Real-time navigation of mecanum wheel-based mobile robot in a dynamic environment.

Path planning and control of a mobile robot, in a dynamic environment, has been an important research topic for many years. In this paper an algorithm for autonomous motion of a mobile robot is proposed, with mecanum wheels, to reach a goal while avoiding obstacles through the shortest path in a dynamic environment. The proposed method uses a hybrid A⁎ and a velocity obstacle algorithms for path planning and obstacle avoidance. The A⁎ algorithm is implemented to explore the shortest path from starting position to the goal while avoiding all the static obstacles. However, in real time applications the dynamic obstacles need to be avoided, therefore, for such a case velocity obstacle algorithm is unified with the A⁎ algorithm. Initially, the proposed algorithm is verified through simulations. Then it is implemented using experimental setup in real time environment using single and multiple static obstacles as well as on a dynamic obstacle. It can be observed that the robot reaches the goal, effectively by avoiding static and dynamic obstacles. Moreover, the performance of the proposed work is evaluated through qualitative comparison between proposed method and recently published work, showing that the proposed algorithm is gives better features than existing work. In the end, the possible application of mobile robot having mecanum wheels with proposed path planning method is also given in the paper.

Novel design and performance evaluation of an indirectly forced convection desiccant integrated solar dryer for drying tomatoes in Pakistan.

The process of drying agricultural products for food preservation is a difficult task that requires a significant amount of energy. The increasing cost and depletion of fossil fuels have led to the development of a food dryer that utilizes renewable energy sources. This research paper proposes the design and performance evaluation of an indirectly forced convection desiccant integrated solar dryer (IFCDISD) at the Solar Energy Research Lab at USPCAS-E, NUST Pakistan. Tomatoes were chosen as the test product due to their importance and widespread consumption. The drying process involves slicing the tomatoes and placing them on the IFCDISD rack, where a desiccant called calcium chloride (CaCl2) is integrated into the dryer. The experiments were conducted during both sunshine (SS) hours and Off-sunshine (OSS) hours. The IFCDISD operates using sunlight during SS hours and utilizes the absorbed heat of CaCl2 in OSS hours via a forced DC brushless fan powered by battery charged thro solar panel. The tomatoes were weighed before and after each drying mode, and the moisture removal was calculated. The results show that the dryer efficiency was 50.14 % on day 1, 66 % on day 2, and an overall efficiency of 58.07 %. The moisture content removal was 42.858 % on day 1, 22.9979 % on day 2, and an overall moisture content removal of 58.07 %. Moreover, the payback period is 5.1396 and the carbon mitigation was recorded as 2.0335, and the earned carbon credit was recorded as 11559.6.

Comparative analysis of enriched flyash based cement-sand compressed bricks under various curing regimes.

This study investigates the substitution of traditional burnt clay bricks (BCB), used since 7000 BCE, with environmentally friendly Fly Ash-Cement and Sand Composite Bricks (FCBs), utilizing industrial waste like Coal Fly Ash (CFA) from thermal power plants. The research encompasses two phases: the first involves experimental production of FCBs, while the second focuses on optimizing FCBs by varying CFA (50%, 60%, 70%), Ordinary Portland Cement (OPC) content (9%-21%), and incorporating stone dust (SD) and fine sand. Comprehensive tests under normal and steam curing conditions, adhering to ASTM C 67-05 standards, include X-Ray Diffraction (XRD), Energy Dispersive X-Ray (EDX), and Scanning Electron Microscopy (SEM) analyses. Results indicate that steam curing enhances early strength, with an optimized mix (MD: 5S) achieving a compressive strength of 15.57 MPa, flexural strength of 0.67 MPa, water absorption rate of 20.08%, and initial rate of water absorption of 4.64 g/min per 30 in2, devoid of efflorescence. Notably, a 9% OPC and 50% CFA mix (MD: 1S) shows improved early strength of 4.95 MPa at 28 days. However, excessive CFA replacement (70%) with lesser cement content negatively impacts physio-mechanical properties. This research underscores the potential of FCBs as a sustainable and economically viable alternative to BCBs in the construction industry.

Multigrid simulations of non-Newtonian fluid flow and heat transfer in a ventilated square cavity with mixed convection and baffles.

The impact of baffles on a convective heat transfer of a non-Newtonian fluid is experimentally studied within a square cavity. The non-Newtonian fluid is pumped into the cavity through the inlet and subsequently departs from the cavity via the outlet. Given the inherent non-linearity of the model, a numerical technique has been selected as the method for obtaining the outcomes. Primarily, the governing equations within the two-dimensional domain have been discretized using the finite element method. For approximating velocity and pressure, we have employed the reliable P 2 - P 1 finite element pair, while for temperature, we have opted for the quadratic basis. To enhance convergence speed and accuracy, we employ the powerful multigrid approach. This study investigates how key parameters like Richardson number (Ri), Reynolds number (Re), and baffle gap b g influence heat transfer within a cavity comprising a non-Newtonian fluid. The baffle gap ( b g ) has been systematically altered within the range of 0.2-0.6, and for this research, three distinct power law indices have been selected namely: 0.5, 1.0, and 1.5. The primary outcomes of the investigation are illustrated through velocity profiles, streamlines, and isotherm visualizations. Furthermore, the study includes the computation of the Nu avg (average Nusselt number) across a range of parameter values. As the Richardson number (Ri) increases, Nu avg also rises, indicating that an increase in Ri results in augmented average heat transfer. Making the space between the baffles wider makes heat flow more intense. This, in turn, heats up more fluid within the cavity.

Interactive Pathology Tutorial in Neoplastic Hematology Disorders for Medical Hematology-Oncology Fellows.

Hematology-oncology (HO) fellows receive limited instruction in the process of establishing a diagnosis for hematologic neoplasms, and learning neoplastic hematology often occurs in limited encounters. In the current study, we developed a web-based interactive pathology tutorial in neoplastic hematologic disorders for HO fellows to work up simulated cases and establish the diagnosis. An online system ("Pathology Playground") was utilized to load case materials including microscopic images and ancillary studies. Twelve high-yield simulated cases of common leukemias and lymphoma were included. At the beginning of each case, trainees review the clinical history and slide images, and then, they are given the option to request additional pathology work-up. Based on the results, they can enter their diagnostic impression. If the diagnosis is correct, the user is shown a short educational presentation. If the diagnosis is not correct, the user gets notified by the message "Incorrect." The tutorial was integrated in the educational curriculum of our HO fellowship program, and bimonthly teaching sessions were held to review two cases each time. During the sessions, trainees request ancillary studies to complete the diagnostic work-up using the software and interpret the findings. As the case is being worked up by the trainee, the hematopathologists and HO fellowship program director discuss the findings, the appropriate work-up tools, and the implications on management. All of our six HO fellows attended the sessions, and a survey from the trainees showed high ease of use of the system and they viewed it as a very useful educational tool. A pre-test and post-test were administered for one of the sessions, and the result showed improvement in the average from 62 to 73%. Expanding the use of this online interactive tutorial and incorporating additional cases would enhance its value as a learning resource.

Up-scaling of fly ash-based geopolymer concrete to investigate the binary effect of locally available metakaolin with fly ash.

Owing to the increasing threat to environment due to the emission of greenhouse gases from cement industry globally, various promising solutions has been introduced in the past decades. The development of geoplymer concrete (GPC) is one of the contribution by the researches towards ecofriendly and sustainable construction. In this research, geopolymer concrete (GPC) is optimized by adding fixed amount of fly Ash (FA) and alkali activator to fine aggregate ratio as 0.5 with varying Molarity from 12 M to 16 M and Na2SiO3/NaOH ratio from 1.5 to 2.5. Physical and mechanical properties along with effect of heat and ambient curing conditions were investigated at various ages. The optimized mixture of fly ash based geopolymer concrete was then up scaled by blending with locally available Metakaolin (MK) with different dosages (i.e., 10%, 20%, 30%, 40%). The aim of the study is to identify the binary effect of FA and MK on overall performance of geopolymer concrete. Results showed that 30% FA-MK based GPC depicted 21%, 19% and 26% more compressive strength, split tensile strength and flexural strength respectively than Fly Ash based GPC alone at heat cured condition. This can be explained mainly due to two facts namely binary action of metakaolin that enhances compaction of GPC and pozzolanic activity of MK that expedite geopolymeric strength causing phases. The results were further verified by Modified Chapelle test and FTIR. Morphology of the developed GPC is also examined from SEM images. The work is an effort to utilize the fly ash produced by coal power plants to effectively address UN sustainable development goal related to sustainable cities and communities.

Development and characterization of non-proprietary ultra high performance concrete.

This paper presents the developmental process of ultra-high performance concrete (UHPC), the most advanced form of concrete. The entire process exclusively utilized locally available materials. The mixes were prepared without using any specialized mixer or treatments, such as elevated pressure, etc. The primary objective of the research was to develop low-cost non-proprietary version of UHPC by optimizing both cementitious and non-cementitious materials to attain the highest levels of workability, compressive strength, flexural strength and durability. The research utilizes a trial-and-error approach, subjecting specimens to curing in both regular and heated water. The findings validate the viability of producing self-compacting UHPC with compressive strength ranging from 120 to 160 MPa, employing local materials and manufacturing methods. Raw materials and mixing sequence had a significant influence on the fresh and hardened properties of UHPC. The inclusion of steel fibers and the application of heat treatment remarkably enhanced the compressive strength. Furthermore, cost analysis revealed that this particular UHPC is only slightly over four times more expensive than conventional concrete, in contrast to commercially available UHPC, which is approximately 10 times expensive than traditional concrete.

Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security.

Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios.

Hepatic Benign Cystic Mesothelioma in Adults: A Case Report of a Rare Hepatic Cyst.

Benign cystic mesothelioma (BCM), also known as peritoneal inclusion cyst, is a benign mesothelial lined cystic lesion, nearly always described in the pelvis of adult females. The hepatic location of BCM is rarely reported in the literature. We report a case of hepatic benign cysts in a 65-year-old woman that was incidentally discovered by imaging studies 12 years ago as a small cyst. Recently, the patient started having abdominal discomfort, distension, and anxiety. A CT scan revealed two low-density fluid-filled cystic lesions, the largest in the caudate lobe measuring up to 10.7 cm and causing a mass effect on hepatic veins and inferior vena cava. Laparoscopic marsupialization of the large liver cyst was done without complications. On gross examination, the collapsed cyst wall was a thin partly translucent pale tan to pink membranous structure with fine vascularity. No discrete nodularity or solid lesion was identified. Microscopic examination showed a thin fibro-connective wall lined by a single layer of flat cuboidal cells with no cellular atypia. The cyst lining showed characteristic calretinin-positive immunohistochemical reactivity for mesothelium, supporting the diagnosis of BCM. Hepatic BCM is among a broad differential spectrum of cystic liver lesions ranging from developmental, reactive, inflammatory, and infectious lesions, benign to premalignant or frankly malignant neoplasms with different treatment strategies. Although BCM is the rarest among the long list of differential diagnoses of hepatic cysts, its identification in this rarely reported location is essential to avoid aggressive surgical treatment.

Skeletal age evaluation using hand X-rays to determine growth problems.

A common clinical method for identifying anomalies in bone growth in infants and newborns is skeletal age estimation with X-ray images. Children's bone abnormalities can result from several conditions including wounds, infections, or tumors. One of the most frequent reasons for bone issues is that most youngsters are affected by the slow displacement of bones caused by pressure applied to the growth plates as youngsters develop. The growth plate can be harmed by a lack of blood supply, separation from other parts of the bone, or slight misalignment. Problems with the growth plate prevent bones from developing, cause joint distortion, and may cause permanent joint injury. A significant discrepancy between the chronological and assessed ages may indicate a growth problem because determining bone age represents the real level of growth. Therefore, skeletal age estimation is performed to look for endocrine disorders, genetic problems, and growth anomalies. To address the bone age assessment challenge, this study uses the Radiological Society of North America's Pediatric Bone Age Challenge dataset which contains 12,600 radiological images of the left hand of a patient that includes the gender and bone age information. A bone age evaluation system based on the hand skeleton guidelines is proposed in this study for the detection of hand bone maturation. The proposed approach is based on a customized convolutional neural network. For the calculation of the skeletal age, different data augmentation techniques are used; these techniques not only increase the dataset size but also impact the training of the model. The performance of the model is assessed against the Visual Geometry Group (VGG) model. Results demonstrate that the customized convolutional neural network (CNN) model outperforms the VGG model with 97% accuracy.

Advancing retinoblastoma detection based on binary arithmetic optimization and integrated features.

Retinoblastoma, the most prevalent pediatric intraocular malignancy, can cause vision loss in children and adults worldwide. Adults may develop uveal melanoma. It is a hazardous tumor that can expand swiftly and destroy the eye and surrounding tissue. Thus, early retinoblastoma screening in children is essential. This work isolated retinal tumor cells, which is its main contribution. Tumors were also staged and subtyped. The methods let ophthalmologists discover and forecast retinoblastoma malignancy early. The approach may prevent blindness in infants and adults. Experts in ophthalmology now have more tools because of their disposal and the revolution in deep learning techniques. There are three stages to the suggested approach, and they are pre-processing, segmenting, and classification. The tumor is isolated and labeled on the base picture using various image processing techniques in this approach. Median filtering is initially used to smooth the pictures. The suggested method's unique selling point is the incorporation of fused features, which result from combining those produced using deep learning models (DL) such as EfficientNet and CNN with those obtained by more conventional handmade feature extraction methods. Feature selection (FS) is carried out to enhance the performance of the suggested system further. Here, we present BAOA-S and BAOA-V, two binary variations of the newly introduced Arithmetic Optimization Algorithm (AOA), to perform feature selection. The malignancy and the tumor cells are categorized once they have been segmented. The suggested optimization method enhances the algorithm's parameters, making it well-suited to multimodal pictures taken with varying illness configurations. The proposed system raises the methods' accuracy, sensitivity, and specificity to 100, 99, and 99 percent, respectively. The proposed method is the most effective option and a viable alternative to existing solutions in the market.

Role of convolutional features and machine learning for predicting student academic performance from MOODLE data.

Predicting student performance automatically is of utmost importance, due to the substantial volume of data within educational databases. Educational data mining (EDM) devises techniques to uncover insights from data originating in educational settings. Artificial intelligence (AI) can mine educational data to predict student performance and provide measures to help students avoid failing and learn better. Learning platforms complement traditional learning settings by analyzing student performance, which can help reduce the chance of student failure. Existing methods for student performance prediction in educational data mining faced challenges such as limited accuracy, imbalanced data, and difficulties in feature engineering. These issues hindered effective adaptability and generalization across diverse educational contexts. This study proposes a machine learning-based system with deep convoluted features for the prediction of students' academic performance. The proposed framework is employed to predict student academic performance using balanced as well as, imbalanced datasets using the synthetic minority oversampling technique (SMOTE). In addition, the performance is also evaluated using the original and deep convoluted features. Experimental results indicate that the use of deep convoluted features provides improved prediction accuracy compared to original features. Results obtained using the extra tree classifier with convoluted features show the highest classification accuracy of 99.9%. In comparison with the state-of-the-art approaches, the proposed approach achieved higher performance. This research introduces a powerful AI-driven system for student performance prediction, offering substantial advancements in accuracy compared to existing approaches.

Magneto-bioconvection flow of Casson nanofluid configured by a rotating disk in the presence of gyrotatic microorganisms and Joule heating.

In this article, we investigate the bioconvection flow of Casson nanofluid by a rotating disk under the impacts of Joule heating, convective conditions, heat source/sink and gyrotactic microorganisms. When Brownian diffusion and thermophoretic effects exist, the Casson fluid is examined. The existing physical problem of Casson nanofluid flow with energy transports is demonstrated under the above considerations in the form of partial differential equations (PDEs). Using the appropriate transformations, the PDEs are converted into non-linear ordinary differential equations (ODEs). The mathematical results are calculated through MATLAB by using the function bvp4c. The problem's results are rigorously examined graphically and described with physical justifications. Velocity fields decrease as the bioconvection Rayleigh parameter rises. The thermal profile and soluteal field of species also magnify with an upsurge in thermophoresis number estimations. The microorganism's fields are decayed by larger microbes Biot number.

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification.

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

Entomopathogen-based biopesticides: insights into unraveling their potential in insect pest management.

Global food security is a critical challenge to fulfill the demands of an exponentially growing population. To date, growers rely on chemicals; the broad-spectrum application of synthetic molecules leads to environmental contamination, resistance development, residual toxicity, pest resurgence, and a detrimental effect on human health and cattle. Crop production needs to be improved considering environmental and human health concerns to ensure food security. Furthermore, economically important crops are prone to attack by insect pests, causing considerable yield losses. Microbes are an eco-friendly, versatile alternative, and a potential candidate for combatting destructive pests below the economic injury level and improving the plant's health and productivity. Several microbial pathogens, including parasites, predators, parasitoids, pollinators, and many beneficial microorganisms, possess toxic properties against target organisms but do not cause harm to the non-target organisms. Entomopathogens (ENMs) have great potential for pest suppression due to their remarkable properties. Bacteria are host-specific, but fungi have a broader host range and can be significantly affected by both soil-dwelling and terrestrial insect pests. Virulent pathogens cause mortality in target insect pests known as ENMs and can penetrate through natural openings, ingestions, and integuments to cause a possible effect on target insect pests. The objective of using ENMs is to sustain productivity, improve environmental health, reduce pesticides, and conserve natural resources. Moreover, research is ongoing to discover other possible aspects, especially exploring potential ENMs. Therefore, there is a need for identification, isolation, and bioformulation to overcome the existing issues. This study is mainly focused on the status of bio-formulations, pathogenicity, their mode of action, and the potential application of different types of microbial formulations for sustainable pest management.

IoT based smart home automation using blockchain and deep learning models.

For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automation systems address either of these challenges, however, home automation that also involves automated decision-making systems and systematic features apart from being reliable and safe is an absolute necessity. The current study proposes a deep learning-driven smart home system that integrates a Convolutional neural network (CNN) for automated decision-making such as classifying the device as "ON" and "OFF" based on its utilization at home. Additionally, to provide a decentralized, secure, and reliable mechanism to assure the authentication and identification of the IoT devices we integrated the emerging blockchain technology into this study. The proposed system is fundamentally comprised of a variety of sensors, a 5 V relay circuit, and Raspberry Pi which operates as a server and maintains the database of each device being used. Moreover, an android application is developed which communicates with the Raspberry Pi interface using the Apache server and HTTP web interface. The practicality of the proposed system for home automation is tested and evaluated in the lab and in real-time to ensure its efficacy. The current study also assures that the technology and hardware utilized in the proposed smart house system are inexpensive, widely available, and scalable. Furthermore, the need for a more comprehensive security and privacy model to be incorporated into the design phase of smart homes is highlighted by a discussion of the risks analysis' implications including cyber threats, hardware security, and cyber attacks. The experimental results emphasize the significance of the proposed system and validate its usability in the real world.

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods.

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R2), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

Predicting the performance of existing pre-cast concrete pipes using destructive and non-destructive testing techniques.

One of the most significant and critical urban assets for a sustainable community is the sewer pipeline network and water distribution system. Water sewer networks and distribution systems have a definite service life span to provide continuous facilities to end users. Therefore, it is pertinent to continuously evaluate the condition of water and sewer concrete pipelines to ensure the reliable, sustainable, and cost-efficient transport of water and sewerage for the safety of society. The condition assessment is commonly carried out by visual observations followed by some non-destructive testing methods. However, it is the need of the hour to shift assessment methods to advance assessment techniques to save time and money for our community. Currently, in this project, the condition assessment of pre-cast concrete pipes was carried out by destructive and non-destructive methods. Different test trials i.e., ultra-sonic pulse velocity, Schmidt hammer also known as rebound hammer test, visual inspection, three edge bearing test, and core cutting test on the old buried and new concrete pipes were performed. It was observed that concrete used for the construction of existing precast concrete pipes still has better quality indices after 20 years as compared to that of concrete of new pipes. However, steel has deteriorated with time and clear corrosion of steel was identified in existing pre-cast concrete pipes. At the same time, it was observed that there should be an automated mechanism to continuously asses the condition of pre-cast existing pipes which will address the sustainable development goals (SDG 6, 9, 11). Consequently, it can be said that condition assessment of pre-cast concrete pipes will lead to sustainable societies and infrastructure.

Wire coating analysis using Phan-Thien-Tanner fluid subject to Lorentz force as a polymer coating material in a canonical covering die.

Wire coating is widely used for electrical insulation to protect the wire from electric shock, prevent electrical leakage, and ensure that the electrical current flows smoothly. In this investigation, a pressurized coating die is used to explore the PTT fluid as a polymer material for wire in a magnetic field. The flow field, flow rate, temperature profile, thickness of the wire coating, volume flow rate, and shear stress are all given exact solutions. Graphs were used to illustrate the effects of certain important technical parameters, including flow rate, wire coating thickness, shear stress, and pressure gradient. It has been noted that as the values of X, Deborah number, and ratio of radii are improved, the volume and thickness of the coated wire rise. The Deborah number has a higher volume flow than the X and radii ratios. A reference to existing literature is made in order to support the validity of the current study.