alexandria engineering journal最新文献

Blood testing is widely regarded as a fundamental diagnostic procedure in healthcare, with blood cell counting being particularly vital for diagnosing conditions and evaluating overall health. Traditional methods employing hemocytometers and diverse laboratory instruments are typically employed to conduct blood counts manually. Using a microscope, manually counting, and examining blood cells takes time and effort. Thus, developing autonomous blood cells detecting and counting cells in this system becomes necessary to help doctors diagnose patients quickly and accurately. The proposed method integrates the YOLOv5 object detection framework with Enhanced Graywolf Optimization (EGWO) and an Attention Mechanism to improve the accuracy and efficiency of RBC detection. The YOLOv5 architecture is optimized for the task through the EGWO algorithm, which fine-tunes the model parameters, while the Attention Mechanism focuses on extracting critical features from blood cell images. The model is trained and validated using the BCCD dataset, employing an 80/20 split for training and validation. Data augmentation techniques, such as random rotations, flips, and color adjustments, are applied to enhance model generalization. Experimental results demonstrate that our method achieves a high accuracy of 99.89 %, outperforming existing models like Faster R-CNN, CNN, and DCNN regarding accuracy, training time, and inference speed. The proposed method's moderate complexity and fast inference time make it suitable for real-time applications in clinical settings. This research provides a robust and efficient solution for automated RBC detection and counting, with potential implications for improving diagnostic processes in hematological analyses. Overall, in practical applications, it is helpful for counting red blood cells from smeared pictures in less than a second.

Leveraging deep learning (DL) to inverse problems has proven transformative in predicting financial futures, mainly in stock price prediction. In terms of financial markets, where predicting stock price is a challenging inverse problem, DL methods like long short-term memory networks (LSTM) and recurrent neural networks (RNN) demonstrate notable prowess. These techniques successfully capture complex patterns and dependencies in historical stock price information, learning to discern subtle correlations and fundamental market trends. DL facilitates the extraction of nuanced features by leveraging large datasets and sophisticated techniques, enabling accurate prediction of future stock prices. This intersection of advanced ML approaches and financial predicting signifies a paradigm shift in leveraging technology to navigate the complexity of the financial market and improves decision-making for traders and investors. This manuscript introduces a Dung Beetle Optimization with a Deep Learning Approach for Solving Inverse Problems in Predicting Financial Futures (DBODL-SIPPFF) technique. The DBODL-SIPPFF technique resolves the inverse problem and predicts the stock prices adequately. In addition, the DBODL-SIPPFF technique exploits data consistency (DC) optimizer to solve inverse problems. Primarily, linear scaling normalization (LSN) is applied for data normalization. The honey badger algorithm (HBA) is utilized for the feature selection. Moreover, convolutional long short-term memory (ConvLSTM) is used for stock price prediction. Lastly, the DBO technique is utilized for the optimum hyperparameter selection of the ConvLSTM model. The empirical assessment of the DBODL-SIPPFF technique takes place under diverse stock datasets. The obtained values stated that the DBODL-SIPPFF technique performs optimally over compared methods.

In this article, we propose a new class of nonlinear fractional differential equations of diabetes disease based on the concept of Caputo fractional derivative. Two numerical techniques are introduced to analyze the solution of the general fractional diabetes model, that describes glucose homeostasis. The first method, which constructed using the nonstandard finite difference technique involves an asymptotically stable difference scheme. This method maintains important properties of the solutions of the studied system, such as the positivity and boundedness. The second method is the Jacobi–Gauss–Lobatto spectral collocation approach, known for its exponential accuracy. By employing this collocation method, the problem is transformed into a set of algebraic nonlinear equations, simplifying the overall task. Numerical simulations were conducted to compare the performance of these two techniques with other standard methods and the analytic solution in specific cases. Our findings show that the Jacobi–Gauss–Lobatto spectral collocation technique provides higher accuracy in solving the fractional diabetes model system, while the nonstandard finite difference approach requires lower computational duration.

This paper proposes an intelligent faculty evaluation and ranking system in a fuzzy environment, with a focus on semester-wise evaluation rather than annual evaluation. This approach is warranted because a university’s academic goals may vary across semesters, affecting the selection and weights of performance indicators related to teaching effectiveness, research output, and official responsibilities. To achieve this objective, the authors introduce the concept of N-framed plithogenic hypersoft set (PHSS), where N represents the number of frames or semesters. Three types of N-framed PHSS are introduced, and an efficient rank reversal-free multi-criteria decision-making technique, namely NR-TOPSIS, is extended by embedding N-framed PHSS in the algorithm of NR-TOPSIS, termed as ENR-TOPSIS. The modified algorithm is capable of extracting data from a source file and producing the desired results. The procedure is implemented for faculty evaluation in a double-framed fuzzy environment, and sensitivity analysis is performed for the proposed ENR-TOPSIS. The developed framework combines intelligent systems that are adaptable and contain additional characteristics, making it more versatile and increasing its accuracy and transparency, in line with SDG-4, which focuses on quality education.

Cliffside carving images are often affected by various types of noise, such as uneven lighting, shadows, dust, and weathering, which impair the clarity and detail of the images. These noise factors significantly impact image quality, making effective denoising crucial. Denoising can enhance the clarity and quality of cliffside carving images, facilitating the study of their artistic style, historical background, and cultural significance. Therefore, this paper proposes the Complementary Transformer Network (CoTrNet), which utilizes an encoding-decoding framework to denoise cliffside carving images. The Diverse Feature Complementary Module (DFCM) is employed for feature extraction and image reconstruction, while the Skip Connection Cross Transformer (SCCT) enhances the transfer of low-level features to higher levels, improving the overall denoising effect. CoTrNet accurately captures the details and features in the images, significantly reducing noise. Using images from Tongtian Rock in Ganzhou, China, experiments show that CoTrNet outperforms existing techniques, achieving Peak Signal-to-Noise Ratios (PSNR) of 27.5706 and 24.9113 at noise levels of 15% and 25%, respectively. This research provides a powerful tool for the preservation, restoration, and conservation of cliffside carving cultural heritage.

Detection of cancer antigen 125 in biological samples is crucial due to its role as a biomarker in the early diagnosis, monitoring, and management of certain cancers, particularly ovarian cancer. This research involved the synthesis and application of an oriented Mn-doped ZnO nanorods composite as a substrate for the development of a biosensor platform to detect ovarian cancer biomarker cancer antigen 125. The synthesized substrate was characterized using various methods. An indium tin oxide electrode modified with Mn-doped ZnO nanorods was used to manufacture a label-free electrochemical aptasensor. The developed assay illustrated a broad linear range from 0.002 ng mL⁻¹ to 3 ng mL⁻¹ with a correlation coefficient of 0.9974 and a low limit of detection (0.5 pg mL⁻¹) based on S/N =3. The biosensor was demonstrated satisfactory selectivity when exposed to various common interfering species. The aptsensor also illustrated acceptable reproducibility and high storage stability. Additionally, the potential of this method for early cancer diagnosis was demonstrated by testing the assay on human serum samples, which yielded acceptable recovery values ranging from 97.5 % to 105.0 %.

In this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM.

This paper explores the impact of MHD couple stress and slip velocity over a conical bearing in the presence of magnetic field. The flow is considered as incompressible. A uniform magnetic field is imposed perpendicular to the bearing along$y$-axis. The modified Reynolds equation is solved using appropriate boundary conditions in dimensionless form to find the pressure distribution which is then used to obtain the expression for load carrying capacity paving the way for the calculation of response time. Numerical computations of the results show that the MHD couple stress on the conical bearings indicates that enhances the pressure distribution, load carrying capacity and squeeze film time of the conical bearings. Further, decrease in these characteristics was observed for the increased values of slip velocity and cone angle.

This work introduces two (3+1)-dimensional expansions of the Korteweg–de Vries (KdV) and modified KdV (mKdV) equations. These extensions incorporate a second-order time-derivative term, similar to the Boussinesq equation. The Painlevé test is utilized to verify the integrability of each extended model. The bilinear form is employed to investigate the existence of multiple-soliton (MS) solutions for each system under consideration. Furthermore, we provide solutions in the form of lumps for the extended KdV equation. The multidimensional KdV-type equations surpass the standard KdV equation. However, they offer enhanced accuracy by representing a broader spectrum of nonlinear phenomena in plasma physics, fluid mechanics, tsunami phenomena, and other science disciplines. Furthermore, the aforementioned equations can be employed to analyze the characteristics of various acoustic waves (AW) in different plasma models, such as their amplitude, width, frequency, and dispersion, as well as the phase shifts after collisions.

Flow around a solid sphere finds utility in numerous single- and two-phase engineering applications, such as sport balls, combustion systems, silt conveyance in waterways, hydraulic conveying, pneumatic equipment, food and chemical manufacturing. Therefore, this paper aims to examine the Casson nanofluids flow and heat transfer over a solid sphere that is saturated in an isotropic porous material in the presence of Stefan blowing and slip conditions. The forced situation is due to the presence of a stagnation point while the surface of the sphere is subjected to thermal slip conditions. Besides, various significant impacts are taken into account such as Lorentz force, thermal radiation, heat source/sink, and activation energy. The solution technique is based on non-similar transformations and implicit finite difference method with the Blottner algorithm. It is remarkable that, for all values of the activation parameter, the growth of Stefan number reduces the gradients of the velocity, temperature, and nanoparticle concentration. Also, the presence of the thermal slip factor reduces the temperature distributions. Additionaly, an increase in either the Casson parameter or Darcy number enhances the flow while both temperature and concentration are diminishing. Furthermore, there is an improvement in values of the Nusselt number up to 50.57 % when the magnetic parameter is varied from 0 to 6.