KUFA JOURNAL OF ENGINEERING

In this numerical investigation, the effectiveness of utilizing advanced helical coiled wires (HCWs) as a tube insert for the purpose of heat transfer and turbulence enhancement under turbulent flow conditions (Reynolds numbers: 3000-11000) was examined. HCW followed a helical guide path instead of a straight one in a typical coiled wire case, resulting in increased flow complexity, six models were tested with pitch ratio (P/D) of 1, coiled wire pitch (p) of 6 mm, large base radius (R1) ranged 7-10 mm, coiled wire centerline radius (r) ranged 3-6 mm. ANSYS Fluent 22 was used for simulation. The working fluid considered throughout the study was air as a single phase flow case. The governing equations; The equation of continuity, Equations of momentum conservation, and The equation for energy conservation were solved using finite volume method and k-ε turbulence model. Results indicated increased Nusselt number (Nu) and friction factor (f) compared to the plain tube. Thermal performance factor (TPF) showed an inverse relationship with Reynolds number. The study reported that the circular wire without tapering insert at Re = 3000 exhibited the highest thermal performance factor of 1.228, while the highest enhancement ratio for Nusselt number of 3.86 was recorded in the same case. Also, this model recorded the maximum friction factor increasing at a friction factor ratio of 30.97

Climate change requires innovative solutions to improve traffic management and safety in transportation systems. This paper tries to expand on the complex relationship between weather, traffic, climate, and an integrated approach to traffic data. In the round, the general aim is to come up with the entire understanding of the relationship between weather and traffic, analyzing the way weather interacts with traffic and the way that traffic interacts with weather. It attempts to consider climate change through the consideration of the variables of extreme temperatures and precipitation, among others. It also looks into the work of traffic forecasting in the traffic models by highlighting the emissions and environmental impacts on environment-related accidents, the impacts of transportation systems on the nature of all living organisms, and a detailed table that aids in the clear comparison of different methods with the literature reviewed to understand the details of the interactions of the complex elements. That includes the elements of importance, such as the data sources, methods, and the advantages, along with some of the shortcomings or limitations that were found in them. Finally, it explains the challenges that the researchers had to face. The main findings from our study also suggest that the traffic forecasting patterns by deep learning models may contribute to 15% improvement over the traditional statistical method. In addition, the significant impact of extreme weather events on traffic flow was found; for instance, heavy precipitation events can lead to a 30% decrease in speed and increased accident rates up to 20%. Climate variability integrated into traffic models increases the prediction of long-term traffic trends by 12%, justifying the significance of the influence of climate factors in traffic management systems

This research explores a new plasmonic multiplexer built with tiny ring-shaped waveguides made of insulator-metal-insulator IMI layers, and implemented using Finite Element Method FEM in a 2D format. Performance evaluation criteria included transmission, modulation depth, extension ratio, and insertion loss. Constructed with silver and oxide zinc materials, proposed design operates by precisely guiding light waves to either negate each other through destructive interference or enhance each other through constructive interference. At 1550 nm wavelength, the device can achieve a transmission threshold of 0.5 in a compact design, with features like transmission exceeding 240%, high Extension Ratio, small footprint, and significant modulation depth. This technology could be a key component in creating ultra-miniaturized circuits that use light instead of electricity, paving the way for even faster computers

An investigation into the geotechnical properties of marl rocks from the Zurbatiyah zone was conducted. Characterization of physical attributes revealed dry densities spanning 2.120 g/cm³ to 2.370 g/cm³, specific gravity from 2.50 to 2.660, porosity from 8.7% to 19.23%, and water absorption from 3.69% to 8.99%. Chemical compositions, focusing on major oxides (MgO, CaO, Fe2O3, SiO2, Al2O3, SO2, and LOI), confirmed that their proportions fall within the acceptable ranges for cement industry raw materials, supported by Chaterjee (2018) and Iraqi standard specification (IQS-5:1984). The engineering properties assessment indicated a moderate strength profile, with uniaxial compressive strength values ranging from 21.80 MPa to 45.50 MPa

This work offers recommendations on the best percentages of CKD (Cement Kiln Dust) to use for soil stabilization to reduce compressibility problems 1%, 2%, 3%, 4%, and 5% for projects where gypsum sand soil is common. The results could lead to more sustainable and effective engineering methods in these areas. 22% of the soil analyzed came from Al-Najaf City in Iraq. Each of the four groups in this work underwent six tests: the first group operated in a saturated state without the use of a High Air Entry (HAE) ceramic disc; the second group used HAE to demonstrate the differences between the two groups; the third group used a matric suction (ψ) of 20 kPA; and the fourth group used the original matric suction of 100 kPa. The findings showed that the settlements were reduced with increasing matric suction and improved with the optimal value of CKD when the CKD additive was increased and the optimum values for the saturation state, unsaturation states with zero matric suction, unsaturation tests with ψ = 20 kPa, and unsaturation tests with ψ = 100 kPa were 2.8%, 2.9%, 3%, and 2%, respectively

The world is now more connected through technology and this has given rise to cyber threats, and malware is one of the threats that a system and data need to guard against. In this paper, we propose a detailed framework wherein state of the art ML methodologies can be employed for malware categorization and identification. In the evaluation of the performances of various ML algorithms, we analyze Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Histogram-based Gradient Boosting (Hist GB), and AdaBoost. The algorithms are assessed in this study using an assembly command dataset and a static and dynamic analysis approach to improve the detection rate and stability. Out of all algorithms discussed, Random Forest, CatBoost, XGBoost, Hist GB are ranked highest with 99% accuracy. As for the accuracy, KNN yielded an accuracy of 97%. Performance analysis based on metrics shows that Random Forest, CatBoost, and Hist GB not only have high accuracy but also precision, recall, and F1-score. Particularly, the accuracy of Random Forest was 99% for both the precision, recall, and F1-score. These results confirm the use of ML-based solutions in the analysis and counteraction of modern malware threats and their advantages over traditional detection methods as well as strengthening cybersecurity

This article demonstrates the influence of varying quenchants and precipitation hardening on the mechanical characteristics of Aluminium alloy 6061. Three samples were chosen for the impact test. Solution treatment was performed at 540°C for 2 hours and then cooled at three different brine concentrations (3%, 7%, and 10%). As a result, brine (3%) had the best result with the lowest rate of shock absorption (18.31 J); therefore, for this study, brine (3%) was selected as the quenching medium. All specimens for the tensile, impact, and hardness tests were solution-treated at 540 °C for two hours, subsequently cooled in Brin 3% to ambient temperature, and artificially aged at 180 °C. for 1,2, and 4 hours, at 195°C for 1,2, and 4 hours, and at 210°C for 1,2, and 4 hours. Due to the formation of finely dispersed grains, notable increases in strength and hardness were seen alongside a decrease in ductility. The overall best result achieved was by precipitation hardening at 210°C for 2 hours, which increased the ultimate tensile strength to 340.3 MPa compared to the as- received and annealed samples values which were 260 MPa and 152.02 MPa respectively. In addition, Hardness value increased from annealed condition (18.23 HRB) to (44.14 HRB)

The transfer of secret texts via channels requires a higher level of security to prevent any third party from revealing or extracting any information related to it. In this paper, a secure hiding algorithm for protecting the secret texts either Arabic or English based on RGB image binary decomposition with 16 characters forming the Vigenere key is proposed. The main feature of the proposed algorithm can be summarized as offering security without transferring any information relating to the secret texts with the stego image. The RGB image is scrambled by binary decomposition and reordered its bit plan. Then, the secret text either Arabic or English is encrypted using 16 characters of Vigenere key with a new method for forming this key based on primary and secondary diagonal pixels of the scrambled red channel. On the other hand, the ciphertext length is hidden in the corner pixels of the same scrambled red channel. The performance of the proposed steganographic scheme is evaluated using the elapsed times for its implementation, PSNR, and MSE while the security provided for the spatial domain pixels in the scrambled green and blue channels before hiding the ciphertext is evaluated using histogram analysis and the entropy value. The simulation results demonstrate the security that is provided for the secret texts during the transmission of the RGB stego image

Driving without a valid driver’s license is considered illegal and causes endangers the safety of people, as well as vehicles. Necessary measures should therefore be taken to ensure that vehicles are operated and driven only by those with the appropriate licences. This paper proposes an authentication framework to operate the vehicles. It uses biometric devices together with the Internet of Things (IoT) technologies, where the system verifies the validity of the driver’s license before allowing the vehicle to operate, guarantee that the only authorized person can control it. Other than simply detecting drivers’ presence, the system provides a practical solution to ensure that drivers comply with the requirements to obtain a valid driver’s license through regulations that focus on the nature of effective biometric authentication tackling the issue of unlicensed driving by integrating internet-related technologies. This approach can go a long way in reducing accidents and improving overall vehicle safety. The proposed system used Raspberry Pi, Firebase, and program in C# language for the matching fingerprint authentication process. The main results show that the average time for each verification process is 6 seconds, indicating constant up time. In addition, the system has a success rate of 65% (13 out of 20 attempts) for authentic fingerprints

The Grey Wolf Optimizations GWO Algorithm, is the newer technique to optimize the uncertain optimization problems by autotuning parameters. GWO algorithm is very useful due to its ease of implementation, direct results, robustness, and low computation cost. However, any application involving control systems can also use proportional integral derivative (PID) controllers. Numerous researchers use a variety of techniques, including fuzzy logic, particle swarm optimization, evolutionary algorithms, and others, to attempt to determine the ideal values of P, I, and D. Nonetheless, the primary goal of this work is to determine how well the intelligent algorithm GWO performs while autotuning the PID controller for a permanent magnet direct current (PMDC) motor. The GWO algorithm was selected as an optimization method, and the standard PID's settings were adjusted using integral time absolute error (ITAE). The results proved that GWO as an autotuning PID Controller is better compared to classical PID controller in terms of high performance of the motor’s speed, and good response with steady -state error equal to zero moreover it can be used to obtain optimal position tracking by using PMDC for many applications

A new 15 level inverter is presented in the paper to improve the solar photovoltaic (PV) grid integration process. Improved substantially in the pattern of waves quality with the current THD 3.91%, and voltage THD to 15.77%. Due to its efficiency the design is able to achieve a power conversion efficiency level of 96.75%. The reductions in switches to seven devices is successful and allows for the operational simplicity and cost effectiveness. MATLAB/Simulink simulations have been used to comprehensively revalued the suggested inverter design performance in terms of its operational characteristics and functionality. And the simulation tests looked into analyzing the PV grid connect while considering a group of operational conditions. The results of the test indicate that the proposed system will be able to operate in real life condition because it provides good results in terms of grid stability and dependability with a smooth connection to the grid. Improvements in inverter technology to achieve high efficiency would be facilitated by research presented here that further refines integration method for solar PV systems that requires less operational complexity and better grid integration

Autism spectrum disorder (ASD) poses a severe challenge to effective communication and social interaction abilities for a large number of individuals worldwide. The P300 signal is difficult to detect in individuals with ASD due to noise, low amplitude, and increased latency compared to others. We enhance in this work the P300 classification in electroencephalogram (EEG) signals for autism disease using Riemannian geometry along with various conventional classifiers and ensemble learning approaches like bagging and boosting techniques (AdaBoostM1, GentleBoost, and LogitBoost), and developing sophisticated pre-processing methods for feature extraction. Using the BCIAUT-P300 dataset, our work achieved 96.37% accuracy, 97.51% sensitivity, and 91.11% specificity compared to existing processes, ranging from 67.2% to 92.3% accuracy. This work highlights the potential of our technique in assisting in diagnosis and supportive ASD technologies

Microwave photonic filters (MPFs) based on stimulated Brillouin scattering (SBS) and their applications in various microwave systems are extensively reviewed. This work summarizes the state of the art, emphasizing new methods, experimental proofs, and optimized passband properties of SBS-based MPFs. Important topics include frequency-tuning-range-extended MPFs, switchable MPFs, high-selectivity rectangular filters, and bandwidth-reconfigurable MPFs, addressing significant issues in traditional filters. Photonic technology in microwave photonics (MWP) enables processing of broadband, high-frequency microwave signals with excellent selectivity, stability, and adjustable passband properties. Applications span optical wireless communication, optical signal processing, radio over fiber (RoF) systems, satellite communications, optical sensors, and instrumentation. This review offers valuable insights into the advancements and potential impact of SBS-based MPFs in microwave systems

Systems that rely on wireless technology frequently use radio frequency integrated circuits (RFICs). Modern wireless communication systems rely heavily on low-noise amplifiers, particularly those operating in the 5 GHz frequency spectrum using 90nm technology. Improving the performance of 5 GHz low-noise amplifiers is the goal of research into these devices, which tries to solve problems with noise, gain, and power efficiency. When designing better, more efficient, and more balanced wireless communication systems, low-noise amplifiers are a must-have component. This research study introduces a 5 GHz wideband low-noise amplifier (LNA) for 5G Wi-Fi applications. A 1.2 V power source powers it. The circuit features an optimized common source topology to lower the noise figure, which in turn increases the voltage gain and reduces power consumption. To ensure that the circuit is compatible with the source impedance, methods such as inductive source decomposition and single-stage common source decomposition are employed. The mathematical analytical method was incorporated into the design process and served as its fundamental element. The results showed that with a transistor width of 165 μm and a current flowing through the circuit of 4 mA,(NF = 1.266) a gain of 18.493 dB S (2,1) and a power consumption of 4.8 mw were achieved. In terms of achieving a balance between the results of noise and gain, we were unable to accomplish the essence of the design process through mathematical analysis. Since a satisfactory outcome cannot be achieved through mathematical analysis, we optimise using the genetic algorithm. This method is known for its high efficiency in establishing a gain-noise balance and yields effective optimization solutions. We got these results: With an NF of 1.308 and an ideal power usage of 2.64 mW, we achieved an optimal gain of 27.21 dB(S2, 1). Our results show that evolutionary algorithms greatly improve LNA performance by pointing to the best settings for maximizing gain and reducing noise. Lastly, this work illustrates how genetic algorithm optimization has drastically changed LNA design. Engineers can accomplish RF signal amplification feats never seen before by integrating state-of-the-art computational tools and modelling methodologies. Since these methods successfully satisfy the demanding requirements of modern wireless communication systems, the findings indicate that RFIC technology is heading in the right direction

The importance of transformers is highlighted in many applications, including industrial applications and renewable energies. Among the types that can be highlighted are a series soft-switching transformer with a voltage multiplier in addition to a double-threaded inductor. The series transformer is considered better than a regular transformer as it is possible to achieve a voltage gain greater than Ordinary transformers. Tests can also be performed using electronic switches in addition to a coupled inductor in the process of building a model of a cascade DC/DC converter. The results of the proposed simulation and tests are being verified to show the increase in gain due to the effect of a double-type, three-coil inductor. Tests were conducted using the ORCAD program by proposing an electronic power transformer to raise the voltage from 24 V to the input voltage. Work is underway to verify the possibility of raising the voltage to 200 V. Some electronic elements are added to the transformer to assist in the process of distributing voltages and branching currents. The passive elements include the coil and the capacitor, as well as the presence of the basic and active elements such as the diode and the transistor. All problems of electronic transformers can be overcome as a result of changing operating conditions with time and the occurrence of any disturbances such as lost energy during opening and closing. Electronic keys in addition to the problem of reverse recovery. Therefore, the effectiveness of the proposed circuit can be investigated to provide the required gain to represent a higher voltage than the low input voltage with a relatively high efficiency. The current study also discussed the behavior analysis of the proposed system to include the steady state, justifying the theoretical analysis and design considerations in detail

In learning analytics and educational data mining, a prominent challenge is posed by the lack of portability and transferability of predictive models across different courses. A novel ontology-based decision tree model is introduced in this study, which significantly enhances portability by incorporating semantic features. Unlike conventional decision tree models that are static and course-specific, the approach allows for the dynamic generation and adaptation of decision tree rules using ontologies, thus enabling seamless application across multiple courses without compromising accuracy. The core innovation of the method lies in the use of the Semantic Web Rule Language (SWRL) to integrate decision tree rules into an ontological framework, allowing reasoning and adaptation based on domain-specific semantics. By embedding decision tree rules that differentiate between pass and fail outcomes within an ontology, superior portability, and adaptability are achieved across courses with similar usage patterns. Applied to the Open University Learning Design (OULD) science courses dataset, advanced machine learning techniques were utilized to derive predictive rules, achieving an exceptional prediction accuracy of 97%. This demonstrates not only the effectiveness of the model but also its potential for robust transferability across various educational contexts, marking a significant advancement over existing methods

Image denoising is a key challenge in the field of image processing, focusing on eliminating undesirable noise while maintaining essential features like edges and textures. This research comparatively analyzed various methods of the Undecimated Wavelet Transform (UWT) for achieving image denoising. The initial section examined the performance of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) utilizing MATLAB, indicating that biorthogonal wavelets provide optimal noise reduction with minimal degradation of detail. The subsequent section investigated various thresholding techniques, specifically SURE, Hybrid, and Universal by calculating their processing times evaluated over four levels of decomposition in LabVIEW. Results demonstrated that SURE exhibits the longest computational duration, particularly at elevated levels of decomposition, whereas the Hybrid approach offered a favorable balance between performance and processing time. Conversely, the Universal thresholding method is identified as the most expedient, proving to be the most efficient at greater levels of wavelet decomposition

This study presents a novel approach for producing carbon-based nano-lubricants, which avoids exposure to dry nanopowders usually used in production processes, thereby minimizing the health risks associated with nanoparticles. Furthermore, it adheres to green chemistry principles, as it does not involve toxic solvents or surfactants. In this approach, glucose, the most abundant and cost-effective monosaccharide in most crops, was subjected to hydrothermal carbonization to produce carbon-based aqueous nanofluid. The nanofluid was then mixed with a base oil (SAE 15W-40) at calculated volume ratios. The resulting mixtures were heated to evaporate the water, resulting in 5, 10, 15 and 20 g/l (i.e., 0.25 %, 0.5 %, 0.75 % and 1 % volumetric concentration) stable carbon-based nano-lubricants. In addition to the characterizations of the nano-lubricants, including viscosity and thermal conductivity, power consumption and wear measurements of a tribological system were conducted. The findings revealed linear relationships between nanoparticle concentration and viscosity, thermal conductivity, power reduction, and wear reduction. The maximum thermal conductivity enhancement and viscosity increase were 6 %, and 9.95 % for a 20 g/l nano-lubricant, respectively. Additionally, the power and the wear reductions were 16.66 % at full load, and 52.5 % for the 20 g/l nano-lubricant

Transformer is one of the major and most important high-cost components of electrical power systems, so it is necessary to prolong their life span , reduce downtime and maintenance. There are many types of electrical and chemical diagnostic methods for monitoring insulation conditions for the transformer, like Doerneburg’s and Roger’s revised IEC-599r methods. This study introduces fuzzy logic, which handles vague, imprecise, and uncertain fault diagnoses for transformers. This work applies fuzzy logic for three methods: Doerneburg’s, IEC 599r, and proposed methods by using MATLAB to measure the dissolved gas in the mineral oil of transformers to explain if the transformer is faulty or NOR., and when the transformer is faulty, what must be done for accumulation of gases. The results explain that the correct diagnosis for the three methods: Doernenburg’s, IEC-599r, and the proposed method is 12%, 76%, and 92%, and the certainty is 83.3%, 90.3%, and 89.1%, respectively

Landmarks are considered essential attractions in city planning and play a role in activating tourism and reviving the region's economy. Still, Basra's landmarks suffer from people's social withdrawal, creating distorted foci of the city fabric. Basra's urban development pursues an ill-considered development policy in developing such places. Therefore, the research considers the role of spiritual values, symbolism, and social dimensions embedded in landmarks to activate the spatial attraction energy. The research aims to identify design variables that create spatial energy in the presence of historical and socially significant landmarks to enhance social interaction and preserve the city's urban aesthetic. This research used a questionnaire and a site survey in multiple case studies to investigate the impact of landmarks' spatial energy on a place's vitality, attraction for people, and surrounding functions. The results identified variables that can help urban designers and authorities develop landmarks and enhance their roles in the city

Land cover is essential for accurately observing land-use changes and assessing soil erosion risks. The study area between the Wasit, Misan, and Thi-Qar governorates is an ecological zone suffering from human-made soil erosion as crop cultivation, livestock grazing, etc. This research aims to analyze vegetation dynamics and evaluate soil cover utilizing Normalized Difference Vegetation Index (NDVI) data from 2013 and 2018-2021 during summer and winter, leveraging Landsat-8, Operational Land Imager (OLI) and a Thermal Infrared Sensor (TIRS), and QGIS. Warmer months exhibited a decrease in water bodies and vegetative cover compared to colder months. The changes from 2013 to 2021 were about 17%, distributionally as a 21% reduction in winter and 3.5% in summer. NDVI values and C-factor of RUSLE were calculated to estimate soil erosion, where Slight erosion occurrences exceeding 70%, while moderate and higher levels are found near Lake Shewicha and temporary water bodies. High to extremely high erosion types concentrated in tributaries of the Tigris River. Soil erosion increased between 2013 and 2021 by 4.2 and 18 Km2, respectively. The erosion soil for 2013 increased in November compared to April, except for slight to moderate and very high erosion, which decreased by 2%. In 2021, erosion decreased in December compared to April for all types, except for slight erosion increased by about 6%. The findings aim to help urban planners and land-use managers promote sustainable land management and soil conservation

Wireless communications are integral to modern technology, particularly as telecommunications innovations have rapidly progressed over last half of twentieth century. The demand for wireless local area network (W LAN) hardware has surged in recent years, driven by advancements in transmission speeds, coverage areas, security, quality of service (QoS), and mobility resulting from continuous evolution of WLAN technology. A wireless communication system's transmitter architecture, transmission rate, and receiver architecture are all determined by wireless communication channel. Since establishment of IEEE 802.11 protocol, WLANs have undergone significant transformation over past two decades, reflecting the increasing demand for their services. IEEE 802.11 standard specifies physical layer for communication across a local area network that is wireless across frequency ranges of 2.4, 3.6, 5, and 60 GHz. Wi-Fi primarily operates within multiple frequency bands, such as 2.4 GHz and 5 GHz, offering a variety of channels to accommodate multiple devices in diverse environments. This work aims to design a radio frequency synthesizer characterized by high speed and minimal noise. The results obtained show that when it relates to producing high-output signals with little spur noise, the second frequency synthesis that was suggested performs superior. At a 2 MHz frequency offset, the phase measurements of noise for 2.4 GHz register at -155.799 dBc/Hz, and at a 3 MHz frequency offset, they reach -170.596 dBc/Hz, which is within the permitted range. Additionally, synthesizer achieves an improved settling time of 6.320 μs

Polymeric composite materials vary in their ability to allow chemical solutions and solvents to penetrate through them depending on the type and quantity of the reinforcement material and the immersion time in the chemical solution. This research included studying the diffusion property of an epoxy resin that is 80 % mixed with 20% nitrile rubber and reinforced with (1, 1.5, 3, 5%) bentonite by weight. The composite material was immersed in distilled water, diluted hydrochloric acid HCl (1:1), and sodium hydroxide NaOH at 5 M for eight weeks to determine the weight gain ratio and diffusion coefficient. The results showed that the weight gain values increased with an increase in the immersion time, but they decreased with the increase in the amount of bentonite at the same immersion time, so the diffusion coefficient was reduced with an increase of bentonite. SEM images show the surface morphology of the composite material

The present research investigates various techniques for producing barium titanate (BaTiO₃) in depth, highlighting how each affects the material's morphology and crystallography and consequently, its electrical and piezoelectric properties. Among the preparation methods taken into consideration were chemical-mechanical, hydrothermal, solid-state reaction, sol-gel, and microwave processes. The study demonstrated that by altering the preparation method, different characteristics and properties can be achieved. Furthermore, the study successfully proved the enhancement of the properties of nanomaterials through the synthesis of BaTiO₃ using advanced techniques, aiming to achieve better results in the future and increase its applicability in modern electronic devices

Fine-grained soils present challenges in construction due to their moisture sensitivity, which impacts the structure's stability. This study examines how saturation levels affect low-plasticity clay shear strength and modulus of elasticity. The standard proctor test assessed soil compaction for maximum dry density and water content. Unconfined compression tests determined the initial tangent modulus of elasticity, strain at failure, and undrained shear strength (Cu). Approach A: Soil samples are saturated by 20% gradually. Approach B: prepares samples at 100% saturation and gradually decreases them by 10% to achieve saturation levels. Results indicated saturation greatly affects Cu, modulus of elasticity, and strain at failure. Approach A reduces Cu gradually (18%) as saturation increases, but approach B reduces (67%) it sharply, especially at low saturation. Higher saturation increases strain at failure, exhibiting plasticity. Both approaches decrease modulus of elasticity as saturation increases. Undrained shear strength was higher in approach B specimens than in approach A

In many countries, cement bound granular mixtures (CBGMs) have been widely used in semi-rigid pavement as base/subbase layers to ensure better load-carrying capacity. However, due to their brittleness, CBGMs are susceptible to cracking under repeated traffic loading, which eventually accelerates pavement deterioration. To address this problem, this investigation was carried out to study the early age mechanical properties of CBGMs reinforced with polyethylene terephthalate (PET) fibers in terms of unconfined compressive strength (UCS), indirect tensile strength (ITS), bulk density, ultrasonic pulse velocity (UPV), static and dynamic modulus of elasticity, toughness, and ductility. Four fiber contents (0, 0.5, 1, and 1.5% by the volume of aggregate) were used. It was found that the inclusion of PET fiber reinforcement has significantly improved the tensile strength, toughness, and ductility by 19%, 203%, and 8.3 times, respectively. Moreover, it was noticed that incorporating PET fibers up to 1% resulted in a slight increase in compressive strength by 7.43%. Both bulk density and UPV, on the other hand, were declining due to PET fiber inclusion. Based on the outcomes of this study, CBGMs reinforced with PET fiber content of 1% are sustainable, cost-effective and environmental-friendly promising solutions

Convolution is a powerful operator that has applications in science, engineering, and mathematics. However, Convolution:-(Issues and Applications) is necessary for addressing many scientific and technical cases, including partial differential equations, signal processing, and image processing. Traditional problem resolution is complicated and has several drawbacks. This work presents a set of efficient algorithmic methods for both linear and circular computing. Depending on the duration of introduction and the treatment medium, three sloven methods can be applied based on the rapid table method. Convolution-based systems are very suitable for dealing with complex data that requires appropriate handling to achieve an ideal solution with increasing data length. Though the suggested techniques are geared toward fully parallel hardware implementation, they are contrasted with depending on length N and multiples, fully parallel hardware implementation using the proposed approach requires 40% to 65% less compared to the traditional approach. Since multipliers need a lot more space on the chip and energy than adders, the proposed algorithms are resource and power efficient when implemented on hardware.

The chaotic optical communication system is a new communication system that uses optical chaotic waveform to transition the data at a high bit rate. Its probable applications include secure communications and wideband communications. The semiconductor lasers are well suited for chaotic optical communications systems because the internal laser oscillation is easy to interfere with a field of optical injection or optical feedback. Chaotic optical communication is a hopeful technique for improving the privacy and security of communications networks. It uses synchronized chaotic emitters and receiver lasers for encoding and decoding the data at the hardware level. High-speed communication has made extensive use of optical communications reliant on semiconductor lasers, which are known for their wide spectrum, low power consumption, and no constraints of electromagnetic spectrum. With the chaotic optical communication's present development phase. In this paper, we review the basic technology of chaotic optical communication and research progress. We will first look at several component-based nonlinear strategies for creating chaos. Next, we focus on enhancing security, developing chaotic optical communication transmission capacity, optical chaotic synchronization, and broadband chaotic communication. We conclude by discussing the obstacles and opportunities of chaotic optical communications.

This work investigates the effects of adding vortex generators (VGs) in front of twin injection holes drilled on a flat plate geometry. To analyse the improvement of heat transfer, VGs are positioned at three different locations: 0D, D, and 3D upstream of the injection holes. The configurations used are prism-shaped VG of height = D, 1.5D, 2D, and 3D, with an inclination angle of 0° to 40°, and a blowing ratio of M=1.0. Using the commercial CFD code StarCCM+, the focus is on temperature distribution to examine the improvement of the injected jet coverage on the wall. The sst k-w turbulence model of the Reynolds-averaged Navier-Stokes method (RANS) is used to model the turbulent flow over a flat plate. The accuracy of the RANS approach to predict the jet in crossflow interaction (JICF) is evaluated in this study. The effective operation of this cooling enhancement is demonstrated by the region covered by the injected air along the flow direction. The results showed that the VG location is the crucial factor that controls the jet coverage. Compared to the base hole, the cooling performance produced by the VGs design exhibits obvious enhancement at all VGs locations. The jet footprint of the VG cases is larger than that of the base hole. Besides, it was also shown that the VG technique applied in the current research decreases the mixing process between the mainstream air and the secondary jet and enhances the coverage level. It was also found that the VGs generate strong reverse vortices moving against the main vortices, which destroys their undesirable effects and reduces the jet lift-off effect which then keeps the jet attached to the wall. In conclusion, the simulation results indicate a greater coverage area can be achieved when the VG is mounted at an upstream location too far from the injection hole

Simultaneous Lightwave Information and Power Transmission (SLIPT) is an emerging technology that combines data and power transfer into an integrated optical link, capable of providing efficient and compact solutions for various clean energy interconnection applications relevant to IoT devices. This work reports an experimental study of an SLIPT system using commercial lasers and photovoltaic (PV) panels for synchronously transmitting information and power. An experimental testbed is performed to analyze SLIPT system prototypes transferring both data and harbored energy over a distance of 12m. The experimental results demonstrate that the Silicon (Si) PV panel can harvest up to 5.5% of laser power with a bit error rate (BER) of 1.32×10-4. This pioneering approach opens up possibilities for low-cost, independent, and self-sustaining systems

Nowadays, it is widely recognized that solar PV is one of the favorited sustainable energy options globally. Recently, new technological methods have been developed based on the use of thin films and nanostructures with the aim of reducing the solar cell cost and increasing its efficiency. This work includes simulation of Cu(In,Ga)Se2 nanostructured solar cell. Silvaco program was used with TCAD technology to obtain (ZnO/CdS/CIGS) tandem solar cell model. The thickness of three layers was changed for optical wavelength range (300 –1200) nm, then the optical quantum efficiency was studied and compared with the measured model. The maximum value of quantum efficiency (80%) was obtained when thickness of ZnO layer was doubled, while the maximum value of this efficiency was obtained (74.5%) when reducing the thickness of CdS layer to one-tenth. As for CIGS layer, by increasing its thickness three times, the optimum quantum efficiency obtained is (82.08%). Several peaks are obtained in each case for different values of wavelengths which are determined from (450-750) nm. The best results reached were relied upon to implement an optimal model for a solar cell with several values of maximum efficiency for different wavelengths, as the highest quantitative efficiency (92%) has been achieved

This study is concerned with the effect of the use of additives and joint material type (cement mortar and adhesive material) in hollow foamed concrete blocks through testing walls built with these blocks by applying axial compression force. In this study, three walls were built. In the first wall, cement mortar as a joint material between the cuboid blocks was used. While, in the second wall bonding adhesive material was used. In the third wall, conventional of foamed concrete (without additives) was used in producing the hollow block units which were bonded by using a bonding adhesive material. The compressive behavior and modulus of elasticity were studied, and the deformation and failure of masonry walls were studied as well. It was observed that the use of a bonding adhesive to bond the block units made of additives gave the highest compressive strength (1.08 MPa) which was about (27 and 38) % higher than the cement mortar wall and the hollow concrete block wall without additives respectively and the modulus of elasticity (46.9 MPa) and also gave the highest bearing capacity (214 kN). However, the use of a bonding adhesive helped in reducing the wall displacement. Finally, it was found that the lower the fractal dimension (D) the stronger the wall.

This paper investigates the channel capacity of unreachable memory cells (UMCs), where a cell is deemed -unreachable if it cannot store values beyond a specific state, . Memories with these impairments are modeled as discrete memoryless channels (DMCs), similar to those used in information theory and communications. We derive Shannon-type capacity equations for memories with unreachable levels and substitution errors. These novel equations generalize classical Shannon capacity to systems with UMCs. We also compare ideal memories (without imperfections or errors) with normal memories affected by random errors only, as well as defective and erroneous memories. Our findings corroborate previous studies, particularly regarding random distributions of defective cells. Our results highlight the impact of increasing faulty cells and substitution errors, demonstrating the necessity of greater redundancy to maintain system performance.

Recent studies have highlighted the impact of nanofluids in enhancing the rate of heat transfer, particularly by improving the plate heat exchangers (PHE) thermal performance. The problems of using nanofluids at high concentrations in plate heat exchangers such as sedimentation, blockage, and cost prompted researchers to try new techniques to improve the efficiency of heat exchangers by using low concentrations of nanofluids. Among these techniques is increasing the number of plates for the plate heat exchanger. Few experimental studies have been conducted in this field , the number of plates used did not exceed 16 plates. Thus, this study examines the increasing of plate numbers effect on plate heat exchangers performance with CuO nanofluids at 1%wt to avoid the problems of using high concentrations to verify the possibility of investigating whether increasing the number of plates may cause noticeable problems in pressure drop when exceeding a certain number and the possibility of finding alternative methods to achieve the best thermal performance. And achieve a balance between improving the design and physical properties of nanofluids at low concentrations. The experiments aim to determine the optimal heat exchanger configuration (plate numbers) that achieves high thermal performance while minimizing the pressure drop and pumping power. Four different configurations (8, 12, 16, and 24 plates) were experimentally investigated. Results revealed that an increase in plate number led to directly proportional enhancements in the rate of heat transfer and the Nusselt number. In the first three configurations, the overall heat transfer coefficient improved by 10%, 17%, and 19%, respectively, with an imperceptible increase in pumping power and pressure. Effectiveness improvements of 7%, 9%, and 12% were achieved with 8, 12, and 16 plates, respectively, while using 24 plates provided the highest effectiveness improvement of approximately 24%. However, the 24-plate configuration showed a remarkable increase in the pressure drop and pumping capacity, which has an adverse effect on the overall heat exchanger performance

Software plays a vital role in all aspects of our daily lives, specifically in the fields of medicine and industry. In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors, this is considered a major challenge due to the limited time and budget specified. Therefore, most software development companies tend to use machine learning for prediction. With the presence of software defects that contribute to improving the quality and safety of the software produced, this is done by relying on and using records, previous projects, and available data. this paper proposed machine learning and ensemble learning suite to predict software anomalies. The evaluated approach is for models in the PROMISE real-word dataset repository containing 5 projects (Turkish company SOFTLAB). The model applies the basic algorithms (Random Forest (RF), Decision Tree (DT), Extra Tree) and the learning model ensemble (Adaboost, xgboost ,Stack, Voting, bagging) and metrics (accuracy, recall, F1 score, accuracy) to measure the prediction performance of the models and a comparison was made between the proposed model algorithms. Both adaboost , stack achieved prediction accuracy about 99.2% when implemented on the ar5 dataset.

The current investigation aims to simulate the heat transfer of conventional fluid and various types of nanofluids inside a horizontal heated tube exposed to a constant heat flux. This study implemented Ansys Fluent release 2021 R1 with a single-phase model that used nearly spherical nanoparticles with a constant diameter of 20 nm and 50 nm for Alumina (Al2O3) and Ceria (CeO2), respectively. The investigation used a volume concentration of nanoparticles from 0.1% to 2% Vol and a Reynolds number range of 400-2000. The findings indicate that, in all situations for hybrid nanofluid, the Nusselt number increased due to an increase in the Reynolds number and the volume concentration compared to the base fluid (Deionized water DI). The improvement ratio between the hybrid nanofluids and conventional fluid at varying volume concentrations was analyzed. Alumina and Ceria hybrid nanofluid with a 2% volume fraction showed the most increased heat transfer ratio, around 13.12%, compared to DI water. The outcomes of this study might be used to fabricate a new generation of nanofluids

Despite the widespread adoption of Global System for Mobile Communications (GSM) in Iraq, the network's capacity and performance limitations have become increasingly apparent, particularly in urban areas with high user density. Currently, LTE (4G) is the dominant mobile technology in Iraq, provides faster data speeds, improved coverage, and enhanced features compared to CDMA. GSM network has still function in rural areas with low population density using GSM infrastructure as Time Division Multiple Access (TDMA), and Frequency Division Multiple Access (FDMA). This paper explores the potential of Code Division Multiple Access (CDMA) technology to enhance the GSM system in Iraq instead of TDMA, and FDMA, addressing issues such as limited capacity, interference, and inefficient spectrum utilization. This study conducted a comprehensive simulation of a GSM network integrated with CDMA technology using a state-of-the-art network simulator. The simulation was designed to evaluate the performance of the enhanced system under various traffic loads, channel conditions, and interference scenarios. The integration involved the allocation of specific frequency bands for CDMA operation, the implementation of CDMA spreading codes, and the optimization of power control mechanisms. CDMA is particularly well-suited for the GSM system in Iraq due to its inherent advantages in handling interference and managing spectrum efficiently. Unlike TDMA, FDMA, which are primarily used in GSM in Iraq, CDMA spreads the signal across a wide frequency band, making it more resilient to interference. This characteristic is especially beneficial in Iraq, where the spectrum is congested and interference levels are high. Additionally, CDMA's ability to dynamically allocate resources based on user demand ensures efficient spectrum utilization and improved capacity. The simulation results demonstrate that the integration of CDMA into the GSM system in Iraq can significantly enhance network capacity, reduce interference, and improve overall performance. The enhanced system is capable of handling higher traffic volumes, providing better quality of service for users, and supporting emerging applications that demand high data rates and low latency

Brain neurons activate Human movements by producing electrical bio-signals. Neuron activity is used in several technologies by operating their applications based on mind waves. The Brain-Computer Interface (BCI) technology enables a processor to connect with the brain using a signal received from the brain. This study proposes a drone controlled using EEG signals acquired by a Neurosky device based on the BCI system. Two active signals are adapted for controlling the drone motions: concentration brain signals portrayed by attention level and the eye blinks as an integer value. A dynamic classification method is implemented via a Linear Regression algorithm for attention-level code. The eye blinking generates a binary code to control the drone's motions. The accuracy of this code is improved through Artificial Neural Networks and Machine Learning techniques. These codes (attention level and eye blink codes) drive two controlling layers and manipulate nine possible drone movements. The experiment was evaluated with several users and showed high performance for the classification methods and developed algorithm. The experiment shows a 90.37% accuracy control that outperforms most existing experiments. Also, the experiment can support 16 commands, making the algorithm appropriate for various applications.

Early diagnosis of breast cancer is critical for effective treatment and reducing mortality rates. Computer-aided diagnosis tools have become essential for identifying and diagnosing cancer in its initial stages. Convolutional neural networks (CNNs) have shown significant promise in medical image analysis, aiding in the detection of cancer cells and the classification of histopathological images through advanced data processing techniques. This study introduces a novel framework that combines transfer learning (TL) with an Incorporation of Prior Knowledge algorithm for multi-class classification of breast cancer using histopathological images. A new dataset comprising 3,600 sub-image histopathological images is presented, generated from the original Bach dataset. The study evaluates various pre-trained deep neural networks, including Inception V3, VGG19, GoogleNet, ResNet 101, and NASNet. Notably, the integration of prior knowledge and the focus on sub-image classification rather than whole images significantly enhanced cancer classification accuracy. The proposed method, leveraging the NASNet architecture, achieved a remarkable classification accuracy of 98.61%. Additionally, this study advances beyond conventional classification tasks by investigating tumor localization within breast cancer, utilizing sub-image analysis to improve diagnostic precision and support effective clinical decision-making. This innovative approach enhances classification performance and contributes to more accurate tumor localization, thereby significantly improving diagnostic capabilities in breast cancer detection

In construction industry of today, the use of concrete types which have superior characteristics compared to normal strength concrete (NSC) has attracted significant interest worldwide. The popular general term to describe a special type of concrete with superior characteristics is high performance concrete (HPC). This paper presents a review about reactive powder concrete (RPC), which is a type of HPC that is used in certain applications and environments. The RPC mainly consists of cement, fine aggregate, silica fume, superplasticizer and water. Steel fibers are usually added for ductility purposes. The review presents the RPC in terms of mix design, compressive strength, the main components, use of fibers and durability. Then, the review reports the applications and experimental studies of the RPC in infrastructure including recent studies for the RPC as a new repairing-strengthening material. Last, the significance of the review and conclusions are highlighted