KUFA JOURNAL OF ENGINEERING

Bearings generally suffer from high temperatures under loads, which leads to heat transfer problems between shaft and journal bearing. in this paper graphite grease mixed with oil used in journal bearing in three weight fractions 15%, 20% and 25%. physical properties such as thermal conductivity, viscosity and density would be measured. in cement plants, the effect adding graphite grease to the oil on temperatures of tier, roller, oil, shaft inlet and shaft exit would be measured. Also, SEM images of three concentrations of graphite grease in oil would be investigated.

For businesses to remain competitive in the market today, the quality of their products must either be improved upon or maintained. Therefore, creating a fresh strategy that might make more use of data from the production process has turned into a necessary program for every organization looking to boost quality. Fuzzy attribute control charts were created in the current study to track the manufacturing process. The triangle membership function was used to get the fuzzy numbers, and the recommended ranking function was then applied to turn them into a conventional sample. Fuzzy control charts have the potential to mitigate uncertainty stemming from incomplete, ambiguous, and/or confusing information, also the inherent uncertainty originating from measurement randomness in quality characteristics. Through data collection from the Al-Mamon facility and comparison with the conventional Shewhart control charts, a case study at the state corporation for vegetable oils in Iraq was used to validate the suggested fuzzy control charts. This study compares the use of fuzzy logic with the traditional way when adopting variable cases through the arrangement function to attain the control limits for faulty percentages and quality control for all samples using (w=0.6, 0.8, 0.9) and (λ = 0.7, 0.9). The results showed that a fuzzy control chart manages manufacturing quality more quickly, cheaply, and accurately. It makes it easier to find defective units during the manufacturing process, which helps to quickly ascertain whether or not production is under control. It also offers quality enhancements that are advantageous to the company

Distributed generation (DG) is regarded as one of the energy resources concentrated in many and diverse areas and depends on numerous resources, which are distributed and relatively small energy resources, and the generated capacity ranges from the kilowatt to megawatts, where reliable local capacity is supplied, enhancing the efficiency of the energy sources use, and reducing radiation to the lowest possible value. The AI based systems have been employed to simulate smart grids to solve many pressing problems like obtaining the best location and size of distributed generation systems. An example of these algorithms: Genetic Algorithms (GA), Monte Carlo method, Artificial Immune System AIS, Particle Swarm Optimization (PSO), Evolutionary Algorithm (EA) and Ant Colony Optimization (ACO). The majority of these approaches use population-based optimization, where the search direction and exploration in objective function search space are crucial to efficiently identify a solution. In this article, we will study the Particle Swarm Optimization (PSO) and its simulation in MATLAB. The particle-based optimization algorithms used have improved the quality condition indicators from the maximum voltage deviation and average voltage deviation. The used method provides the best and optimal way to reduce energy losses by choosing the optimal size and location of generator distribution systems and achieving the lowest cost

Absorbable metals, designed for temporary biomedical implants, degrade in physiological environments, with three primary families being magnesium, iron, and zinc alloys. Magnesium degrades rapidly, often losing structural integrity before bone healing completes, whereas iron degrades very slowly, maintaining excellent mechanical properties but with prolonged persistence in the body. Zinc, emerging as a promising biodegradable metal, offers an intermediate degradation rate and good biocompatibility. However, pure zinc has limitations like low strength, poor hardness, and limited plasticity, making it unsuitable for direct clinical use. Additionally, zinc’s low melting point (419.5 °C) raises concerns about creep resistance, susceptibility to aging, and recrystallization, potentially causing device failures during storage and use. This research explores strategies to improve zinc’s properties, focusing on alloying for enhanced strength and surface modification to improve biocompatibility, blood compatibility, and corrosion resistance. Future studies should aim to develop zinc alloys with optimal mechanical properties and predictable degradation suitable for medical applications

The SecureSustainNet Framework- a novel technique for enhancing security and sustainability in data centers is introduced in this paper. Given the security and sustainability concerns with data centers, this paper tackles the challenge of balancing data center security and energy efficiency. Existing solutions are unable to provide the required security level without large processing and power costs. States existing approaches that combine efficient resource utilization with high security performance is identified as a research gap. The multi-objective optimization of those approaches is designed to incorporate energy, efficient techniques and security functionalities. The approach has 6 main algorithms; Intrusion Detection System with Anomaly Detection; AES, 256 encryptions with virtualization, based key management; Role Based Access Control RBAC with dynamic policy tuning; dynamic resource allocation; energy consumption monitoring and management; and renewable source integration. The framework has been implemented into the network simulator ns, 3. A remarkably high 98.7% Anomaly Detection Rate ADR was elicited through an interpreter Intrusion Detection System IDS against Gaussian Mixture Models GMM, 6.4% above existing approaches, with a mean 0.4% False Positive Rate FPR. It achieved a 2.5% increase in processing time due to its AES, 256 Encryption system through which employ a dynamically managed key management system. It attained a 97.4% Access Control Effectiveness (ACE) and an 8.8% increase against traditional models through its dynamic policy adaptations based on user context. It obtained an 85.2% Resource Utilization Efficiency RUE and a 7% increase over its competitors while reducing the energy consumption by 15.6% through its efficient resource utilization. The power usage effectiveness PUE of the system was calculated as 1.25, a far cry from current models that achieved it as high as 1.47

Zeolites are a particular type of crystalline aluminosilicate minerals that stand out for their remarkable chemical characteristics and distinctive porous architectures. This work offers a thorough summary of the characteristics, procedures for preparation, synthesis parameters, and various uses of zeolites. Zeolites are particularly useful in catalysis, adsorption, and separation processes because of their large surface areas, great heat stability, and ion-exchange capabilities. Their characteristics can be adjusted by a variety of preparation procedures, including ion-exchange operations, sol-gel methods, and hydrothermal synthesis. The achievement of particular material properties depends critically on modifications to synthesis parameters, such as temperature, pH, and precursor concentrations. Zeolites are particularly useful in the petrochemical industry for isomerization and cracking, in environmental engineering for air purification and wastewater treatment, and in agriculture as soil conditioners. The importance of maximizing synthesis conditions to improve zeolites efficiency and selectivity for certain applications is emphasized in this review

To stay up with the constantly changing energy sector, wind turbine lifespan and energy efficiency must be increased. The advantages of the composite-material blade include low weight, high tensile strength, anti-corrosion, fatigue resistance, and thermal insulation. The selection of materials for wind turbine blades has been the subject of extensive research, particularly the comparison of metals and composites. Because of their characteristics, composites clearly produce better results than metals. However, there remains a research gap in determining which of the several composites is the best material. To examines the natural frequencies and associated deformation of four wind turbine blades composed of Kevlar, aluminium composites, Carbon Fibre Reinforced Plastic (CFRP), and Glass Fibre Reinforced Plastic (GFRP) this research work proposes WTB4: Modal Analysis of Wind Turbine Blades Behavior with Four Distinct Materials. This research goal also includes confirming the blade's strength and evaluating the aforementioned materials to determine which is most appropriate for a wind turbine blade and their vibration patterns. From the results, it is observed that the Carbon fibres provide stronger stiffness, lower compressive strength and a greater sensitivity to misalignment compared to other composite materials

Most rural communities in Nigeria still face inadequate power supply, while others await connection to the national grid due to their remote locations. To meet the energy requirements in these areas, the adoption of renewable energy sources has become crucial for society and the nation at large. Renewable energy resources are largely attractive because of their availability, environmentally friendly nature, and cost-effectiveness through continuous supply. However, due to their intermittent availability, hybrid renewable energy systems are employed to mitigate the drawback caused by their intermittency. In this study, the reliability of a complex hybrid renewable energy system involving five subsystems components is evaluated. The minimal cut-sets of the complex system were first determined, followed by the construction of the fault tree diagram. The failure variables associated with the parameters of each component were assumed to follow Weibull failure laws. The system's reliability was assessed for various arbitrary parameter values, such as failure rate (λ), shape parameter (β), and operating time (t) of the components. The results show that for λ = 0.01, the system reliability ranges from 0.97474 to 0.84816 for β values from 0.1 to 0.2, and t values from 10 to 20. For λ = 0.02, reliability ranges from 0.96671 to 0.57295 over the same parameter ranges. For λ = 0.03, the reliability varies from 0.95568 to 0.34419; for λ = 0.04, from 0.94058 to 0.19465; and for λ = 0.05, from 0.92004 to 0.10677, with β values between 0.1 and 0.2 and t between 10 and 20. The dynamics of these reliability indices are presented both graphically and numerically, based on arbitrary values of the system components' parameters

Challenges of open-channel flow are discussed, with emphasis on energy dissipation and the difficulties brought by channel design for hydraulic pressure and velocity measure. The influence of the rheology of the slurry on channel design is also complicated. The objective of this work is to study the flow behavior in open channels of different shapes and with variable wall stability (fixed and movable). ANSYS Fluent (Release 2, 2021) Computational Fluit Dynamic (CFD) simulations were performed for the velocity distribution and pressure profiles in four configurations of channels: parallel, zigzag, wavy and curved. The study examines the effects of channel height variations and various inlet velocity (6, 3, and 0.3 m/s) on flow behavior. Findings indicate that increasing channel height reduces internal pressure, while lowering the height increases it, with pressure also varying by channel geometry. The curved channel shows the maximum pressure at a height of 0.5 m, and the channel with wavy shape exhibits the maximum pressure at 2831.92 MPa, with the curved channel reaching 3384.85 MPa under fixed-wall conditions

Our research focused on the capability of Naphthylamine (NPHA) & Phenylenediamine (PHDA) to inhibit the corrosion of Cu-Ni alloys in HCl solution, which was examined using the weight wastage technique. Increasing the concentration of (NPHA & PHDA) reduced the corrosion values, demonstrating the influence of corrosion inhibition. Adsorption of (NPHA) on the surface of the (Cu-Ni) alloy plays an important role in repression activities. Corrosion inhibitors are compounds that, when introduced in tiny quantities to an acidic media, prevent the corrosion process from expanding. Inhibitors are one of the most effective methods of preventing corrosion in metal alloys, and they are widely utilized. Corrosion prevention of (Copper-Nickel) alloy in acidic solutions (3.5 % HCl) at the temperature (30 - 50 ℃) was calculated in two cases; with and without inhibitors. Naphthylamine (NPHA) and Phenylenediamine (PHDA) indicated that the substances used in the experiential work were at concentrations (2 - 8 g/l). The method of weight-loss was employed to assess the values of corrosion rates. Corrosion rate value decreased with an increase in the concentrations of inhibitor and decreasing the temperature. The values of adsorption isotherm of inhibitor (NPHA) on the sample surface were observed to conform to Freundlich, in another hand the data of kinetic-thermodynamic and isotherms of adsorption were applied to assess the equilibrium constants (K) values

Metal additive manufacturing (AM), often known as 3D printing, is revolutionizing the production of complex engineering components by constructing them layer by layer. This innovative process utilizes a high-energy heat source and various materials, such as powder, wire, or sheets, as feedstock. The latest review is focused on the progress made in AM, with its potential to produce steel materials of outstanding properties that cannot be matched by conventional manufacturing techniques. This is mainly attributed to the characteristic microstructural changes in the course for AM. This review indicates that many steel types could be process successfully with AM processes. The microstructure, consisting of hierarchical (sub) grains and fine precipitates formed in the process, is largely responsible for its high strength and wear resistance relative to conventional steel. However, ductility remains an issue with AM steels. Moreover, additive manufacturing is an intrinsic heat treatment process and “in situ” phase transformations such as tempering and different type of precipitations are possible when applied to a range of steel grades. In short, the review amply discusses the effects of these unique microstructure features on the properties of steels made by AM.Metal additive manufacturing (AM), often known as 3D printing, is revolutionizing the production of complex engineering components by constructing them layer by layer. This innovative process utilizes a high-energy heat source and various materials, such as powder, wire, or sheets, as feedstock. The latest review is focused on the progress made in AM, with its potential to produce steel materials of outstanding properties that cannot be matched by conventional manufacturing techniques. This is mainly attributed to the characteristic microstructural changes in the course for AM. This review indicates that many steel types could be process successfully with AM processes. The microstructure, consisting of hierarchical (sub) grains and fine precipitates formed in the process, is largely responsible for its high strength and wear resistance relative to conventional steel. However, ductility remains an issue with AM steels. Moreover, additive manufacturing is an intrinsic heat treatment process and “in situ” phase transformations such as tempering and different type of precipitations are possible when applied to a range of steel grades. In short, the review amply discusses the effects of these unique microstructure features on the properties of steels made by AM.

The nonlinear vibration behavior of rotating functionally graded carbon nanotube-reinforced composite (FG-CNTRC) beams is investigated with the impact of carbon nanotube (CNT) distribution patterns and system parameters. Five CNT distribution patterns are considered: Uniform Distribution (UD), Functionally Graded Λ-shaped (FG-Λ), V-shaped (FG-V), Outer (FG-O), and X-shaped (FG-X) to analyze their influence on the mechanical and dynamic properties of the beams. Utilizing Galerkin's method, the frequency ratios are determined with varying amplitudes, hub rotational radii, and volume fractions of CNTs. Statistical results reveal that the FG-Λ distribution gives the highest frequency ratio, enhancing by around 18-20% as compared to the UD configuration, while the FG-O and FG-X distributions represent intermediate improvements. The finding illustrates that a modification in the CNT volume fraction from 0.12 to 0.28 results in a significant increase in the system stiffness with increasing frequency ratios ranging from 1.36 to 2.0 at the peak amplitudes. Additionally, the effect of a hub radius (r = 0.5) also increases the frequency ratio by 6-8%, hence demonstrating the material and geometric parameters' synergy. These findings emphasize the importance of optimizing CNT distribution and system geometry for future engineering use, particularly in aerospace and energy systems

Cell Formation CF is regarded as critical aspect of a Cellular Manufacturing System CMS. It focuses on the creation of part families and machine cells. There are numerous methods, algorithms and models suggested to deal with solving the CF problem. However, there are several drawbacks to some existing CF methods such as, their inability deal with large size datasets, ill structured datasets, the simultaneous creation of clusters of parts and machines, lack of ability to find optimum solutions, and increased material handling costs. Thus, the suggested polar coordinates effectively address the CF problem by clustering parts and machines to reduce the material handling costs. It offers efficient initial clustering, and adapts to different production environments. While not globally optimal, it provides good starting solutions for further optimization, excelling in cost reduction and efficiency. A polar coordinates-based approach combines the repetition of parts and machines with angles and then the resulting pattern is used for checking similarity criteria. The purpose is to improve the Grouping Efficacy GC, decrease the travel time of parts inside and outside cells and enhance Machine Utilization MU. GC is applied as a performance measure to identify the quality of the suggested CF approach. After the polar-based approach is applied, it enhanced the proportion of exceptional elements and machine utilization. On the other hand, it showed better results for GC when compared with Rank Order Clustering (ROC). For some datasets, it recorded (60, 90) compared with (53.33, 71.93) for the traditional ROC

In geotechnical engineering, reinforced soil walls are preferred due to their economical advantage and versatility. Nonetheless, the determination of the structural response of reinforced soil walls under different loading states remains a challenge, especially concerning the selection of an adequate soil constitutive model. This paper describes the development of a two-dimensional computational model capable of examining the effect of soil constitutive models on the structural response of reinforced soil walls. This study utilized a precise simulation of the design process, combining the results with soil parameters obtained through experiments. Two soil constitutive models are considered: the Mohr-Coulomb (MC) model, which is a linear elastic-perfectly plastic model widely used in geotechnical engineering, and the Cap-Yield (CYsoil) soil constitutive model, which captures the soil nonlinearity and stress-dependent stiffness. Results reveal the significant effect of the soil constitutive models on the general response of the structure, including the soil displacement field. Specifically, the MC soil model, which simplifies the shear strength of the soil, overestimates the soil stiffness, predicting lower soil displacements. On the contrary, the CYsoil soil model, which captures the soil nonlinearity, stress-dependent stiffness, and plastic volumetric strains, provides predictions of larger soil displacements. The obtained results highlight the role of selecting a suitable soil model in designing reinforced earth structures. This work has shown that including non-linear soil behavior improves simulation accuracy in numerical modeling for geotechnical problems

The segmentation of medical images via deep learning is troubled by two primary issues: lack of segmented data and difficulty of shapes. Modern approaches for data augmentation have clearly surpassed classical techniques like shifting, rotating and flipping in terms of performance. The AdaptiveRandOARMaskAugmentation method implements elastic deformations alongside dynamic deformations and intelligent edge noise injection to develop authentic anatomical changes in training data. This method has been deployed within the MONAI framework because it enables interoperability for deep learning systems. The approach outperformed traditional approaches by obtaining a 7.4% Dice score enhancement alongside a 2.9 mm reduction of HD95 on OpenKBP radiological planning dataset tests. The method retained its 12% accuracy decrease when elastic deformation was severed from testing procedures. The research shows the promising practical value of this approach for radiological planning since it boosts reliable critical organ recognition performance across anatomically comparable areas. The technique solves both incomplete data problems and class distribution problems to improve model outcomes for real-world applications

The electrocardiogram is an essential tool in biomedical diagnostics for detecting cardiovascular diseases. However, the non-stationary characteristics of ECG signals frequently result in noise contamination, leading to inaccuracies that prevent diagnosis and analysis. The proposed methodology improves electrocardiogram signal processing by preserving essential diagnostic features, including the ST segment, which exists in the low-frequency region and is susceptible to noise interference. The proposed methodology involves three key steps: first, a bandpass filter is used to separate the electrocardiogram frequency band within 0.5–40 Hz; second, noise reduction techniques like Savitzky-Golay, Butterworth, and Gaussian window filters are used to eliminate of Electromyogram, Baseline Wander, and Power Line Interference noise; and third, segmenting the processed data for further analysis. These steps make the signal stronger and more reliable, which leads to more accurate diagnostic results and easier advanced feature extraction. The performance metrics with MATLAB R2023a and the MIT-BIH database showed a notable increase in the signal-to-noise ratio going up to 44.13 dB and the mean squared error going down to 0.0000

5G is the new global wireless standard network. Ultra-low, higher multi-Gbps peak data speeds, lower latency as well as enhanced reliability are among its key features. These features make it easier for 5G technology to be used in Real-time applications like industrial automation, autonomous driving, and immersive, low-latency experiences. However, achieving low latency requires an understanding of the complex interplay between various network parameters. In contrast to the existing studies, the novelty of this research lies in the systematic examination of the effect of 5G parameters on video streaming latency by means of a new end-to-end latency model. While prior works mainly study isolated network components, our focus integrates all aspects of numerology, network deployment strategies (i.e., MEC vs. centralized cloud), traffic load variations and link capacity allocation in one unified model. These values where inspected through simulation, and numerical analysis and the discussion of individual and combined effects on total latency. The results offer insights on the trade-offs of selecting different parameters, and provide the useful guidelines for 5G networks deployment to support latency-sensitive video streaming services. Filling the gap between theoretical latency modeling designs and the practical nuanced network deployment strategies, this study provides a toolbox to help a network operator and application developers understand how to take advantage of 5G technology in high-performance video delivery applications

Despite DC-DC converters are currently extensively utilized within power electronics for their effectiveness in generating a stable DC output with high efficiency, the fact that they are inherently nonlinear makes the selection of advanced converter components and control techniques indispensable for their improvement. In the context of overcoming the performance constraints of DC-DC boost converters, the purpose of the current research work is to use the concept of optimization, which targets the critical parameters of the continuous conduction mode. During the optimization procedure, the objective is to identify the best possible parameters for the inductor, the capacitor, as well as the frequency of switching. In addition to that, the adjustment of the parameters of the Proportional-Integral (PI) controller is carried out to improve the performance characteristics of the DC-DC boost converter circuit. The use of the Golden Eagle Optimization algorithm functions as the basis of the presented optimization approach. The validity of the current technique using the Golden Eagle Optimization algorithm for the design of the DC-DC boost converter circuit is demonstrated through a MATLAB/Simulink simulation. From the results, it can be seen that the proposed design using GEO outperforms various classical optimal techniques such as Grey Wolf Optimizer, Firefly Algorithm, Simulated Annealing, Particle Swarm Optimization, and Moth Flame Optimization. This proposed design using GEO results in a power loss reduction of over 6% and has 10% improvement in output voltage over the latest techniques proposed by Grey Wolf Optimizer. This again manifests the potential of GEO in developing boost converters in terms of power electronics and renewable energy sources

Improving the mechanical properties of biomedical alloys is essential for enhancing their performance and longevity in medical applications. This study investigates the effect of zirconia (ZrO₂) addition on the mechanical behavior of Zr2.5Nb alloy prepared via powder metallurgy. Characterization by scanning electron microscopy (SEM) and mechanical testing (hardness and wear resistance) were employed to evaluate the samples\' microstructure, phase composition, and mechanical performance—the addition of 3 wt.% ZrO₂ led to a notable improvement in hardness, increasing from 153 HV (for Zr2.5Nb) to 185 HV (for Zr2.5Nb–3ZrO₂). Furthermore, wear resistance was enhanced, as indicated by a reduced wear rate from 2.39 × 10⁻⁷ g/cm³ for the base alloy to 1.88 × 10⁻⁷ g/cm³ for the ZrO₂-reinforced alloy under a constant load of 20 N and duration of 40 minutes

Numerous strategies have been proposed in the field of unconstrained optimization to address various optimization challenges, particularly those associated with large-scale systems. Among the classical methods, Newton and Quasi-Newton approaches are well-known for their rapid convergence properties, especially when initiated with accurate initial estimates. However, these methods rely on second-order derivative information and require the computation or approximation of the Jacobian matrix, which is computationally expensive and thus impractical for large-scale problems. As a result, their direct applicability becomes limited when dealing with high-dimensional optimization tasks.Although originally developed for unconstrained optimization, these methods have been extended to solve systems of nonlinear equations and large-scale optimization problems. In contrast, spectral approaches utilize eigenvalue-based techniques to improve computational efficiency, while conjugate gradient (CG) methods minimize quadratic forms without requiring second-order derivatives, making them more suitable for complex, large-scale systems.The Dai–Yuan (DY) method improves upon the classical CG algorithm by providing a more efficient search direction, which can lead to faster convergence and improved numerical stability in certain scenarios. Similarly, the Hestenes–Stiefel (HS) method offers an alternative formulation that often enhances the convergence rate by more accurately approximating the ideal solution path.More recently, a two-spectral conjugate gradient approach has been explored as a promising technique for solving large-scale unconstrained optimization problems. This method enhances both robustness and efficiency by combining the current gradient direction with a previous search direction, resulting in a more effective search path. Furthermore, many recent CG algorithms incorporate a Wolfe–Powell-type inexact line search strategy, which enables efficient step length determination without the computational burden of exact line search methods. This inexact line search provides a favorable trade-off between computational efficiency and solution accuracy, enhancing the practicality of these methods for large-scale unconstrained optimization

In this research, thirteen T-shaped hybrid concrete beams were designed to investigate their flexural behavior. Flanges of all beams were cast by 106 N/mm2 fcu of self-compacting high-strength concrete (SC-HSC), while the webs were made by 45 N/mm2 fcu of normal-strength concrete (NSC). One beam was loaded up to failure as a control beam (CB), and the other beams were divided into two groups; the first group consisted of six beams preloaded by 45% of CB's ultimate load (Pu), and the other group of beams subjected to 85% of CB's Pu. Beams were retrofitted along the entire clear span using various numbers and placements of glass fiber-reinforced polymer (GFRP) bars mounted near the bottoms and side(s) surfaces. However, tests resulted in a notable enhancement ranging from 35% to 101% of retrofitted beams. At the same time, the maximum deflections of both groups witnessed a range from 21.9 mm to 34.9 mm beside CB's ultimate deflection (33.8 mm); in addition, ductility ratio, toughness, and stiffness values varied between (55.89% and 73.8% reduction), (15.95% decrement and 31.96% increment), and (2.36% and 47.83% reduction), respectively. Furthermore, the failure mode was mainly GFRP-Epoxy debonding

Partial shading continues to pose a substantial obstacle in photovoltaic (PV) systems, frequently resulting in substantial decreases in system efficiency and energy output. One of the techniques used to mitigate the effect of partial shading is the hierarchical dynamic reconfiguration. It is based the combination scheme of PV panels as the elemental building block. This paper, introduces a simple adaptive PV panel combiner called the Transition Unit (TU) to control the configuration of PV panels in real-time based on shading conditions. The proposed TU system is practically implemented and tested under different shading conditions. The results show that it is capable to produce a more stable and higher output as compared to conventional parallel combiner and microcontroller (MC) based systems by 21% and 6%, respectively. The TU system does not involve MCs and hence ignores hardware and software complexity, which emphasizes its significance with respect to MC based counterparts. Therefore, the proposed TU system can be considered as an effective and sustainable approach to enhance the efficiency of the PV systems subjected to partial shading

The urgent deployment of fifth-generation (5G) communication networks necessitates wideband operation, high-gain, and stable multiple-input multiple-output (MIMO) performance antennas, especially in the sub-6 GHz frequency band. In this paper, a compact hybrid MIMO patch antenna is presented that is optimized for the 600 MHz to 1 GHz frequency band with the goal of breaking through significant drawbacks of conventional antenna designs such as narrow bandwidth, high mutual coupling, and inadequate polarization diversity. The proposed antenna integrates electromagnetic bandgap (EBG) structures, defected ground planes (DGS), and metamaterial radiators to enhance isolation while maintaining a low-profile configuration. Through comprehensive full-wave simulations and experimental validation, the antenna achieves a bandwidth exceeding 200 MHz, a gain of 12 dB, and total efficiency of 90%, with an envelope correlation coefficient (ECC) below 0.02 and mutual coupling reduced to -22 dB. These results demonstrate significant improvements over conventional designs, which typically offer only 80 MHz bandwidth, 6 dB gain, 70% efficiency, and -10 dB mutual coupling. The experimental measurements align closely with simulation outcomes, confirming the antenna's robustness and practical applicability. Comparative analysis with published data highlights the superior performance of the proposed design in terms of bandwidth, isolation, and polarization diversity, making it a promising solution for next-generation sub-6 GHz 5G networks. This study advances the development of compact, high-efficiency MIMO antennas by effectively mitigating mutual coupling and maximizing spatial diversity, thereby ensuring reliable wireless connectivity in future 5G systems

Dielectric materials are important for use in micro- and nanoelectronics systems, as well as in devices like Thin Film Transistors (TFT), planar waveguides, solar elements, and Light Emitted Diode (LED). The synthesis of such materials, alongside the acquisition of physical information. On their structural, optical, and electronic properties, is very important for the development of new stable devices based on them. The new special issue of “Materials” will include research articles and review papers written by leading scholars in field of materials science and technology. Actually, dielectrics have the thin layer created have good insulating qualities and an effective dielectric constant. Adapted single, binary, and perovskite oxides can be manufactured to provide dielectric materials with high dielectric constant. The benefits of using nanomaterial of using nanomaterial structures in energy storage devices with distinct performance are reviewed and discussed. The advancements in dielectric materials derived from a thin-materials methodology are assessed

This study examines the effects of changing flap angles on the aerodynamic performance of a wind turbine blade, concentrating on torque coefficient (Cm), thrust coefficient (Ct), power coefficient (Cp), and power in watts over a range of rotational speeds. The results show that adding a flap considerably increases Cp and Cm, especially at low-to-moderate speeds. The maximum power and torque values are produced using a 6° flap. The 6° flap design in particular exhibits improved energy collection capabilities, with a maximum Cp gain of almost 20% above the baseline (0° flap). Notable gains in Cm are also seen, with the 6° flap offering a peak torque up to 25% greater than the baseline, indicating possible advantages for starter torque under specific conditions. at low wind speeds. A moderate flap angle of 4° increases thrust without overtaxing the blade structure at higher speeds, resulting in the best balanced improvement for Ct. The results show that a flap angle of 4° offers the best balance, boosting stability and efficiency; however, larger angles (6°) may cause excessive drag and structural loads at high speeds. German cod is used to calculate the geometrical data.According to this study, wind turbine blade design is optimized for better performance in low wind speed conditions

A novel multilevel inverter design for electric vehicle applications is presented in this research. In order to generate the necessary twenty-nine levels of output voltage and current waveforms, this multilevel inverter is developed using ten power switches and four distinct asymmetrical DC sources. Pulse width modulation is the most often utilized control method in multilevel inverters. This multilevel inverter uses sinusoidal pulse width modulation to remove low order harmonics. MATLAB is used for simulation, and a resistive load and resistive-inductive load is used for testing. A 210V, single phase, 29 level proposed architectural prototype uses MOSFETs as switching devices. In the hardware configuration, IRF840 MOSFETs are used. For inverter switches, the Ardiuno micro controller generates the gate signal. To achieve a twenty-nine level output waveform, typical multi-level inverters require a greater number of power components, which raises switching losses, costs, and harmonic distortion. By using 10 switches, the suggested inverter greatly reduces switching losses, low order harmonics, and switching costs, which in turn lowers overall harmonic distortions

Most animals make sounds and calls to express their condition or to communicate with other members of the group communicate with other group members. Poultry chickens live in groups and communicate with each other socially through their sounds. The lack of food or stress that chickens are exposed to negatively affects the global production of eggs and meat. Therefore, in this paper, we present a deep learning-based approach to classifying the status of farm chickens by their vocalization, which helps improve production and monitor the animals. First, all the sounds are converted to the time domain, and the Discrete Cosine Transform (DCT) is calculated to produce distinct features. Then, A two-dimensional grayscale image is generated from the coefficient matrix. Secondly, these gray images are utilized as input into the YOLO v8m model to perform classification. The results showed that the proposed model achieved a high classification accuracy of 91.6% without data augmentation and 98.6% with data augmentation, although there is noise interfering with the recordings or the sounds of chickens nearby. This study may play an important role in animal acoustic signal analysis studies

The submitted algorithm increased data-hiding capacity, extended capability, and improved quality of the reconstructed secret image as compared with traditional steganography and existing coverless information-hiding methods. Unlike conventional steganography, in which the secret information is embedded into a cover image, our method involves the design of generative models that generate a cover image independently of the secret image; thus, there is no cover image explicitly defined. Two generative models are thus used in the proposed method: F creates a middle image with respect to the secret image, while G reconstructs the secret image from the generated cover image. The content consistency extraction module thus encodes content information from the secret image into the generated cover image so as to address the issues of color distortion and content loss in the reconstructed secret image. The experimental results confirm that our method embeds more data, exhibits larger security, and has higher visual quality based on improved PSNR (30.2313), SSIM (0.7762), and reduced MSE(40.1271) values. This method used a dataset comprising 21,550 images, with its results showing vast improvement compared to existing methods

Water towers play a Key role in supporting urban infrastructure, particularly in seismic regions where their collapse can endanger the safety of the public. In this work, A seismic analysis of elevated water tank is carried out (full, half, and empty) using Newmark-Beta time integration procedure with Summation square root of sum of squares (SRSS) method for multi directional analysis. The analysis uses the Newmark-Beta time integration scheme to interpret the dynamic response of the tank under bi-directional seismic loading. The natural period of the tank goes from 0.412 s (empty) 0.856 s (full), whereas the maximum Lateral displacement is 0.28 mm and Base shear forces of 215.4KN under full condition. Structural calculations verify that drift ratios are below 0.5% (IBC,2021) with uniform stress distribution in the columns. The results highlight the need for factoring of fluid-structure interaction and multi-directional analysis in seismic design codes

Nowadays, Internet of Things (IoT) and Sensor Networks (SNs) consider the transformative technologies that enable ubiquitous connectivity and data collection in various fields. In distributed sensor networks, the nodes are responsible on environments monitoring for sensing and collecting various conditions. The forwarding process for these data gains a significant attention in the researches because it considers the first step for the operation of any algorithm or techniques in routing process in any type of wireless networks. In general, during any routing process, the node with data needs to select its next hop efficiently and thus will affect the optimum route from source to destination. The most common techniques that utilized in this step are the flooding and gossiping. These two techniques suffer from different problems such as implosion, overlaps and blindness of any resources. Therefore, this paper proposed an efficient technique for best next hop selection by taking in consideration the main resources that effect the sensor’s power level and thus will reduce the power consumption and then will improve network lifetime. In the proposed technique, the node that has maximum power level, nearest to the current node and it does not receive or send the required packet will be selected as the next hop node. These assumptions help mitigate flooding and gossiping issues. The proposed technique has been implemented using MATLAB simulator. The results show that the proposed system has reduce the node’s power exhausting in single cycle and thus has improve network’s performance

The DC-DC converters are essential to the operation of renewable energy systems because they implement conversions for changing input voltage levels to fixed output levels of voltage. In this paper, we propose adopting a Self-Lift Negative Output Luo DC-DC Converter (SLNOLC), which implements two more configurations – an inductive switching cell and a hybrid switching cell – to increase the voltage gain and efficiency of the converter while reducing component fatigue and ripple. The first design implements an inductive boosting cell, which improves gain by reconfiguring the energy transfer path, while the second design enhances performance even further with the addition of a hybrid inductor-capacitor cell. The proposed converters deliver output voltages of –48V to –60V at 12V input voltage, with pump efficiencies of up to 97 percent. Through continuous conduction mode (CCM), a dynamic response PI controller delivers output stability and fast adjustment within set response values. Simulations done in MATLAB/Simulink demonstrate the superior performance of the proposed designs, validating their use in low-voltage and high-efficiency environments for renewable energy systems.

This paper proposes a hybrid control algorithm that combines geometric tracking control (GTC) with special orthogonal group SO(3) for position tracking and quaternion orientation-based attitude tracking control (QOATC) in order to achieve trajectory tracking with high attitude maneuverability for quadcopter systems suffering from aggressive orientation. Firstly, to solve the problem of the limitation of executing large-angle maneuvers in a quadrotor system caused by the lack of globality or uniqueness in Euler angles models, an improved geometric controller is constructed on SO(3). Secondly, an advanced attitude quaternion-based controller guarantees the tracking performances in finite time and addresses the persisting issue associated with Euler angle-based solutions. The proposed controller guarantees stability and precision by leveraging nonlinear control methods, with thrust and torque computed directly in the body frame. Furthermore, the theoretical research assures the asymptotic tracking and boundedness of all signals in the closed loop system by utilizing Lyapunov's stabilization theory on SO(3). Several numerical simulations are presented to demonstrate the superiority of the proposed approach, showing accurate trajectory tracking, reduced position and velocity errors, and robust performance under aggressive conditions. The hybrid controller effectively avoids singularities and ensures global stability, making it suitable for real-world UAV applications requiring high performance.

Water Quality Index (WQI) is a practical and distinctive way to gauge the quality of water. It is frequently used to analyse water quality and determine its suitability for various uses by stating the status of the water in plain terms (good or terrible, usable or unusable, etc.). The purpose of the study is to evaluate the water quality index (WQI) in the Euphrates River (Al-kufa and Al-Abassia rivers). The site was monitored and evaluated, then data collection over a two-year from 2022 to 2023 on two station sampling sites, S1 and S2 within the river. Twelve physio-chemical parameters were examined in accordance with the Canadian Council of Ministers of the Environment Water Quality Index methodology (CCME WQI) in order to determine the WQI. These parameters (pH, turbidity (TUR) dissolved oxygen (DO), total dissolved salts (TDS), sulfate ion (SO4-2), Chloride ion (Cl-), nitrate ion (NO3-), Phosphate ion (PO4-3), total hardness (TH), alkalinity (Alk), calcium (Ca2+), and magnesium (Mg+2). All data were within the World Health Organization's allowed limits, with the exception of SO4-2, TUR, and TDS, which all exceeded the WHO's allowable limits at the two stations (S1 and S2) over the study period, as well as chloride (Cl-) concentrations during specific months. At the S1 station, the highest values for the parameters (SO4-2, TDS, and TUR) were (466, 1100, 19.8) and (414, 1218, 20.15) in 2022 and 2023, respectively. At the S2 station, the highest values for the parameters (SO4-2, TDS, and TUR) were (427, 1043, 16.45) and (397, 1151.5, 22.55) in 2022 and 2023, respectively. during the study period the WQI displayed clear fluctuation, with good, fair, and marginal quality, The highest values were (79.12 and 65.81) at the S1 station in 2022 and 2023 and (86.1 and 79.33) at the S2 station in 2022 and 2023, respectively. According to the current study, human activity, agricultural runoff, and the discharge of improperly treated wastewater have all contributed to the pollution of the Euphrates River, rendering it unfit for human consumption unless properly treated

The behavior of a non-prismatic beam under static and dynamic loads depends on the density distribution and geometry of the beam along its length. An essential type is a pre-twisting beam. In the current study, the dynamic response of a pre-twisted beam vibrated freely and forced under a harmonic force was investigated by establishing finite element modeling via ANSYS software. Beam length and twist angle were considered as pre-twist parameters. Beam length varying from 300 mm to 1000 mm and twist angle ranging from 0 to 360° with incremented 45o. Modal analysis and then harmonic response were implemented to calculate the natural frequency, total deflection and equivalent stress, and the damping ratio variation with pre-twisting beam parameters. The model was validated against previous studies, showing perfect agreement. The obtained results show that the first natural frequency increases when the twisting angle rises till (180o °) and then remains constant. Notably, the influence of the beam length on the harmonic response reduces when the twisting angle is greater than 180o. Similarly, the deflection of harmonic response increases when the frequency ratio is approximately unity. Finally, the influence of the length of the beam and twisting angle on the stress appears when the twisting angle is smaller than 180o

Fiber reinforced polymer (FRP) bars are more commonly used as alternatives to conventional steel reinforcement because of their non-corrosive characteristics, especially in aggressive environments. Therefore, a large number of researchers have concentrated on studies involving the mechanical behavior of the flexural strength of concrete beams FSCB reinforced by FRP with regard to using FRP bars. In this paper, the flexural moment capacity of concrete beams reinforced with FRP bars and a combination of FRP and steel reinforcement are investigated. The authors carry out a statistical analysis using a database of 144 beam specimens, including 72 reinforced with FRP bars and 72 with hybrid FRP-steel reinforcement. The experimental flexural strength results were compared to theoretically predicted values obtained according to ACI 440.1R-2015, CSA S807-2012, and a hybrid reinforcement equation, in which the three models were analyzed with respect to their prediction accuracy and reliability. Mean, coefficient of variation (COV), standard deviation (SD), Mean Absolute Percentage Error (MAPE), Root-Mean-Square Error (RMSE), Pearson's r, and Nash -Sutcliffe efficiency index are some statistical metrics which were used to better compare the predictive performance of all the equations. The COV of the developed hybrid equations was 15.27 % and SD was 0.16. Contrarily, model ACI 440.1R-2015 presented such COV of 17.5% and SD = 0.17, whereas model CSA S807-2012 showed a COV of 17.11% and an SD = 0.15. It led to a 12% improvement in the prediction of error compared with both ACI 440.1R-2015 and CSA S807-2012, suggesting better precision and uniformity in predicting the bending resistance of reinforced concrete beams. These results highlight the accuracy of the predictive hybrid model, especially in the estimation of the most important parameters such as elastic modulus, FRP, concrete compressive strength, and reinforcement ratios

The development of Mg and Fe based biomaterials in the past decade has been extensively studied as biodegradable material for medical applications. The development of this material is limited in terms of its suitability for clinical applications. Zn-based alloys began to be an alternative to be studied as a substitute for Mg and Fe based biomaterials. Zn-based alloys have a moderate degradation rate but have low mechanical properties, so other elements need to be added to improve their mechanical properties. In this study, the added elements are silver and Zirconium. Compared to pure Zinc the mechanical strength was enhanced significantly for all tested Zinc alloys. Accordingly, we design four alloys (pure Zinc, Zinc+0.4 Silver, Zinc +0.4 Zirconium and Zinc + 0.4 Silver+ 0.4 Zirconium) wt.%. The alloys are developed by stir casting process and homogenized at 280 C° for 1 hour. The Vickers hardness and compression tests of alloys were investigated. The hardness value of zinc based alloy is (64.28 kg/mm2) increased to (100.53kg/mm2) for (Zn­Ag­Zr) alloy, and the improvement percentage of hardness to (Zn­Ag­Zr) alloy arrived to maximum (56.3%). In addition, the compression value of zinc based alloy is (165.95 MPa) increased to (307.76 MPa) for (Zn­Ag­Zr) alloy, and the improvement percentage of compression test to (Zn­Ag­Zr) alloy is (85.45%). So (Zn­Ag­Zr) alloy is considered the best alloy to enhance compressive strength where achieve higher improvement percentage

This review aims to carefully analyze how Carbon Fiber Reinforced Polymer (CFRP) bars and sheets are used in reinforced concrete, focusing on their strength, durability and ability to support a structure. The analysis compiles recent research to look into the positives and negatives of using CFRP rather than steel as a reinforcement for structures. The main parts of this field are bonding ways, extra costs, effects on the environment and how CFRP performs in various situations. The summary points out that CFRP can considerably increase flexural and shear strength in concrete, resist corrosion and create objects that are both durable and light in weight. Despite the expensive cost, limited information about durability and some bonding-related issues, CFRP systems exhibit encouraging effects in applications like flexural strengthening, shear reinforcement and confining concrete columns. Research and new technologies will likely allow CFRP to be more widely used in the future, solving many infrastructure problems

This investigation affords a computational evaluation of a Hybrid Solar Vortex Engine (HSVE.PV) gadget, aiming to enhance airflow dynamics and improve machine efficiency thru changes to the manual vane geometry. Three vane configurations had been expected: a immediately vane at 20°, a curved vane, and a mixed right away-curved format. Using industrial Ansys Fluent 2024, the fashions were simulated under regular boundary conditions to investigate the effect on airflow pace and vortex formation within the chimney. The effects found out that the curved movement over the base case. The better vortex glide and stress distribution located in the third configuration suggest a large development in energy switch capability. The effects observed out that the curved Guide Vane accelerated airflow velocity moderately, whilst the blended layout completed the best average performance, with apeak outlet tempo of 1.737 m/s representing a 20% improvement over the base case. The more advantageous vortex waft and pressure distribution determined within the third configuration imply a large development in power transfer ability

This work is investigation about the engine performance and emission of a single-cylinder variable compression ignition engine working on diesel blended with Waste Plastic Oil (WPO) under various load conditions. The blends containing 10%, 20%, 30%, 40%, and 50% WPO by volume were examined with pure diesel (D100). Results show a gradual decrease in Brake Thermal Efficiency (BTE) with increasing WPO volume while Exhaust Gas Temperature (EGT) rose with higher blends, peaking at 337 °C for D50WPO50.The emission of CO₂ found to be less by 33.3% at the highest blend, while CO and hydrocarbon (HC) increased by 32.7% and 51.3%. Emission of Nitrogen oxides (NOₓ) showed a non-linear trend, initially decreasing at D80WPO20. D80WPO20 is the best-balanced blend keeping in mind optimum performance and emission. The findings are aligned with the past studies conducted in same fields

This study introduces an innovative AI-driven robotic system designed to enhance search and rescue operations in disaster-stricken areas, including earthquakes, floods, and conflict zones. The system integrates a multi-modal sensory framework combining RGB and thermal imaging, processed through a robust pipeline: raw sensor data undergoes validity checks and noise filtering to ensure input integrity, followed by human detection via the You Only Look Once (YOLOv8) algorithm. Concurrently, ultrasonic sensors relay environmental data to a Field-Programmable Gate Array (FPGA) module, enabling real-time obstacle avoidance and dynamic path planning for rapid, precise navigation. Experimental results demonstrate a 96% accuracy rate in survivor detection, outperforming comparable AI-FPGA hybrid systems—particularly in identifying partially obscured humans. The system also exhibits superior response times and operational safety, as quantified in the results section. Future work will focus on expanding sensory modalities, adaptive learning mechanisms, and multi-robot coordination to further enhance mission efficacy