KUFA JOURNAL OF ENGINEERING

As a result of stereotypical or morphological changes in cities, new cities have appeared with rules that are disconnected from the existing city center physically and intellectually, and those rules prevented the interconnection of the master plan areas with each other. The urban interconnection of new cities is of great importance in meeting the needs of current and future individuals. This work aims to achieve urban interconnection between the new city center and the current city within a cohesive and integrated framework design based on the most important design strategies of urban democracy. The research begins with the definition of the malleable A renewal framework and its impact as a research topic. Then, a review and discussion of a set of studies crystallized the research problem of "the lack of comprehensive knowledge of the impact of the malleable renewal strategy in city planning in general and in achieving urban interconnection between the centers of new cities and the existing city in particular", on the basis of which the research objective was determined in identifying the most important terms of the malleable renewal strategy within the design strategies of urban democracy in the city planning and linking them with new cities, to build a comprehensive theoretical framework. The research adopted the descriptive analytical approach in building the theoretical framework, then applied it within the practical study. The results of the survey presented that the local project achieved good results. With regard to the obvious impact of this strategy in the development of the master plan of the city of Baghdad by integration and spatial interconnection with new cities as a set of contextualized targeted measures, positive change, and patterns of intervention in urban morphology as a model that harmonizes with local reality and achieves originality, diversity and creativity of architectural and design products. Intervention at various levels within the master plan of the city of Baghdad contributed to the upgrading of random or degraded urban areas, achieving organically interconnected cities through organic mechanisms (organizing, tension, and reconstruction), structurally interconnected cities through structure mechanisms (improvement, creation, and redevelopment), and networked interconnected cities through network mechanisms (innovation, rhythm, and extension)

Generalized frequency division multiplexing (GFDM) is one of the most promising candidate Multiple Access technologies for the 5G of wireless communication, as it shows great potential and benefits to help create a networked society for the next generation. GFDM is a wave technology that shows it's advantages through flexibility in spectrum usage, to adapt to the requirements of channel conditions, low latency, noise reduction, as well as reduction of Out-Of-Band (OOB) emissions. In this paper, an overview of the development and performance analysis of GFDM in the next generation is presented, and a group of technologies that have been integrated and used with this technology are discussed, such as Multiple Input Multiple Output (MIMO) systems, channel coding, channel estimation, pulse shaping filters, and many other technologies. As well as other technologies that will improve the performance of wireless networks. The strengths and weaknesses of this technology will be discussed, and an analysis of the system's capabilities in terms of bit OOB, Peak-To-Average Power Ratio (PAPR), Bit Error Rate (BER), and Signal-To-Noise Ratio (SNR) will be performed. In addition to discussing researchers' visions for other technologies with GFDM and the future dimension of GFDM research

Root mean square (RMS) values must be measured precisely in the present era in order to improve the performance precision and high dependability of wireless communication equipment. The usual method of measuring RMS, which involves calculating the average of the squares of the input signal samples, has been the subject of numerous research articles. However, there are certain disadvantages to this approach, particularly when working with harmonic signals. In this paper, three formulas are proposed to estimate the systematic error in RMS measurement to contribute to solving this problem. The proposed formulas take a new approach and demonstrate the effect of the number of input signal samples and the type of domain used (time or frequency) on measurement accuracy. To demonstrate this change, graphs were created using MATLAB simulation. In this paper, the Gaussian window was tested in both the time and frequency domains, and the study demonstrated that the RMS error can be significantly reduced if the correct parameter is chosen and a suitable sample size is used for both domains. At an SNR of 30 dB, the impact of sample size on the outcomes is also described. When the simulation parameters were set to Fc/Fs = 0.25, the MEE findings were (0.00029, 0.000016, and 0.0000054) for sample sizes of 16, 32, and 64, respectively. The results obtained in this paper demonstrate the importance of selecting appropriate parameters for each application, confirming and enhancing the applicability of this research in most applications to support the high performance, high accuracy, and reliability of modern wireless communication devices. Several techniques and methods in this field have been reviewed, and the current RMS evaluation error was found to be approximately 10-4 (Sean Reiter and Stephen W. 2024), while the methodology proposed in this paper produces lower errors of approximately 10-5

In recent years, lie detection has been a prominent focus for many researchers due to its significant influence on criminology and society. A key challenge in numerous investigations has been the system\'s accuracy, which is heavily reliant on the type of algorithm employed in the classification process. Another critical factor affecting accuracy is the signal-to-noise ratio (SNR). In this paper, we explore a deep learning framework within the lie detection test paradigm. During the signal acquisition phase, EEG signals were recorded using the Natus device. Independent component analysis (ICA) was subsequently applied to remove noise during signal processing. Additionally, regression baseline correction was used to adjust the baseline, followed by averaging to generate event-related potentials (ERP), which resulted in a 23.2% improvement in SNR. Several algorithms were also employed, including support vector machines (SVM), deep belief networks (DBN), and linear discriminant analysis (LDA). Our proposed approach achieved an accuracy of 86% when using the same dataset from previous research, while new data collection from several participants yielded an accuracy of up to 78%

In this work, the effect of the support addition to LaNiMnO3 perovskite catalyst is investigated. Perovskite catalysts are prepared via the sol-gel method, namely LaNiMnO3 (LNMO3), LaNiMnO3/MCM-41 (LNMO3/MCM-41), and LaNiMnO3/TiO2 (LNMO3/TiO2). The catalysts are characterized using BET, TGA, XRD, FE-SEM, EDS, and FTIR techniques. The catalytic performances of those catalysts are evaluated in the catalytic dry reforming of methane to produce synthesis gas (H2 and CO). The results of this study indicate that the LNMO3/MCM-41 presents the highest rates of conversion for CH4 and CO2, with 80.56 and 76.32%, respectively. The optimal temperature of reaction to obtain the highest conversion of syngas is 800 °C. The order of activity and stability can be presented as LNMO3/MCM-41> LNMO3/TiO2 >LNMO3. To conclude, this research develops new catalysts with high catalytic performance to improve syngas production from the reaction of methane with carbon dioxide

Hair loss impacts approximately 80 million people in the United States and arises from factors such as aging, genetic predisposition, stress, and medication. In many cases, early signs of scalp and hair disorders remain unnoticed, delaying timely diagnosis and treatment. Neural networks, particularly in image-based diagnostics, have shown great promise across medical domains, including oncology and dermatology. This research investigates the application of deep learning for identifying three major scalp conditions: alopecia, psoriasis, and folliculitis. Major challenges included limited literature, a small dataset, and non-uniform image quality from online sources. To address these, the study compiled 150 scalp images and employed preprocessing techniques such as denoising, histogram equalization, augmentation, and class balancing. A two-dimensional Convolutional Neural Network (CNN) was trained on this dataset, achieving a training accuracy of 96.2% and a validation accuracy of 91.1%, with consistently high precision and recall across all classes. Additionally, a new scalp scan dataset was introduced to support future research, contributing to the development of AI-driven diagnostic tools for hair and scalp health assessment

A sustainable approach to improving the properties of high-strength concrete is essential for the construction industry due to its excellent properties, such as high compressive strength, reduced dead load of the concrete structure, reduced construction cost, and environmental friendliness. The aim of the present work is to use a new technique (fresh concrete mixture treatment with magnetic field) to enhance the properties of HSC. The magnetic method is considered one of the techniques that develop mechanical characteristics of concrete, either by utilizing magnetic water to mixture the concrete mixture or by magnetizing the mixture itself. This research studied the influence of a magnetic field on characteristics of high-strength concrete. The experimental work was done in two lines; one was magnetizing fresh, high-strength concrete by passing the concrete mixtures through four devices. The first three devices contain (3, 4, and 5) magnetic units, respectively. Each magnetic unit has 3000 gauss intensity. The last device was a tube without any magnetic units. The second line was exactly the same as line one, just for one thing: the mixing water was magnetic water instead of tap water. When the mixtures are subjected to a magnetic field, The slump and compressive strength did not improve significantly and the values were variable. The highest increase in slump was achieved 4.7%for the magnetized mixture with the device equipped with 4 units (F3). The compressive strength of the mixture passing during the pipe equipped with 5 magnetic units (F4) was increased by percentage 7%at 28 days. As soon as magnetic water was utilized in place of ordinary water, the slump value was increased to 19% from the reference mix

The present study evaluated the impact of chemical pre-treatment on biogas and methane generation from corn and wheat stalks. Three types of animal manure were used as a source of bacteria (cow, cattle and ostrich manure). Chemical treatment using sodium hydroxide led to an increase in biogas and methane generation from wheat and corn stalks, where the increase for wheat stalks pretreated with cow, cattle and ostrich manure was (468.28; 469.08%), (242.51; 230.39%), (317.90; 295.77%) respectively compared to the untreated sample. As for corn stalks, the increase for biogas and methane with the same wastes above was (345.28; 444.59%), (230.90; 291.20%), (277.99; 297.011%) respectively compared to the untreated sample. With a correlation coefficient of more than 96, the findings also demonstrated that the values that were measured and the values that were derived by using the modified Gompertz model were in accord with one another

This paper characterizes interval-valued anti-neutrosophic fuzzy ideals (IVANFIs) within near-rings, addressing the gap in algebraic theory related to their behavior in such structures. We define the direct product, complement, and factor group of IVANFIs and explore their properties through examples. The study combines interval-valued fuzzy sets with anti-neutrosophic logic, offering a flexible framework for handling uncertainty. By examining key properties such as homomorphisms, union and intersection behaviors, and relationships with other fuzzy ideals, the paper enhances the understanding of these structures and their applications in decision-making under uncertainty

Heat exchangers are managed in numerous topics like motorized, training, turbine, energy plants, and water managements. A radiator is a heat exchanger of that category, when the air emerges surrounding the circle of the finned frames, and cools the fluid which requirement be abated. A small variation has been presented on the radiator intention to make it more effective, where the recent creation is a helical tube radiator lesser than the fin and tube air cooled radiator commonly applied today, and outstandingly in vehicles. The detached of this investigate is to measure the implementation distinction relating the function of a helically coiled heat exchanger to that of consecutive tube heat exchanger. From the upstairs it is assumed that heat transfer in helical spherical tubes are superior than in the perpendicular tubes due to the helical characteristics of the tubes. This explains why on the outer side of the pipe fluid moves slightly faster than on the inner side, this is commonly referred to as the curvature effect. Hydro experimental proves to reveal the manner, in which the effective thermal performance of helical coil radiator depends on the value of the water flow rates. The analysis was executed via the evidence records with WIT = 60 °C, AIT = 20 °C, and WFV extending from 0. 5 to 5 gallons per minute (gpm) Stylish and Modern. As of the overhead extension, it can be assumed that for The Vertically Helical Coil radiator, the relating fluctuation of the water instrument heat enhances with stream speed is minor than that of Stretch Tube Radiators. This indicates that Vertically Helical Coil radiator might have a greater heat transfer property as contrasted to the flat tube at shorter flow rates

This study investigates the methodology of using the capability of Artificial Neural Networks (ANNs) as an alternative approach for modeling nonlinear inductors, nonlinear resistors, and power electronic circuits in electrical power systems. While traditional physical modeling approaches face challenges due to the complex dynamic behavior of these elements and power electronic circuits. In comparison, physical models offer accuracy but require extensive time and detailed parameter knowledge. In contrast, the ANN-based methodology is considered a powerful and efficient solution for enabling approximation of any nonlinear component, simple implementation, and fast computations. The research explores diverse ANN architectures (nonlinear autoregressive network with exogenous inputs (NARX), A feedforward neural network (FNN), and A recurrent neural network (RNN)) to model nonlinear inductor, resistors, diode, and a Buck converter. The investigation demonstrates the effectiveness and versatility of ANN-based modeling in predicting and optimizing nonlinear behavior for the nonlinear components

In this research paper, two models of different power plants with similar operating capacity were discussed using the Homer Software. One of these stations depends on fossil fuels in its operation, and the other depends on ethanol gas, the biofuel extracted from the fermentation process of Zahdi dates. The estimated location of the station was determined according to the program system in Baghdad Governorate randomly without choosing a specific location. The type of station is a substation that supplies a fixed load. The estimated costs, electrical generation capacity, and the amount of fuel consumption were deduced, in addition to the benefit resulting from reducing environmental pollution due to the combustion of biofuel ethanol gas, which is considered one of the environmentally friendly gases. All these calculations and conclusions were made by using the easy programming of the Homer Software system. The results will be modeled and concluded by program and it give higher power generation for biogas substation about 2,190,000 kw/hr/yr with capacity factor about 50%

Industrial sound analysis plays a key role in machine monitoring systems, as experienced machine operators can hear and detect many rotary machine faults. However, due to factories having complicated noise environments, they are challenging to detect. Detecting rotary motor faults is important to reduce repair costs, avoid unexpected machine downtime, and stop production. One of the primary approaches for fault diagnosis involves deep learning (DL) techniques that classify the fault sound produced by motors. This research proposes a method to detect and classify faults using a Convolution Neural Network (CNN) based on converting sound to a gray image. First, converts the 1-D time domain vibration sound into a 2-D gray image depending on Discrete Cosine Transform (DCT) to extract feature patterns by dividing the 1-D matrix of coefficients resulting from the DCT after normalization into equal parts and storing them in a column followed by a column in a 2-D matrix whose dimensions have been determined. Second, these gray images are utilized as input for CNN, which performs multi-classification in a supervised learning framework. (IDMT-ISA Electric Engine) dataset is used to demonstrate the effectiveness of the suggested approach. The results show high classification Accuracy 99.7%, Precision 99.6%, Recall 99.6%, and F1-Score 100% with effectiveness in diagnosing faults under various noise conditions associated with sound recording. This study could have an impact in the field of sounds analysis

The Stream Quality Model (QUAL2KW) used for simulation the Quality of Euphrates River Water. The study area extends along (78 km) of the river path from downstream of Ramadi Barrage to upstream of Falluja Barrage. Tharthar-Euphrates Canal carries the dead storage water from Tharthar reservoir to the Euphrates River at the meet point (52 km) from downstream of Ramadi Barrage. Statistical tests conducted between values of water quality parameter concentrations obtained from sites with the values simulated by this model. The results of statistical tests showed great convergence and gave reliability and credibility in adopting the use of this model for river quality simulation. To evaluate the effect of using the reservoir dead storage water on the river water quality, model operated with scenarios represented different releases of dead storage water by (30%, 60%, and 100%) lead to increases in these quality parameters concentration by (3%-9%), (5%-14%) and (8%-19%) respectively, which does not significantly affect the Euphrates River’s water quality. The results of applying the various scenarios showed that 30% is optimal for maintaining the quality of river’s water for drinking purposes after performing the required treatments, and 100% for maintaining the quality of river’s water for irrigation purposes without affecting the various plants

Recently, image content based information retrieval has been widely used in many applications. A Mobile Visual Search (MVS) system involves capturing an image for the object to be searched for, extracting useful features, constructing object signature and searching local and/or remote databases for a match. This paper investigates the diverse effects of signature encryption on the accuracy, search burden and response time of an MVS system. The aim of this research is to provide a comprehensive understanding of the implications of signature encryption to figure out the design trade-offs that facilitate better MVS performance under given operation hypotheses. An MVS system employing different signature encryption algorithms is simulated with different system parameters. The obtained results show that signature encryption generally adds to system processing time from 5.9 µs to about 22.7 µs in test cases 4 and 6. Moreover, it makes the MVS system more sensitive to imaging and communication link imperfections leading to less capability to correctly identify entries and increased search and transmission loads from 1984 to 8075 requests to access the main database for test cases 3 and 9. These effects depend on the features of the specific encryption algorithm. Therefore, a trade-off between system performance and required security level should be considered to achieve a target performance suitable for the intended application

The purpose of this research is to evaluate the impact of different reinforcements, hollow shapes, and fire on ten lightweight concrete slabs that are reinforced with glass fiber reinforced polymer (GFRP) in order to decrease their weight. All slabs were uniformly sized at 2400 mm x 1000 mm x 150 mm. They were subjected to continuous static load tests and heated from below using a fire chamber to evaluate their fire resistance performance. The study analyzed three reinforcement types (steel, hybrid, and GFRP) and two hollow shapes (circular and square). The findings showed that increasing the GFRP reinforcement ratio and the presence of hollows led to diminished fire resistance and flexural strength compared to solid reinforced concrete slabs. Specifically, the quantity of GFRP reinforcement increased the slabs' deflection, up to a maximum increase of 1.5 times that of the steel-reinforced slab. The slabs' ultimate capacity dropped by around 35% in terms of load capacity. The decrease in stiffness was much more noticeable. Experimental fire exposure results indicated that slabs reinforced with GFRP bars exhibited significant degradation after just over fifty minutes, contrasting with solid reinforced concrete slabs, which withstood the two-hour test. The core focus of this research is to characterize the structural response of GFRP-reinforced lightweight hollow slabs subjected to concurrent fire and stress. The computational models created herein also provide a basis for future analysis, enabling the evaluation of different fire scenarios on specific structural elements or the holistic simulation of a full structural system with realistic boundary conditions

Pressure loss coefficient of laminar flow across 30°, 45° and 60° mini scale mitre bends investigated for the first time by using CFD simulation. Various flow velocities between 0.05 and 2.2 m/s and three different pipes’ diameters 1, 2 and 3mm were used to obtain laminar flow with Reynolds numbers range between 150 and 2190 for each mitre angle. More than one hundred case studies were used in the study. The extracted data from the simulation were employed to compute the key values theoretically for each case. The friction factor results of the present work agrees with the experimental values from the literature with less than ±5 % average error. The obtained data of mitre bends’s pressure drop illustrate the independency of the Pressure coefficient K on both of the flow Reynolds numbers and the pipe diameter for the employed pipe sizes and flow conditions. The results obtained from this work verify the independent influence of the mitre angle on the pressure loss coefficient. New values of pressure loss coefficient for mini mitre bends are found, and they have 39-59% larger values than those for normal size mitre conduits

Dynamic task allocation is an essential aspect of robotics especially when achieving frequent mobility or running sensitive tasks that must be done with extreme precision and efficiency. Several objectives such as achieving high accuracy in task execution, accurate path mapping, successful target reach, precise trajectory prediction and optimized control which are increasingly facilitated by the integration of cloud computing. Traditionally, multi-agent robotic systems have been utilized where the overall path is divided into smaller partitioned tasks, then each robot is assigned a specific task and the entire system is optimized using variety of hybrid optimization algorithms. Neural networks are used for automated prediction, while cell decomposition methods help in task allocation and path planning. In this study, we introduce an effective method by using a single-agent robot with cloud computing. We highlight the advantages of this method over traditional multi-agent systems particularly in scenarios requiring comprehensive path information. Our approach leverages optimization techniques, cell decomposition and Elman neural network to streamline task allocation and execution. The results show that the proposed approach achieved accuracy about 92% with task completion time around 8 minutes. Although the multi-agent robot performed higher accuracy nearly to 95% and less time about 5 minutes, the single-agent robot significantly reduced a computational load and energy consumption. This approach consumes 45 Watt-hour (Wh) compared to the multi-agent strategy which consumes more energy 60 (Wh). In conclusion, this review article improved the efficiency and effectiveness of dynamic task allocation and path planning in robotics and offered a responsive solution for complex environments

One of the primary causes of death globally has been traffic accidents involving vehicles on public roads. To address this issue, we introduce Capsule Recurrent Neural Network (CapRNN), a new deep learning architecture designed specifically to improve road accident prediction performance. CapRNN uses capsule networks for capturing spatial relationships and recurrent neural networks for capturing temporal patterns. A series of experiments has been performed with CapRNN on a large-scale dataset of U.S. traffic accidents from 2016 to 2023 and compared it with other typical models. The result was the 95.82% accuracy of CapRNN, which was the highest one. The standard LSTM and Bi-LSTM models could only achieve 93.58%, respectively. Also, the performance of CapRNN was higher than that of XGBoost (95.73%) and Random Forest (95.02%). Besides accuracy, recall was also high for CapRNN (95.82%) and precision was also high (95.24%), i.e., it was stable at different time resolutions. This finding supports our claim that hybrid deep learning models like ours have the potential to effectively solve the spatiotemporal nature problem of road accident data. CapRNN's improved prediction accuracy could be used directly to make public safety policies more efficient by enabling them to better allocate the resources devoted to traffic management and urban planning. We, therefore, propose further steps: testing the model on other data sets, making it more interpretable, and, finally, incorporating real-time data to strengthen predictions

The selection and assessment of single-photon detection modules is a crucial problem in satellite-based QKD systems. The system's overall efficiency, secure key rate and quantum bit error rate are all significantly influenced by single-photon detection modules. There is a knowledge gap about the practical performance of commercially available single-photon detectors because existing research frequently relies on theoretical characteristics. This paper introduces a study on the effect of the parameters of three commercial single photon detection modules from ID Quantique company: ID Qube, ID100, and ID281 on certain Bennett-Brassard 1984 protocol parameters such as secure key rate, mean photon number per pulse, quantum bit error rate, link efficiency and zenith angle with the presence of link and geometrical losses for a downlink geostationary satellite. The study accounts for link and geometrical losses, zenith angle, and mean photon number per pulse. Results indicate that the ID281 detector outperforms the other two in maintaining a better secure key rate and quantum bit error rate, while ID100 shows superior performance in shorter wavelength ranges suitable for downlink operations. The findings suggest that global quantum key distribution coverage is achievable using geostationary satellites. To the best of our knowledge, there has been no prior study that has introduced using real parameters of commercial SPDMs to evaluate secure key rate for quantum key distribution systems based on downlink geostationary satellite

Non-Orthogonal Multiple Access (NOMA) is proposed for huge growth of 5th generation communication systems and beyond. It has the ability to benefit various applications, including future cellular networks like; Internet of Things (IoT), machine-to-machine (M2M) and device-to-device (D2D) communication. It is particularly suitable for scenarios with a high data traffic and large number of connected devices, as it offers efficient spectrum utilization and improved connectivity. In general, any Simulink software provide the researchers with the ability to evaluate various performance metrics of the system. NOMA system was implemented in this work via Matlab in his both (m. file) and (Simulink Block), and many cases were studied and evaluated. Where 2-users and 3-users cases are applied for different values of; user’s distance from the base station, bandwidth, power allocation coefficients, modulation type and different noisy channels. The bit error rate (BER) versus two important parameters i.e. signal to noise ratio (SNR) and the transmitted power were measured and evaluated. Also fixed power allocation (FPA) and Dynamic power allocation (DPA) for the coefficients were implemented and tested for both cases; far and near user in DL–NOMA (down link). The enhancement value that achieved in outage probability for DPA over FPA is equal to 7.5 bps/Hz in far user case, while 6 bps/Hz is achieved in near user case

Image steganography constitutes a specific form of steganography wherein an image is employed as the concealing medium. Medical Image Steganography represents a distinctive subfield within the broader domain of Image Steganography. The safeguarding and confidentiality of sensitive patient data necessitate heightened scrutiny and scholarly investigation. Consequently, numerous techniques have been introduced over the past twenty years aimed at concealing patient information through the utilization of image steganography. In the present study, a new methodology is introduced for medical image steganography, leveraging Discrete Wavelet Transform (DWT) in conjunction with Particle Swarm Optimization (PSO). A Low-Density Parity Check was employed to encode the medical data prior to its concealment within the cover medical image. The experimental analysis was conducted on four distinct medical imaging modalities, namely X-ray, MRI, CT scan, and Ultrasound images, with the evaluation of performance based on metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM). The findings of this investigation suggest that the PSO algorithm significantly enhances the efficacy of steganography, particularly when integrated with DWT techniques

In the present work, the effect of inter-critical heating temperatures on the micro-structure, microhardness, and wear properties of AISI 1018 steel has been investigated. AISI 1018 steel was heated at three different temperatures 750°C, 775°C, and 800°C inter-critical temperatures for 30 min and followed by hardening in salt water. The results of this study showed that the concentration of martensite phase increases by incrementing inter-critical temperature as 36% Vm. till 70% Vm. At 750°C and 775°C there is one-stage of work hardening has taken place, while two stage have taken place at 800°C. The exponent of work hardening (n) increases with increments of temperature by 0.239 at 750°C, 0.302 at 775°C, and 0.391 at 800°C. While the mechanical properties, such as microhardness, yielding strength, and tension strength have been increased by 162 (HV), 599 MPa and 800 MPa respectively, with an increment of the concentration of the martensite phase till 50%, at which the concentration of the martensite phase is equal to the concentration of the ferrite phase and then slightly increases. Finally, the results of wear behavior reveal that the rate of wear decreases with increasing the temperature of heat treatment by 60×10-7 to 30×10-7, as shown in Fig.9. The photomicrographs of worn surfaces have shown that the grooves of the sample heated at 800°Care finer than for the samples heated at 750°C and 775°C owing to the increase in the concentration of the martensite phase

Concrete slabs of (800×800)mm and a thickness of )100(mm resting on Winkler support, for subgrade reaction of (0.028)N/mm3, and strengthened with different types of synthetic fiber meshes, that are fiberglass, polypropylene, carbon, geogrid, and waste rubber, are used to study the characteristics of toughness, stiffness and ductility index of these slabs incorporated with these meshes. The addition of these fibers increases the rigidity of slabs resting on the ground, to reduce the damage as minimum as possible, which keeps pace with the current development of using these materials. The experimental results show that the toughness and stiffness have been significantly improved in all the specimens strengthened with these meshes, especially for the specimens for carbon fiber mesh, where the increase was 187.0% and 33.7% respectively. The lowest increase percent in toughness of 12.9% in the case of strengthening by waste rubber, and the lowest increase percent in stiffness of 8.1% in the case of strengthening by polypropylene mesh While the specimen reinforced with geogrid showed a weakness in the ductility index (2.0), compared to the highest value for the models reinforced with carbon fibers of (3.3)

Internet of things is significantly important in different applications including health and sport. Size and weight in wearable sensors are very important factors the designer should consider. Designing an adaptable and scalable system using wearable wireless sensor nodes that can be used in different applications with low cost, and low energy consumption is a challenge. This paper focuses on building an affordable wearable wireless embedded system for health and sports applications based on the Internet of Things (IoT). A novel multi-purpose IoT-based node of less than 10$ with a diameter of 4.2cm and 12g of weight (25g with GPS) is presented. Fall detection using Machine Learning (ML) is considered for the health application, achieving 99% accuracy. Moreover, for sports, a real-time data gathering and analysis is performed, also simulated scenarios using OPNET showed a latency of less than 50µs at 54Mbps. Data for both applications are stored in a MySQL database and can be managed using a web server. Generally, the node operates for about 12 hours with a 240mAh battery, and methods to expand battery life are discussed. Additionally, a robust data security model is presented. When compared to alternatives, the system proves its feasibility, adaptability, scalability, cost-effectiveness, low energy consumption, and high performance

The classification of bone fractures in medical imaging distinguishing between fractured and non-fractured bones is one of the most critical and difficult tasks. Traditional manual examination is prone to errors, especially in cases of tiny or minute fractures. Similarly, these tiny fractures are a big challenge for deep learning models, which struggle to detect and classify them effectively due to their size and variability. In this paper, we propose a new augmentation method called the Cropping Small Fracture Patches (CSFP) aimed at enhancing the representation of small fractures within training data. Our approach integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image contrast, followed by a targeted patch-based augmentation strategy that extracts small fracture regions, upscales them using Lanczos interpolation, and then combines them with original train images to enrich the dataset and used to train the YOLOv8m classification model. The proposed methodology was applied to the FracAtlas dataset and the result demonstrates a significant improvement in classification performance achieving 100% accuracy outperforming previous methods. These findings confirm the effectiveness of this approach in improving model performance; by enhancing small fracture visibility, reducing class imbalance, and improving the diversity of the dataset. However, certain limitations exist. The augmentation method relies on bounding box annotations, which may not always be available in classification datasets. Additionally, this study currently focuses on binary classification (fractured vs. non-fractured) and does not classify fracture types or anatomical locations

Herein we report a simple way to improve the thermochromic behavior of polydiacetylene (PCDA) by the incorporation of surfactants. modified by the addition of anionic (SDS), cationic (CTBA) and nonionic (Triton X-100) surfactants in different ratios. Polymerization was performed at room temperature by UV irradiation. We evaluated the effect of surfactant type, concentration, and temperature (25°C, 36°C, and 40°C) on thermochromism. Ideal color transitions (from blue to red at less than 40°C) were done with 0.012g SDS, 0.015g CTBA, and 1ml Triton X-100. SDS-modified PCDA was identified for textiles by CIE-Lab* results, whereas CTBA and Triton-X-100 modified PCDA for as safety indicators and smart packaging applications. Results of particle size analysis shows that SDS form the largest particle sizes with the PCDA polymer, where its D50 is 566.9 µm compared with 1.153 µm and 4. 671 µm for CTBA and Triton respectively. Using DFT calculations, it is determined that there is an increase of energy when adding a surfactant and that electronic properties of PCDA are altered with it, where it shows a decrease in energy gap, which suggests potential uses in thermoelectric materials development, sensors and in measuring temperature

The online monitoring of work environment plays significant role in contemporary industry automation by utilizing internet-of-things (IoT) for different measures, including security, operation efficiency, and proactive response. This article proposes an IoT technique to monitor the environment air temperature, humidity, and the required safety concerns such as fire alert and security access control using video recording. The system hardware consists of DHT22 sensor, smoke detector, and motion sensor which are all integrated using ESP8266 microcontroller and wireless router as access point to internet. The proposed IoT approach is developed using variable scheduler scenario for adjusting the temperature and humidity level of the environment based on particular season of the year. A GUI dashboard is implemented based on Blynk application to display the real-time sensors’ readings and changing the operation status of some IoT devices. The operation and functionality of this IoT application is validated and tested inside a dedicated laboratory to mimic different environment conditions. While maintaining convenience air temperature, the proposed approach achieves 8%-14% improvement in power efficiency of the air-conditioning unit

This study aimed to improve the morphological properties of Sikadur 52 epoxy resin by incorporating polyurethane (PU) and liquid silicone rubber (LSR) as toughening agents. The modifiers were added at various weight percentages (3, 6, 9, and 12%) and cured with a hardener. SEM analysis revealed microphase separation within the epoxy matrix, with distinct PU and LSR domains. FTIR analysis verified that addition of LSR strengthened some chemical bonds in the epoxy matrix, and SEM analysis provided direct information about phase separation behaviors of the modified epoxies. This micro-phase separation is believed to contribute to the enhanced mechanical properties of the modified epoxy resins

The main purpose of this study is to investigate potential energy savings by considering two different air-conditioning systems, constant refrigerant flow (CRF) and variable refrigerant flow (VRF) systems, in a new residential compound in Basra, Iraq, where extreme temperature and humidity are observed beside facing a shortage in electrical power supply. One- and two-storey houses were considered, and the initial, installation, maintenance, repair, and energy costs for each AC system were accounted for. The required cooling loads were calculated using Carrier's Hourly Analysis Program (HAP 5.1). The findings revealed that although the VRF system exhibited a higher initial price than the CRF system by 20 % and 41 % for one- and two-storey houses, respectively, its monthly operating cost was lower by 16 % and 19 %, respectively. The energy savings percentage achieved using VRF compared to CRF in the 10th year was 20 % and 22 % in one- and two-storey buildings, respectively. The results demonstrated that implementing VRF systems can save power and minimize electricity waste in residential buildings, which may amount to significant savings in electricity consumption in buildings and compounds constructed in the future

In transportation planning and engineering projects, the process of calculating and estimating the origin-destination (O-D) matrix is ​​very important, to understand how travel demand occurs within an urban area. In this review, some of the main methods and techniques used in origin-destination matrix estimation are summarized and explained, highlighting some of their objectives, applications, results, and sometimes their limitations. It should be noted that in each method of origin-destination matrix estimation, there are challenges, including issues related to the accuracy of data collection and complex calculations, as well as issues related to local standards. In short, it can be said that the process of origin-destination matrix estimation depends on the accuracy and availability of data. In the future, data collection techniques are expected to develop further and improve the accuracy of data collection, which is an important issue in the process of origin-destination matrix estimation

The drip irrigation method is one of the highly efficient methods to provide irrigation water in arid and semi-arid areas, and subsurface drip irrigation is the best in terms of reducing evaporation from the soil surface. The study aimed to follow up the progress of the wetting front in three directions perpendicular for two adjacent emitters to determine the best distances between emitters, then reduce deep percolation losses and provide the root area with the required moisture. Soil studied was Sandy loam with primary moisture content as follows: 3%, 7%. Emitters were buried under 20 cm of soil surface with a basic drainage rate of 2.5 liters/hour. Three distances between emitters are 25, 35, 45 cm. Empirical equations were derived to find the progress of the wetting front when changing the equation conditions to obtain an optimal modeling. The progress at the middle of the spacing of Z up and Z down was different. The confluence of the two wetting front at moisture content 0.08 was faster than 0.06 and 0.04 until the end of the experiment time for the same spacing between emitters. Increasing the spacing between the emitters from 25 to 55 cm reduced the chance of the two follicles meeting by 30%. Vertical progress below and above the emitter was not affected by the spacing change

This paper aims to investigate the performance of geopolymer adhesive (GPA) as a substitute for epoxy in the near-surface-mounted (NSM) technique to enhance the shear strength of reinforced concrete (RC) beams. Seven RC beams of 1200 mm in length, 120 mm in width, and 160 mm in height were tested under three-point loading. The specimens involved one control beam and six strengthened beams. This paper investigates several parameters, namely the types of paste adhesive (epoxy or geopolymer), spacing of NSM bars (80 and 65 mm), and inclined NSM bars (90° and 45°). The initial crack opening load, ultimate load-carrying capacity, deflection, and crack pattern. According to the results, the epoxy adhesive can increase load capacity by up to 96.36%. Geopolymer adhesive yielded a maximum of 68.62% compared to un-strengthened RC beams. The first crack load on strengthened specimens was increased by up to 18% compared to un-strengthened RC beams

Due to the effect of changing the angle of incidence of solar radiation on the surface of the photovoltaic panel in determining the amount of electrical production, the world is heading today to study this effect and work to achieve the optimal position of the panel to increase its efficiency. This includes studying and analyzing the angles of PV panel tilting, taking into consideration the change in the position of the sun (altitude angle). In this article, a system has been designed using MATLAB/SIMULINK software to simulate the work of a home canopy that exploits photovoltaic panels to generate electrical energy. The system is designed to process multiple data at the same time, ten tilt angles of panels are adopted, in addition to taking into account the change in the position of the sun (from 40ᵒ to 80ᵒ) in winter and summer respectively, every second over a whole year. The curves of the proposed model showed that the high solar elevation angle in summer of Iraq (which did not require significant tilting of the panels to obtain vertical radiation (only 10ᵒ-20ᵒ) allows for the benefit of panels installed on a flat surface, at least if the panels cannot be oriented at the required tilt angle. However, flat panels are not effective in the winter due to the need to orient them at a relatively high tilt angle (50ᵒ) to obtain vertical radiation. It is also possible to design a fixed canopy throughout the year with panels tilted at an angle close to the latitude of the station's geographic area, given the relatively high radiation values in Iraq throughout the four seasons. these results show realistic behavior with excellent accuracy, by reaching the required monthly and seasonally tilt angles, which confirm the importance of having accurate models that enable the users to estimate angles and production quantities under different conditions and locations

The increasing demands for electric power in daily life with the consuming power increasing day by day. The energy optimization that used in homes has grown importance in the era of smart technology. This will force us for a goal is to design and implement a wireless sensor network (WSN) for real-time consume energy monitoring and controlling in smart homes and save electric power that leads to sustainable energy by reduce the power consuming in electric device and to afford homeowners precise control over how much energy can be using, lower total consumption, and support in the creation of smarter, greener residential environments. the task of proposed system is to influencing the technology of advanced sensor and effective communication protocols so that electric power can be conserved throughout the deactivation of devices that are not in use. The methods used are: creating a systematic architecture for the wireless sensor network that is suitable for monitoring and controlling smart home energy consumption and indicating the accurate sensors for detecting various parameters. The system model consists of (microcontroller (UNO R3), temperature and humidity sensor (DHT22), electric device (fan) and Xbee as wireless communication module). The system saved the power throughout turn OFF Fan if the temperature of room below than 26 C° and turns ON if its exceeds or equal 26 C°. These events are monitoring and control by using internet of things (IoT). The system has two location area End Node and Base Station; the data sent from End Node to Base Station by Xbee modules in real time. The following results were obtained: the power in KWh has been calculated and it’s recorded about 1.43 kwh in one day and 42.9 kwh in 30 days (in April) and the cost reach 815.9 Iraqi dinar. Conclusions. If the model didn’t used the fan is in ON state in all days therefore the power reach 2.64 kwh in one day and 79.2 kwh in one month and the cost recorded 1504.8 Iraqi dinar. This paper emphasizes how WSNs can transform smart home energy usage monitoring and control. The usefulness of the arrangement and the encouraging results give prominence to how vital WSN technology is to improving sustainability and energy efficiency in residential settings

Chitosan is a biopolymer that is obtained by deacetylating the chitin that is recovered from different sources, such as marine and animal. Also, the waste of agriculture and the seawater industry are considered good sources for extraction. This waste may be an environmental pollution source as a result of burning and dumping or a good source for producing important materials such as chitosan which is considered a biocompatible, biodegradable, biopolymer that has many industrial, biomedical, and agricultural applications. Chitosan material, extracted from chitin, has many attractive biomedical properties. This review focuses on the extraction methods of chitosan from different production sources, taking into account industrial considerations, and summarizes them, taking into account their environmental impacts and marketing potential. Also, this article reviews—based on existing publications—the classes of chemical methods and reaction parameters used in chitosan preparation and biological methods

High-resolution imaging techniques are essential for accurately studying viruses and their effects. Due to their small size, viruses often require sophisticated imaging tools for successful detection and analysis. Microscopy techniques, such as electron microscopy or fluorescence microscopy, are commonly used to capture images of viruses. However, these images can sometimes lack contrast and detail, making it challenging to identify important structural features. This paper introduces a new method to enhance the gray-scale images of 36 electron microscope virus images by increasing contrast and brightness using dark channel prior and adapted histogram equalization used Otsu's segmentation. To determine the efficiency of the proposed method in improving microscopic images, it was compared with several other methods. The quality measurements demonstrated significant success, with entropy (EN) at 7.800, average gradient (AG) at 16.996, mean standard deviation (MSTD) at 44.321, and contrast enhancement measurement (CEM) at 0.8752. The results indicate the algorithm's effectiveness in preserving image clarity and enhancing its features in a superior manner

In engineering, a foundation is a crucial structural element that transfers load from a structure to the underlying soil. Foundation failure can have severe consequences, highlighting the need to understand and mitigate potential risks. To meet the growing demand for efficient and sustainable foundations, engineers are constantly exploring innovative designs that optimize material use while maintaining or enhancing load-bearing capacity. One such system that has gained popularity in recent decades is the piled raft foundation. This system offers a cost-effective and high-performance solution, particularly for challenging soil conditions. It is needed to highlight a general and broad information about this system including pros and cons, design criteria, soil-pile interaction as well as pile-raft interaction. So, this paper provides a comprehensive review of various design concepts for piled raft foundations, as proposed by different researchers. the load-sharing mechanism within these systems was explained as well. The study reveals that several factors influence the load-sharing ratio, and that the raft component plays a significant role in reducing total and differential settlement

This study aims to experimentally investigate the performance of steel fiber-reinforced concrete deep beams with various sizes of square web-opening applied to shear and shear-bending control loading scenarios. Twelve specimens were distributed to two groups according to loading control (shear & shear-bending). Each group consist of three beams prepared of fibrous concrete and others prepared of normal concrete. The three specimens consists of one that is solid as a reference and others containing various-sized square web openings (200 mm & 300 mm). The test results show that all the tested beams failed as shear failure except for the solid beams under shear-bending control, which failed as flexural failure. The presence of square openings with sharp corners and edges leads to high stress concentration, especially when these openings are located in the natural load transmission path. Steel fibers maintain the linear behavior in deep beams with openings until late loading stages, as evident in load-deflection curves. Fibrous beams with openings remained nearly identical. Unlike normal concrete beams, there was a large difference in the load-deflection curves since the early stages of loading. The tested beams failed after yielding of steel reinforcement, as the steel strain of fibrous specimens was lower than that of normal concrete. Steel fibers contributed to reducing stresses in reinforcing steel. Fibrous beams exhibit higher stiffness than normal beams, largely due to the presence of steel fibers. These fibers provide additional tensile strength, resisting shear cracks. Additionally, fibers increase the ductility index and absorption energy, redistributing stresses and provide warning before failure

We have experimentally demonstrated and simulated an enhanced design of a triple-band patch array. The proposed antenna design is enhanced by merging multiple split-ring resonators (SRR) on a single-layer dielectric substrate. The proposed design works on reducing the footprint size with improved return loss, gain, and bandwidth compared with related works. The simulated results show that the proposed antenna operates at 5.96 GHz, 7.96 GHz, and 14.94 GHz and it agrees with the experimental results. High gain values of 9 dB, 6.9 dB, and 7.6 dB were obtained from the designed antenna with split ring resonators at those frequencies, respectively. The return loss for each frequency was -45.77 dB, -33.21 dB, and -45 dB, respectively. The outcomes show an agreement between the simulated and measured results with the fabricated antenna. The suggested antenna outcomes and analysis show that the antenna can operate within the KU and C bands, therefore it has potential applications in Wi-Fi, microwave devices, radar, and satellite communications systems