MATLAB Algorithm Research Articles

An Image Processing Algorithm for Facile and Reproducible Quantification of Vomocytosis.

Vomocytosis is a process that occurs when internalized fungal pathogens escape from phagocytes without compromising the viability of the pathogen and the host cell. Manual quantification of time-lapse microscopy videos is currently used as the standard to study pathogen behavior and vomocytosis incidence. However, human-driven quantification of vomocytosis (and the closely related phenomenon, exocytosis) is incredibly burdensome, especially when a large volume of cells and interactions needs to be analyzed. In this study, we designed a MATLAB algorithm that measures the extent of colocalization between the phagocyte and fungal cell (Cryptococcus neoformans; CN) and rapidly reports the occurrence of vomocytosis in a high throughput manner. Our code processes multichannel, time-lapse microscopy videos of cocultured CN and immune cells that have each been fluorescently stained with unique dyes and provides quantitative readouts of the spatiotemporally dynamic process that is vomocytosis. This study also explored metrics, such as the rate of change of pathogen colocalization with the host cell, that could potentially be used to predict vomocytosis occurrence based on the quantitative data collected. Ultimately, the algorithm quantifies vomocytosis events and reduces the time for video analysis from over 1 h to just 10 min, a reduction in labor of 83%, while simultaneously minimizing human error. This tool significantly minimizes the vomocytosis analysis pipeline, accelerates our ability to elucidate unstudied aspects of this phenomenon, and expedites our ability to characterize CN strains for the study of their epidemiology and virulence.

Scripts to Insert Cohesive Elements at the Interfaces between Matrix and Precipitates with Irregular Shapes in Representative Volume Elements in ABAQUS

A MATLAB algorithm was developed to insert cohesive elements at the interfaces between the matrix and precipitates in two dimensional (2D) representative volume elements (RVEs) of metals. The RVEs were created using OOF2 and imported into the “Complete ABAQUS Environment” (CAE) interface. These RVEs are based on actual images of the metal at the microscale, where the precipitates have irregular shapes. The RVEs contain precipitates that are dispersed into matrix materials. Commercial finite element (FE) ABAQUS software does not provide the option to automatically generate cohesive elements at the interfaces. The presented algorithm enables the insertion of cohesive elements at the interfaces between the matrix and precipitate in a convenient manner. This algorithm enables the simulation of the fracture process, including initiation at the interfaces and propagation at microscale, for metals that contain precipitates and/or particles. This algorithm extends the simulation capabilities of the FE solver ABAQUS.

Abstract 18921: Obstructive Sleep Apnea in the Presence of Heart Failure With Preserved Ejection Fraction is Associated With Low Arousal Threshold and Increased Sleep Fragmentation

Introduction: Heart failure with preserved ejection fraction (HFpEF) is often accompanied by obstructive sleep apnea (OSA). The effects of HFpEF on OSA pathophysiology remain relatively unexplored. Pulmonary congestion and sympathetic nervous activity (SNA) may drive ventilatory control instability (high loop gain [LG]). Elevated SNA also theoretically predisposes sleep to easier interruption by breathing events of OSA, reflected by low arousal threshold (ArTH). Hypothesis: In comparison to patients with OSA alone, patients with both OSA and HFpEF have higher LG and lower ArTH. Methods: We reviewed records to include patients with 1) transthoracic echocardiography with EF>40% and left ventricular diastolic dysfunction by decreased age-adjusted e’ and/or left atrial volume index >28 mL/m 2 ; 2) OSA by apnea-hypopnea index (AHI3A) ≥15 events/hour; and 3) raw polysomnography (PSG) data available for advanced signal analysis. LG and ArTH were estimated by Phenotyping Using Polysomnography (PUP), a MATLAB algorithm that quantifies pathophysiologic mechanisms of OSA using PSG data. Values in HFpEF+OSA patients were compared to values from research volunteers with OSA alone. Results: The HFpEF+OSA group included 21 patients. The OSA alone group included 43 research volunteers. LG was lower for HFpEF+OSA (0.63±18) compared to OSA alone (0.69±18), but the difference was not statistically significant. ArTH was significantly lower for HFpEF+OSA (117.0±15.9 %eupnea) compared to OSA alone (152.8±54.8 %eupnea, p = <0.001) and remained significant after adjustment for age, sex, and body mass index. ArTH was correlated with sleep efficiency (R = 0.43, p = <0.001) and negatively correlated with wake after sleep onset (R = -0.35, p =0.005). Conclusions: HFpEF is associated with low arousal threshold, which may contribute to sleep fragmentation. Our data did not confirm the hypothesis of greater breathing instability in patients with HFpEF and OSA compared to OSA alone.

A linear model-based simulation tool for estimating number of trials needed for upper limb stroke recovery in a given rehabilitation session

Traditional methods for assessing upper-limb functional outcomes in stroke patients often fail to estimate the number of trials required to achieve performance stability of a chosen kinematic metric. Limited non-model-based studies have attempted to tackle this issue. To bridge this gap, this study utilized an iterative learning algorithm (ILA) in MATLAB, employing linear models to represent the muscle dynamics and forearm extension of impaired patients. The reference task space trajectory was set as a straight-line point-point trajectory within a range of 0 - 0.2828m. By using the root mean square error (RMSE) as a metric for evaluating kinematic accuracy, a maximum kinematic deviation error of 0.01m was imposed with respect to the trajectory by the (ILA). Results indicate that over 16 trials, performance stability was obtained with improvement in deviation error from 0.0168m in the first trial to 0.0060 at sixteen trials. The result obtained is in line with similar non-model studies and our findings inform the potential of ILAs with linear models for estimation of trial numbers required to attain performance stability of a selected kinematic metric (i.e., kinematic accuracy).

MATLAB-assisted visual ratiometric fluorescence sensing of tetracycline based on antenna effect

Tetracycline (TC) is a widely used antibiotic that accumulates in animal foods, posing a threat to human health and even causing environmental pollution. This research aimed to establish a rapid method for the detection of TC. To this end, sodium alginate-protected gold nanoclusters (SA-AuNCs) were synthesized, which combined with europium ions (Eu3+) to construct a ratiometric-sensing platform for the detection of TC. When added TC, Eu3+ interacted with TC could result in enhanced fluorescence at 620 nm, which was used as a response signal, while the fluorescence intensity of SA-AuNCs remained unchanged, acting as a reference signal. Accordingly, a linear relationship was established in a wide range of TC (0–60 μM) and the detection limit (LOD) was 22.07 nM. The recoveries of the spiked samples ranged from 94.71 % to 109.73 %, implying that the as-proposed method could be successfully applied to detect TC in real samples. Furthermore, color information was extracted from the fluorescence images taken with a smartphone and analyzed by MATLAB, in which a visual mini program could be designed for calculating the channel values of red (R), green (G), and blue (B) of the generated fluorescence images. Utilizing the image processing functions and data processing capabilities of MATLAB, the obtained R/G values were selected for the semi-quantitative detection of TC in real samples without any time-consuming pretreatment and expensive instruments, demonstrating the great potential prospects of integrating MATLAB algorithm with SA-AuNCs-based ratiometric fluorescence probe into one ideal system for the development of rapid food assay.

Modeling and Optimizing Wastewater Stabilization Ponds for Domestic Wastewater Treatment

In Wastewater Stabilization Ponds (WSPs), baffle walls (BWs) have the dual benefit of reducing area requirements and increasing contaminant removal efficiency up to a certain threshold. However, this advantage is mitigated by the increased demand for construction materials, highlighting the need for optimization. Effectively optimizing WSPs to suit diverse climatic regions can substantially alleviate nationwide wastewater treatment challenges. This study focuses on optimizing WSPs across seven distinct climatic regions in Turkey. In the initial phase, a comprehensive analysis was conducted using design spreadsheets for the WSPs to determine the best configuration based on traditional methodology (TM). The results demonstrated a significant decrease in WSPs area and hydraulic retention time (RT), validating the effectiveness of BWs provision. However, this approach requires additional time and effort. Subsequently, mathematical modeling (MM) was used to further reduce the time required for the optimization process. Using the interior-point algorithm in MATLAB and the generalized reduced gradient (GRG) algorithm in MS Excel Solver, both algorithms within MM effectively decreased the WSPs area and RTby approximately 10%, while decreasing the required concrete volume by approximately 5% compared with TM. As other algorithms may yield better optimization, they can be investigated by developing specialized software for WSPs. Doi: 10.28991/CEJ-2023-09-11-014 Full Text: PDF

A Combination of Vigenere Cipher and Advanced Encryption Standard for Image Security

In an era where digital information security is paramount, this research addresses the pressing need for robust encryption methods. We propose a novel approach that combines the Vigenere Cipher and the Advanced Encryption Standard (AES) for secure digital image transmission. Our study recognizes the research gap in secure image transmission methods and aims to bridge it with a powerful encryption solution. We implement this hybrid encryption approach using the Vigenere Cipher in C++ and the AES algorithm in MATLAB. Our experiments validate the effectiveness of our program in concealing and restoring digital images during transmission. This hybrid encryption technique has promising applications in healthcare, military, and confidential business operations, bolstering image security in real-life scenarios. By enhancing image security, our research can contributed to safeguarding sensitive information in the digital age

On controlling, Vibration Performance and Energy Transfer of an Offshore Wind Turbine Tower System via PPF Controller

In this paper, we focus on the control, energy transfer, and vibration performance under multi mixed excitations for the offshore wind turbine tower (OWTT) system. For reducing the controlled system oscillations, the positive position feedback (PPF) controller is applied. The energy transfers occur in the system of wind turbine by adding the PPF controller to the system equations. With the help of the phase plane approach, frequency response equations, and Poincare maps, the bifurcation and stability at worst resonance cases are sought and investigated. The vibration behaviors are studied numerically at different parameters values for the wind turbine system. Additionally, the response and numerical outcomes are examined, also, the approach of multiple scales is used to establish the approximate solutions of the wind turbine-controlled system. Besides that, MAPLE and MATLAB algorithms are used to implement the numerical results and compare analytical solutions with numerical behavior. The results also be compared to previous research that has been published.

Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study.

The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.

A Comparative Study using Scale-2 and Scale-3 Haar Wavelet for the Solution of Higher Order Differential Equation

A comparative study of scale-2 and scale-3 Haar wavelet has been presented to illustrate the level of accuracy attained by both the wavelets by applying on higher order differential equations known as Emden fowler equation, which has great importance in the field of astrophysics. Approximation of space variable is done by scale-2 and scale-3 Haar wavelet method by choosing different scales. The method is tested upon several test problems. The results are computed and compared in the form of absolute errors. The numerical tests confirm the accuracy, applicability and efficiency of the proposed method with different levels using both the wavelets. By the help of MATLAB algorithm simplification of the computational process is done.

Energy, exergy, economic and environmental (4E) analysis of two-stage cascade, Linder-Hampson and reverse Brayton systems in the temperature range from −120 °C to −60 °C

Cryopreservation in the temperature range from −120 °C to −60 °C is an important way to achieve high-quality food storage. In this paper, an in-depth quantitative thermodynamic comparison of two-stage cascade cycle, Linde-Hampson cycle and reverse Brayton cycle was carried out in the temperature range from −120 °C to −60 °C. Aspen HYSYS was used to invoke advanced genetic algorithm in MATLAB to optimize the system parameters. The two-stage cascade system performs best at the evaporation temperatures above −80 °C, achieving a COP of 0.97 at −60 °C. The Linder-Hampson system performs best when the evaporation temperature is below −90 °C, achieving a COP of 0.35 at −120 °C. In order to further explore the impact of environmental change on the system performance, five typical cities in China were selected to analyze the seasonal performances of the three systems. The seasonal analysis demonstrated that Harbin has the highest latitude and its seasonal energy efficiency ratio is significantly higher than other cities, the performance of the two-stage cascade system fluctuates more obvious within 24-h. What's more, the Linder-Hampson system has the lowest annual investment cost and annual operating cost at the evaporation temperature lower than −90 °C, as well as the lowest emissions of CO2 and SO2.

Graphical User Interface-Based Detection of Kidney Stones Using Image Segmentation Techniques

The exponential increase in detrimental surroundings and unhealthy nourishments is causing various health issues in humans. The most destructive effect of such lifestyles is on the kidneys, which cause many kidney diseases, and the most common among them are kidney stones. Kidney stones are a regular but life-threatening disease as they mostly remain unrecognized at the initial stages, leading to an increased threat of end-stage kidney failure. Due to the high recurring rate, Medical Imaging technologies are of paramount importance in detecting this serious public health threat worldwide. This research paper provides a Graphical User Interface for the detection of kidney stones that enables ease of understanding and point-and-click control of the algorithm in MATLAB. The proposed work uses CT scan images to explore image processing techniques due to their reliability and regularity. The algorithm enhances kidney stone screening by improving image quality and focusing on the region of interest. This study highlights the best solutions of imaging techniques to resolve problems like grainy pixels, low-resolution images, and the inaccurate detection of kidney stones due to size and resemblance with nearby parts. All this begins with examining the medical imaging slices from the body area, which later undergoes preprocessing, segmentation, and boundary detection techniques. To check the accuracy of the algorithm, 12 features were extracted using GLCM. Finally, the obtained features were classified using the Classification Designer App in MATLAB. An accuracy of 99.2% was acquired using Ensemble Classifiers. Also, for further progress in the detection of kidney stones in the future, an AI model can be trained so that it can deal with images having different thresholds for better management of the disease.

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.

Effect of atomizer and cross-flows on liquid and gelled Jet A-1 fuel injection

The current study aims to experimentally investigate the effect of atomizer and cross-flow velocities on liquid and gelled Jet A-1 fuels. For gelled Jet A-1, Jet A-1 liquid, Thixatrol® ST and rectified xylene are the base fuel, gellant and solvent, respectively. Gelled Jet A-1 is prepared by adding 85% by weight of base fuel along with 7.5% by weight of gellant and solvent, each at optimum processing conditions. Atomization using a simple-plain-orifice atomizer is investigated to understand its effect on Jet A-1 gel fuel break-up without air cross-flow. Based on the investigation from qualitative flow visualization and quantitative analysis of Jet A-1 gel fuel break-up, a modified simple-plain-orifice atomizer called an internally impinging air-blast atomizer is made. Primary atomization using this new atomizer is studied to understand the break-up of liquid and gelled Jet A-1 gel fuels without any air cross-flow. Secondary atomization of liquid and gel fuel is investigated by transversely injecting fuel into different low-speed subsonic air cross-flow environments. Laser sheet Imaging (LSI) technique is used to capture spray images of both fuels. Break-up mechanisms of both liquid and gel fuels were observed, and the results were compared. Information on the cross-flow air mass flow rate effect on the liquid and gel spray is extracted by developing an algorithm in MATLAB using an image processing tool. Relative droplet size distribution of liquid and gel spray revealed their dependence on cross-flow air mass flow rate. Total droplet count of both liquid and gel fuel decreases significantly as the cross-flow Reynolds’ number increases. At higher cross-flow Reynolds’ number, gel droplets in the flow are observed to be more as compared to liquid fuel.

Liquid hydrogen centrifugal pump optimization based on reducing hydraulic loss and improving cavitation

According to the operation parameters and the design experience parameters, a liquid hydrogen centrifugal pump was developed and tested to meet an application requirement of small flow and high head in cryogenic systems or cryogenic storage tanks. The test results showed that 6 m of hydraulic loss in the pump at the rated condition and the inlet pressure of the cryogenic fluid decreased and fluctuated, which resulted in the pump being prone to cavitation. To improve cavitation resistance and hydraulic efficiency of liquid hydrogen centrifugal pumps, the model was optimized based on the genetic algorithm of MATLAB applying 7 parameters of impeller taken as optimization variables, each sub-objective function was given corresponding weights, and the optimal solution combination at the minimum of the objective function was finally obtained, and an inducer was matched. The results showed that by reducing the angle of the inlet, increasing the diameter of the impeller, and adding an inducer, the cavitation performance of the pump can be improved and the hydraulic loss can be reduced by appropriately reducing the impeller outlet diameter, to improve the head of the pump.A comparison of the performance of the preposition inducer optimized pump with the preliminary pump shows that, at the rated condition point, the head was increased by 4.6 m resulting in an efficiency of 43%. A numerical calculation considered the thermodynamic effect of non-isothermal cryogenic cavitation on the Reynolds-averaged Navier-Stokes combined with the k-ε turbulence model was applied for getting the pump hydrodynamic performances. It was proved that the multi-objective optimization method and preposition inducers were effective, and the calculating results were by experimental data well. Feasibility of the model had been validated to a certain extent. It can bring a high level of pump optimization, integration, and quality to new and existing designs.

Effects of Vibratory Source on Auditory-Perceptual and Bio-Inspired Computational Measures of Pediatric Voice Quality

The vibratory source for voicing in children with dysphonia is classified into three categories including a glottal vibratory source (GVS) observed in those with vocal lesions or hyperfunction; supraglottal vibratory sources (SGVS) observed secondary to laryngeal airway injuries, malformations, or reconstruction surgeries; and a combination of both glottal and supraglottal vibratory sources called mixed vibratory source (MVS). This study evaluated the effects of vibratory source on three primary dimensions of voice quality (breathiness, roughness, and strain) in children with GVS, SGVS, and MVS using single-variable matching tasks and computational measures obtained from bio-inspired auditory models. A total of 44 dysphonic voice samples from children aged 4-11years were selected. Seven listeners rated breathiness, roughness, and strain of 1000-ms /ɑ/ samples using single-variable matching tasks. Computational estimates of pitch strength, amplitude modulation filterbank output, and sharpness were obtained through custom-designed MATLAB algorithms. Perceived roughness and strain were significantly higher in children with SGVS and MVS compared to children with GVS. Among the computational measures, only the modulation filterbank output resulted in significant differences among vibratory sources; a posthoc test revealed that children with SGVS had greater amplitude modulation than children with GVS, as expected from their rougher voice quality. The results indicate that the output of an auditory amplitude modulation filterbank model may capture characteristics of SGVS that are strongly related to the rough voice quality.

Best practice guidelines for the dynamic mode decomposition from a wind engineering perspective

This paper distills the serial work of Li et al. (2022a, 2022b, 2022c, 2023b, 2023a, 2021, 2020a) into a practice guide for the Dynamic Mode Decomposition (DMD) with a particular emphasis on the quick learning and adaptation of the technique from a wind engineering perspective. The guide begins with discussions of the DMD's connections with the Koopman, Fourier & Laplace, the Proper Orthogonal Decomposition, and machine learning theories. Then, based on 1.5 × 106 core hours of parametric investigations on paradigmatic cases, recommendations that broadly apply to bluff-body wind engineering problems—especially regarding the input sequence—are formulated. In the temporal aspect, results highlight the DMD's preference for steady, stationary, equilibrium, or quasi-equilibrium systems, and suggest a sampling resolution 15–20 times the frequency of the dynamics of interest. Emphasis is also placed on the necessity of the Stabilization state for sampling convergence while avoiding the input of overdetermined systems. In the spatial aspect, high-dimensional, mean-subtracted data with minimal interpolation and noise proved to yield better Koopman models. Different input variables, though derived from the same system, may also result in non-unique Koopman modes depending on their nonlinear richness. Finally, two open-source MATLAB algorithms have been discussed and made open-source to facilitate the easy dissemination and usage for wind engineers. Via pedagogical examples, Algorithm 1 outlines a standard procedure to check for sampling convergence, and Algorithm 2 performs the similarity-expression DMD and visualizes the outputs.

Optimizing the performance of the ZnO-based varistors using the neural networks technique

This study aimed to propose a scientific method depending on neural networks technique to determine the optimal proportions of some impurities (Bi2O3, Nb2O5, MnO2, Co3O4, Cr2O3, NiO, La2O3, Ce2O3) used in manufacturing the ZnO-based varistors. The technique enables obtaining varistors with high structural homogeneity, high performance, and surge energy absorption capability. A nonlinear neural model between the nonlinear coefficient and the proportion of each impurity was accurately created. Subsequently, the neural models have been trained to approximate the correlation factor to the optimum value (R = 1), which proves the accuracy of the models. Then, using the optimization toolbox in MATLAB, the proportion of each impurity producing the highest nonlinear coefficient was determined. Afterwards, a nonlinear neural model representing the relationship between the most influential impurities combined (MnO2, NiO, Ce2O3, La2O3) and the nonlinear coefficient was built. Through training the neural model, the correlation factor was R = 0.99516, and the mean square error was MSE = 0.1078, proving the model's accuracy. Depending on the optimal solutions based on the Lagrange-Newton algorithm in MATLAB, the optimal proportions of impurities combined corresponding to the highest value of the nonlinear coefficient were determined. The accuracy of the proposed method was practically confirmed by preparing a varistor according to the optimal proportions and performing the measurements. The relative error between the theoretical value (α = 99.7522) and the practical value (α = 99.41) of the nonlinear coefficient was RE = 0.0035. That confirms that the proposed method has high reliability and accuracy to be adopted.

Developed by Koo and Kleinstreuter model, the time-dependent Blasius-Rayleigh-Stokes flow and melting heat transfer over a semi-infinite hot plate

ABSTRACT The present study investigates a Blasius-Rayleigh-Stokes flow with melting heat transfer across a semi-infinite hot plate. The base fluid included SAE50 motor oil, and the nanoparticle was zinc oxide (ZnO). The similarity variable is used to convert the nonlinear differential equations to ODEs. The RKF-45 MATLAB algorithm is used to solve these nonlinear equations numerically. In order to demonstrate the effect of the many variables in the issue, the findings are shown visually. Different parameters on fluid velocity and energy profiles may be offered in graphs for the nanofluid phase (ZnO+SAE50) and the fluid phase, which show the effects on physical aspects of fluid behaviour. Measurements of physical properties are recorded and observed. With the addition of base fluid, the nanofluid (ZnO+SAE50) becomes more effective. Moreover, it is also observed that the higher values benefit from a boost from the temperature field. Additionally, the thickness of the momentum layer is lowered for values of that are rapidly rising. Greater is made possible by an increase in thermal layer thickness as a result of melting.

Optimizing Distributed Generation Placement and Sizing in Distribution Systems: A Multi-Objective Analysis of Power Losses, Reliability, and Operational Constraints

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.