreements.

A smart, environmentally friendly superabsorbent polymer was prepared using solvents. It was polymerization on the microwave rays at a medium capacity and for 25 minutes, where the yellow gel was obtained. The polymer was cut and washed with absolute ethanol and methanol and was dried at a temperature of 60°C. Then, the polymer was extracted and milled with ceramic fat until obtaining a very soft powder, and the tests were taken as a formula for a scanning electron microscope and infrared spectrum. The results showed that the absorption value of polymer is at the equivalent acid function, where the absorption capacity was 467.32 grams. At room temperature, the water retention rate was 71%, and at 50°C, it was 52%, and the gel content was 90%. The results showed an improvement in the properties of the gypsum soil in terms of virtual density, porous, and acidic function, reaching 7.3%. The proportion of significant elements (P, N, Ca, K, Na) and moisture content in the soil was 64%, the cumulative tip amount and the consistency of soil granules through wet and dry palm, penetration resistance, electrical conductivity 4 ms, and organic material content were as follows, and the results were very high.

Schistosoma mansoniis one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image‐based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. However, the SPNN proved to be faster than the CNN.

Melanoma skin cancer is a fatal illness. However, most melanomas can be treated with minimal surgery if found early. In this regard, the addition of image analysis techniques that automate skin cancer diagnosis would support and increase dermatologists’ diagnosis accuracy. As a result, enhanced melanoma detection can benefit patients who are showing indicators of the disease. Convolutional neural networks can learn from features hierarchically. Since the implementation of a neural network requires a large volume of images to achieve high accuracy rates, an insufficient number of skin cancer images represent an additional challenge in the detection of skin lesions; the current work aims to develop an intelligent system that allows, based on the analysis of images of skin lesions and contextual information of the patient, to accurately determine if it represents a case of melanoma‐type skin cancer. The TensorFlow library was used to execute models in the constructed app. The Mobilenet V2 model was used with a collection of 305 pictures retrieved from the Internet. Diagnoses included melanoma, plaque and psoriatic skin conditions, and Kaposi’s sarcoma and atopic dermatitis. There were two separate machines used to conduct the application tests. There was more than 75% acceptable performance in predicting Kaposi’s sarcoma‐like illnesses for melanoma‐like lesions, as well as plaque psoriasis and atopic dermatitis, respectively. Despite the low amount of images used in training, the constructed mobile application performed well.

Cancer is a noncommunicable chronic disease that indistinctly affects people of any nationality, race, ethnicity, age, or social class. Because of its unpredictability, receiving the diagnosis of this disease is almost always alarming for the patient. Breast cancer, especially among women, occupies a prominent position in this ranking. However, if diagnosed early, there is an excellent chance of a cure. In this sense, digital technologies have been advancing at an increasingly fast pace to support the early diagnosis of the disease. Clinical analysis of breast cancer is commonly performed using diagnostic imaging. One of the most used exams considered the main one for the early detection of this type of cancer is mammography. This exam allows the visualization of breast tissue from image screening using X‐rays. In this sense, the use of computational techniques is essential to assist medical professionals in diagnosing this disease, thus making prevention and early diagnosis even more effective in the current context. The present work is limited to the use of digital technologies (image processing and artificial intelligence) that cooperate with the early diagnosis of breast cancer, which supports the medical professional to analyze images and be able to diagnose, from an early stage, the emergence of breast cancer of the disease, significantly increasing the chances of curing it. It can be gathered from this research how the discovery of X‐rays and the growth in this sector combined with cutting‐edge technology have benefitted in the early detection of the disease and even offered the cure of many cases.

Artificial intelligence (AI) is a subfield of computer science concerned with developing intelligent machines capable of performing tasks similar to those performed by humans. This human‐created intelligence began more than 60 years ago. The goal of previous generations of applications was to demonstrate generic human‐like behaviour. The goal has expanded with the advancement and increased compliance of this technology. It includes areas such as healthcare, gaming, and smart devices. The COVID‐19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses and clinicians who have had to shift delivery modes quickly. In this study, we have conducted a systematic literature review (SLR) to provide an overview of the current state of the literature related to software measurement of healthcare using artificial intelligence. The study followed a secondary research strategy. The systematic literature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initial searches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the healthcare sector to bring efficiency to the systems. The research concluded that the healthcare setting crucially requires the implementation of software automation.

In this study, a novel 7D hyperchaotic model is constructed from the 6D Lorenz model via the nonlinear feedback control technique. The proposed model has an only unstable origin point. Thus, it is categorized as a model with self‐excited attractors. And it has seven equations which include 19 terms, four of which are quadratic nonlinearities. Various important features of the novel model are analyzed, including equilibria points, stability, and Lyapunov exponents. The numerical simulation shows that the new class exhibits dynamical behaviors such as chaotic and hyperchaotic. This paper also presents the hybrid synchronization for a novel model via Lyapunov stability theory.