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Studying the effect of boiler water released in rivers on some water properties such as density, viscosity was done in this research. Equations of momentum, conversation, turbulence and energy were used with the relation between the temperature and water properties to describe those properties along the study reach. Simplifying of partial equation was done to obtain system of linear equation. Verification of numerical model by conducting comparison of observed data from Euphrates River at Mussaib Thermal Power Station with those computed by the model. The comparison results of density and viscosity show an agreement with using statistical tests such as Chi square and RMSE. The results of the study showed that increasing the bed roughness from 0.04 m to 0.12 m causes a longitudinal retardation of the values of density and viscosity values about (26 and 22%) respectively and vertical advance of these values about (14 and 12% ) respectively. Increasing the water surface slope from (6 cm/km to 12 cm/km) causes a longitudinal advance of density and viscosity values about (19 and 12%) respectively and vertical retardation of these values about (16 and 13%) respectively. HIGHLIGHTS Effects of boiler water on water river properties such as water density and water viscosity were studied.; Significance and degree of freedom between observed data from the river and predicted data from the model were studied.; The effects of hydraulic river properties such as bed height and water surface slope on distribution of the values of these water river properties were explored.;

Aerosols, both natural and anthropogenic, are an important but complex component of the Earth’s climate system. Their net impact on climate is about equal in magnitude to that of greenhouse gases but can vary significantly by region and type. Understanding and quantifying these aerosol effects is critical for accurate climate modeling and for developing strategies to mitigate climate change. In this paper, we utilize AERONET (Aerosol Robotic NETwork) data from 10 stations situated in the dust belt region to characterize aerosol properties essential for climate change assessment. Aerosol optical depth (AOD) data at 500 nm and Ångström exponent (α) data at the pair of wavelengths of 440 and 870 nm (α440-870) in the study region are analyzed to discriminate among different types of aerosols. The annual and monthly variabilities in AODs are analyzed to see the aerosols trend in the study region. In addition, the AOD and α440-870 data are utilized in order to determine different aerosol types during the period of study. Furthermore, the correlation coefficient between AODs and various meteorological parameters (temperature, wind speed, wind direction, relative humidity, and visibility) is analyzed. The results of the study indicate that Tamanrasset (2.49%), KAUST (1.29%), Solar Village (1.67%), and Dalanzadgad (0.64%) indicate an increasing trend, while Cairo (−0.38%), Masdar (−2.31%), Dushanbe (−1.18%), and Lahore (−0.10%) indicate a decreasing trend in AODs during the study period. Similarly, the results of characterizing aerosol types show that the highest percentage of desert dust aerosols (68%), mixed aerosols (86%), and biomass burning aerosols (15%) are found over Tamanrasset, Lahore, and Dalanzadgad AERONET stations. The study revealed a strong correlation between AODs and visibility, a moderate correlation with temperature, and a low correlation with other meteorological parameters (wind speed, wind direction, and relative humidity) in the study region. The results of the study are very encouraging and enhance our confidence in using historical AERONET data to improve our understanding of atmospheric aerosols’ characteristics.

Assessment of water quality is a major step in the water industry to ensure the suitability of water for human use. In this study, statistically evaluate the quality of raw and treated drinking water of the Al-Mussaib drinking water treatment plant, Babylon city, Iraq, from January to December 2020. Additionally, the water quality of treated water was assessed according to World Health Organization (WHO) and Iraqi standards for drinking water. The results showed the plant has a good efficiency in removing the studied parameters, such as alkalinity, calcium and hardness. It is noteworthy to mention that although the measured concentrations/levels met the WHO and Iraqi standards, they were higher than the favourable limits. For example, the measured sulphate concentration in the produced water was 248 mg/L, which is higher than the favourable concentration (200 mg/L) (WHO). The statistical analysis indicated significant differences between the quality of raw and treated water (p-value 0.05) in terms of alkalinity, pH, calcium and sulphates concentrations. The results of this work could be useful for water authorities and decision-makers in Iraq and national and international environmental agencies. HIGHLIGHTS Water quality of Al-Mussaib drinking water treatment plant, Iraq was studied.; Water quality was acceptable according to WHO and Iraqi standards.; Significant differences between the influent and effluent turbidity, hardness, magnesium and dissolved solids concentrations.; No significant differences between influent and effluent alkalinity, pH, calcium and sulphate concentrations.;

In many classes of problems of elastic systems such as gyroscopic and circulatory systems stability investigations are being conducted .The concept of a "stability boundary" arising in connection with multiple loading parameters is used for stability investigation. The concept is extended to analyze stability of conservative pipes conveying fluid since they are regarded as gyroscopic systems. In this approach the pipe system is discretized to a two –degree of freedom by using Galarkin projection. The solution of the Eigen-value problem leads to the characteristic equation describing the parameters -frequency relationship .By plotting the root locus of these characteristic equation the main stability features such as stability, buckling and flutter instability and destabilization has been investigated graphically .The validity of this approach was tested by comparing it with the other published methods The results gave good agreements. The effect of the fluid parameters such as fluid velocity, fluid pressure and pipe-fluid mass ratio on the pipe stability are also investigated. The results showed that the mass ratio has a major effect on stability behaviors since the sequence of stability can be dramatically changed whereas ,the fluid pressure showed slight effect since the stability sequence is not altered , for wide range of the fluid velocities .

Abstract Diabetes mellitus (DM) is a rapidly prevailing disease throughout the world that poses boundless risk factors linked to several health problems. Vildagliptin is the standard dipeptidyl peptidase-4 (DPP-4) inhibitor type of medication that is used for the treatment of diabetes anti-hyperglycemic agent (anti-diabetic drug). The current study aimed to synthesize vildagliptin-loaded ZnO NPs for enhanced efficacy in terms of increased retention time minimizing side effects and increased hypoglycemic effects. Herein, Zinc Oxide (ZnO) nanoparticles (NPs) were constructed by precipitation method then the drug vildagliptin was loaded and drug loading efficiency was estimated by the HPLC method. X-ray diffraction analysis (XRD), UV–vis spectroscopy, FT-IR, scanning electron microscope (SEM), and EDX analysis were performed for the characterization of synthesized vildagliptin-loaded ZnO NPs. The UV–visible spectrum shows a distinct peak at 363 nm which confirms the creation of ZnO NPs and SEM showed mono-dispersed sphere-shaped NPs. EDX analysis shows the presence of desired elements along with the elemental composition. The physio-sorption studies, which used adsorption isotherms to assess adsorption capabilities, found that the Freundlich isotherm model explains the data very well and fits best. The maximum adsorption efficiency of 58.83% was obtained. Further, In vitro, anti-diabetic activity was evaluated by determining the α-amylase and DPP IV inhibition activity of the product formed. The formulation gave maximum inhibition of 82.06% and 94.73% of α-amylase and DPP IV respectively. While at 1000 µg/ml concentration with IC50 values of 24.11 μg/per ml and 42.94 μg/ml. The inhibition of α-amylase can be ascribed to the interactive effect of ZnO NPs and vildagliptin.

Abstract The importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.

The objective of this study is to determine the optimum size of stone revetment for different water standing durations and critical drawdown rates for 80% height of flood. The model bank was constructed using soil sourced from the Parlalpur ferry ghat of the Ganga River in Kaliachak, West Bengal, with a field density of 1,500 kg/m3 achieved by maintaining a 10% moisture content (MC) in the model bank soil. The model riverbank was prepared considering Froude (F) similitude having a distorted depth scale of 1/20 and a linear scale of 1/200. In this study, effective stone sizes (D10) – 2.33, 3.22, 4.58, 6.31, and 8.84 mm – were used. These stone sizes were investigated in conjunction with three water standing durations: 15, 30, and 45 min. The bank slope was prepared at 1V:1.5H, and a drawdown ratio of 80% was maintained. The effectiveness of stone revetment size was analysed in terms of the percentage loss of stone revetment and the percentage loss of the model bank's cross-sectional area. The outcomes of this study indicate that the 6.31 mm stone size exhibits optimal performance. HIGHLIGHTS Optimized stone size was determined.; This underscores the importance of careful stone selection, considering soil characteristics.; The findings of this study offer valuable insights into the temporal dynamics of bank stability under real-world hydrological conditions, which are beneficial for engineers.; The importance of synergetic impact of drawdown and velocity for designing the stone revetment for river bank protection.;

In this research the removal of sesame oil from aritifical water by Fenton process technique was investigated. The results of removal efficiency were compared with actual wastewater. Effect of time, pH, mixing speed, temperature, oil content were investigated to find the perfect circumstances. The conducted experiments revealed that the optimal conditions for this process were 160 minutes of reaction time in an acidic medium where pH was equal to 3.5, temperature was discovered to be 25 °C, the optimal mixing speed was at 500 rpm, and the Sesame oil concentration amounted to 1500 mg/l. Applying the optimal conditions for this processing, the results of compersion showed the sesame oil had a removal efficiency of 95% for the artificial wastewater in which the concentration decreased from 12800 to 68 mg/l, while the Fenton method was used to treat an actual sample of wastewater the removal efficiency is found to be 87%. [ABSTRACT FROM AUTHOR]

Abstract Background Breast metastasis from primary ovarian cancer is rare, with an estimated frequency of 0.07%. More than 110 cases are reported in the literature of metastatic spread of ovarian cancer to the breast and axilla. This entity usually represents aggressive late disease characterized by multi-drug chemoresistance and a poor prognosis with a median survival time of 16 months. Currently no standardized treatment protocol exists for this condition. Case presentation We present a case of a 59-year-old Caucasian female with recurrent high-grade serous ovarian cancer who was diagnosed with symptomatic unilateral breast metastasis while on fourth line chemotherapy with weekly paclitaxel. She was treated with local radiation with 2300 cGy to the right breast with a complete response. She then had a subsequent recurrence in the ipsilateral breast 8 months after completion of post treatment imaging. She remains alive to date approximately 2 years after her initial diagnosis of breast metastasis on seventh line treatment. Conclusions Breast metastasis from primary ovarian cancer is rare and represents advanced disease characterized by multi-drug chemoresistance and a poor prognosis. This case describes radiation therapy as a safe, effective treatment option to improve local control and quality of life in these patients, but with limited durability of response.

Amputation can have varying impacts on amputees depending on the extent of residual muscles left. Commercial prosthetics prioritize functional movements, neglecting patient-specific gestures related to the amputation type. Therefore, examining user-specific non-functional movements is pivotal. Rather than training on innumerable motions, prioritizing efficiently identifiable movements enhances prosthetic user-specificity. We examined 16 distinct wrist and finger motions using five classifiers (Wide Neural Networks, Ensemble Bagged Trees, Cubic SVM, Fine KNN, Logistic Regression Kernel). One-Way Anova testing ( $\text{p} < 0.05$ ) revealed significant classifier differences, with Wilcoxon signed-rank tests ( $\text{p} < 0.05$ ) distinguishing finger and wrist gestures. Ensemble Bagged Trees excelled ( $\text{p} < 0.05$ ) for finger (98.2% ± 0.81) and wrist (99% ± 0.73) movements; Fine KNN and Cubic SVM scored up to 96.3% ± 0.81 and 95.9% ± 1.43, respectively. Notably, Abduction of thumb and flexion of others consistently showed high accuracies for finger gestures. Wrist Radial Deviation (Clockwise) and Wrist Extension emerged as top wrist movements. Our study addresses the oversight of non-functional movements in myoelectric control. Our innovative approach focuses on selecting optimal finger and wrist movements for accurate gesture recognition, a departure from prior research neglecting these critical movements. By pinpointing these movements’ significance and performance, we provide a solution to enhance personalized prosthetic development. Our findings mark a pioneering step in leveraging accurate finger and wrist gestures to craft more effective and personalized myoelectric control systems. These insights bridge the gap in existing research and pave the way for improved prosthetic device design.

Abstract Diabetes is a serious health issue that can be a great risk factor related to numerous physical problems. A class of drugs “Gliflozin” especially Sodium Glucose Co. Transporter 2 was inhibited by a novel drug, which is known as “empagliflozin”. While ZnO nanoparticles (NPs) had considerable promise for combating diabetes, it was employed in the treatment and management of type-2 diabetes mellitus. The new drug empagliflozin was initially incorporated into Zinc Oxide NPs in this study using the surface physio-sorption technique, and the degree of drug adsorption was assessed using the HPLC method. The tailored product was characterized by using the FTIR, EDX, Ultraviolet–Visible, XRD and SEM techniques. With an average particle size of 17 nm, SEM revealed mono-dispersion of NPs and sphere-like form. The Freundlich isotherm model best fits and explains the data for the physio-sorption investigation, which examined adsorption capabilities using adsorption isotherms. The enzymes α-amylase and α-glucosidase, which are involved in the human metabolism of carbohydrates, were used in the in-vitro anti-diabetic assays. It was discovered that the composite showed the highest levels of 81.72 and 92.77% inhibition of -α-amylase and -glucosidase at an absolute concentration of 1000 μg per ml with IC50 values of 30.6 μg per ml and 72 μg per ml.

To be a stakeholder/validator/token holder is not so difficult in the Proof of Stake (POS)-based blockchain networks; that is why the number of validators is large in these networks. These validators play an essential part in the block creation process in the PoS-based blockchain network. Due to the large validators, the block creation time and communication message broadcasting overhead get increased in the network. Many consensus algorithms use different techniques to reduce the number of validators, such as Delegated Proof of Stake (DPoS) consensus algorithms, which select the set of delegators via stake transactions for the block creation process. In this paper, we propose S&SEM, a secure and speed-up election process to select the ‘z’ number of validators/delegators. The presented election process is based on a traditional voting style with multiple numbers of rounds. The presented election mechanism reduces the possibility of malicious activity in the voting process by introducing a special vote message and a round that checks duplicate votes. We did horizontal scaling in the network to speed up the election process. We designed an improved incentive mechanism for the fairness of the election process. The designed reward and penalty procedure controls the nodes’ behaviors in the network. We simulate the S&SEM, and the result shows that the presented election process is faster and more secure to select delegators than the existing process used by DPOS.

Due to parallel infrastructure developments in both automobile and alternative transport systems, a modal shift from the automobile to alternative modes has not occurred at its expected level, because of people's affection for the automobile. In this situation, the necessity of imposing a strategic and appropriate gap between demand and supply of auto-related transportation infrastructure is identified first in this paper. Secondly, several models ate developed to calculate demand of auto-related road infrastructure both empirically and theoretically. After that, an index of gap between demand and supply of auto-related road infrastructure is developed, and its relations to travel behavior and land uses are analyzed. In this regard, cities are classified based on their population size and density and level of transit service. Research results show a significant relation of gap index to modal shares and land uses. CE Database keywords: Infrastructure; Transportation models; Land usage; Travel patterns; Highway transportation.

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.

The opportunity of utilizing activated carbon prepared from banana peels (BPAC) as a permeable reactive barrier (PRB) to eliminate copper (Cu+2) from the contaminated ground water has been explored. The activated carbon was prepared from banana peels by chemical activation utilizing a drenching agent, for example, a phosphoric acid (H3PO4). The Influence of different factors including initial copper amount, contacting duration, agitation speed, initial pH of the solution and sorbent dosage was studied in batch experiments. The optimum magnitudes for these factors that resulted in the highest copper removing efficacy (96%) were 40 minutes, 6, 250 rpm, 50 mg/l, and 1 mg/100 ml, respectively. The isotherm models of Freundlich and Langmuir have been utilized to analyze the sorption data for Cu+2 ions acquired via batch studies. The Langmuir model has been utilized to explain the sorption of Cu+2 onto BPAC, according to the findings. The partial differential formulas that describe copper transport in one-dimensional (1D) under equilibrium conditions have been solved utilizing COMSOL Multiphysics 3.5a software based on the finite element technique. The PRB has a significant function in preventing the copper plume from moving, according to the expected findings (COMSOL solution) and experimental findings. Finally, the excellent agreement between anticipated (theoretical) and actual findings, with an RMSE of less comparison with 0.1%, demonstrated that these techniques are useful and efficient instruments for describing copper transport processes. [ABSTRACT FROM AUTHOR]

Human and which serves to preserve the resource at the science forefront. Water of electromagnetic is old but addressed to popular the quantity of contaminants in a new way. The current study presents results related to the influence of an electromagnetic field (EMF) on synthetic water with Al2Si2O5(OH)4 (represented by Turbidity). The impact of an EMF as the only factor or as an intensifying element in stainless steel packing systems that limits pollutant concentrations in the treated water was analyses. The system that combined simultaneously stainless steel filling was most efficient. The electromagnetic treatment unit was designed and constructed for testing the treatment of syntheses water having turbidity. The influence of two variables on the efficiency of contaminant elimination during the function of the electromagnetic treating unit was checked. A significant dependence between the kind of system used reactor diameter and number of electrical coil windings were observed. The results showed that the PH values remain constant with EMF. The performance of the unit is determined by turbidity, the result shows greatest efficiency removal with 99.99% at the minimum diameter (2.54 cm), 99.99% in three reactors with three different numbers of coil turns. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John’s campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John’s. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R2 score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention.

This paper presents the design and optimization of a bidirectional Dual Active Bridge (DAB) converter for electric vehicle battery charging applications, encompassing both heavy and light electric vehicles. The core of this study is a 5.6 kW DAB converter that can seamlessly transition between 3.7 kW and 11.2 kW power outputs to accommodate different vehicle requirements without the need for circuit component changes. This flexibility is achieved through the novel integration of interleaved and parallel operation capabilities, allowing for efficient operation across a broad power range. Key innovations include the design of a high-frequency transformer with dual secondary outputs to facilitate power transfer at high currents up to 30 A, an optimized thermal design, and minimized stress on the circuit board. The use of next-generation power semiconductors and low-loss magnetic circuit elements has resulted in an optimized single-stage bidirectional converter design that showcases enhanced efficiency and competitiveness in the field. Furthermore, the converter’s design enables easy reconfiguration to meet the desired power output, vehicle type, and application needs, making it adaptable for future applications such as Vehicle-to-Grid (V2G) systems. The combination of these features—versatility in power output, efficient high-current transfer, innovative use of power semiconductors, and adaptability for future technologies—positions this DAB converter as a significant advancement in electric vehicle charging technology, offering a scalable solution to meet the evolving demands of electric mobility and renewable energy integration.

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications.

This paper presents a novel single-phase non-isolated bidirectional buck converter topology. The proposed converter uses a basic switching cell structure with a coupled inductor and an interleaving switching scheme. This article addresses a crucial challenge in bidirectional DC-DC conversion by prioritizing reducing output current ripples and minimizing filter inductor size. The employed method includes using MOSFETs with fast recovery diodes to mitigate reverse recovery and body diode losses. Furthermore, the optimization of the switching frequency of the output inductor to be twice the actual switching frequency contributes to reducing the component size of the converter. The coupled inductor also helps to reduce stress on components by distributing currents among its legs. The experimental result demonstrates the proposed converter has a very low ripple current as compared to the conventional converter. The low current ripples and smaller filter inductor size enabled by high-frequency operation have improved the efficiency and size of the converter. A common ground between input and output terminals ensures robust performance without common mode current concerns. Overall, the proposed converter represents a significant improvement in DC-DC converters, promising enhanced efficiency, reliability, and compactness in bidirectional DC-DC conversion systems. In order to verify the performance of the proposed converter, a 460 W buck converter prototype was built and tested.

Wastewater management is considered to be a great challenge, particularly in towns and cities with rapidly growing population. The study aimed to assess the Al-Muamirah WW treatment plant (WWTP), located in the Babylon governorate of Iraq, in terms of its hydraulic loading as a quality performance indicator. The findings showed that there will be a significant deficit in the capacity of the plant of 20%, 32% and 37% in the year of 2024, and will cumulatively arise in 2030 to become 26%, 41.5% and 47.7% for suggested growth rates of 1.27%, 2.5% and 3.95%, respectively. The outcomes of expected amounts of daily discarded wastewater in terms of estimated population were analyzed using the ANOVA test and it was found that the population growth ratio has a significant impact on the generated inflow, as f (2,30) = 3.713, and p= 0.019 < 0.05. [ABSTRACT FROM AUTHOR]

Abstract Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.