Journal of Engineering and Sustainable Development (JEASD)

This paper presents an experimental investigation of the flexural behavior of reinforced concrete one-way slabs with longitudinal hollows. Hollow ratios (weight reductions) used in this work are 11.43% and 22.86%. Two longitudinal reinforcement ratios (ρ = 0.58 % and 1.03 %) and four steel fibers volumetric ratios (Vf= 0, 0.2, 0.4, and 0.8%), were used. Results show that slabs with longitudinal hollows having weight reduction up to 22.86%, show reductions in strength (ultimate load) up to 32% and toughness (energy absorption) up to 45% and higher deflections compared to corresponding solid slabs. However, these reductions lowered to 27.5% and 24.5%, respectively using 0.8% steel fibers or 6.3% and 25.5%, respectively by increasing longitudinal reinforcement from 0.58% to 1.03%. Furthermore, increasing longitudinal reinforcement from 0.58% to 1.03% along with using 0.4% steel fibers in a hollow slab givesa strength gain of 17.5% with a reduction in toughness of only 9.8% compared to reference solid slab with 0.58% longitudinal reinforcement and 0% steel fibers. Results also showed that hollow slabs offer stiffer load-deflection behavior (lower deflections) and less maximum crack width as longitudinal reinforcement and/or steel fibers

The primary goal of photovoltaic panels is to generate electricity. The efficiency of solar systems, in particular photovoltaic panels, is generally low. The performance of PV panels depends on many parameters, such as temperature. Studies have shown that panel efficiency decreases at a rate of (0.45 - 0.65) per one degree Celsius, so the decrease in temperatures of the photovoltaic cell leads to Higher performance, and therefore requires keeping the photocell as cool as possible. This can be achieved through the cooling system, which not only enhances the electrical efficiency of the PV module but also reduces its rate of deterioration over time, thus extending its life. In addition, the excess heat removed by the cooling system can be used in many applications. There are many cooling technologies available, including the use of air or water as the working fluid with motion generated by the circulation of the water, forced water heat exchanger, water mist, or the use of fans to circulate air. The cooling process using water is more effective than air, as the average temperature decreases from (9-17) degrees Celsius. This paper presented a review of relevant literature on active cooling for thermal regulation and electrical efficiency enhancement of PV modules. Research conducted in this field has shown that these techniques have been successfully applied to improve the efficiency of photovoltaic cells under various conditions.

The Republic of Iraq, similar to other Gulf countries is affected by the swings in oil costs, which motivates the policy makers to start thinking about integrating renewable energy such as PV power plants into the grid. This study is conducted to provide a solution for the accumulation of dust and dirt on the PV panels which is a problem of national and international significance. The accumulated dust on the panels of solar cells reduces the applied radiation of sunlight and reduces the efficiency of the solar cells. In this paper, a remote cleaning control system is designed and implemented practically to maintain the panels' cleanliness. The control system is implemented on the real solar panel at different times throughout the day and is characterized by a lightweight and tight structure. The results show that there is a noticeable efficiency improvement for the solar panel output when it is cleaned in comparison to when it is heavily and medium dusted. After cleaning the panel, the average dissipated power with different tilt angles is increased by almost 1.75W when power measurement is collected at 9 am and 1.85W when measurement is collected at noon.

Road safety assessment is an essential step in the management of transportation projects. The proactive approach in the safety assessment considers the pre-construction stage to reduce the likelihood of road accidents. This research aims to assess the risk of road infrastructure in Iraq by applying the most recent methodology based on road features and supported traffic data. The selected methodology significantly contributes to enhancing a proactive approach to road safety and incorporating risk assessment in the planning and design stages of road projects. A segment of 15km from the beginning of the Latifiya expressway was selected for this study and assessed using the international road assessment program methodology. Data on road features and traffic were collected for this goal; where data on 38 aspects of road features is collected. The traffic data includes traffic volume and spot speed. Then these data were coded and processed using VIDA online web. The assessment results were presented for three road user groups: vehicle riders, pedestrians, and motorcyclists. The results showed that the assessed segment has a high-risk level; 1 to 3 stars for vehicle riders and 1 star for motorcyclists and pedestrians. Countermeasures were presented, and their efficiency was tested to raise the star rating for all road user groups to five stars.

One of the most prevalent behavioral biometrics is the signature. In this paper, signatures are utilized in the case of recognition. Multiple contributions are provided here. Firstly, statistical analysis of efficiency is taken into consideration for the feature extraction. Secondly, a novel classifier is suggested. It is employed to recognize the signatures and it is called the Normalized Generalization Neural Network (NGNN). In terms of error rates, comparisons are established between different neural networks in the literature and the novel NGNN. The proposed NGNN consists of the input layer, normalization layer, Radial Basis Function (RBF) layer, and output layer. It can be considered as an enhanced or developed version of the Generalized Regression Neural Network (GRNN). A large number of signatures' attributes from the Biometric Ideal Test (BIT) database is utilized. That is, 1750 patterns of attributes are exploited. A significant improvement in the error rates over previous networks is achieved when using the novel NGNN. The Mean Absolute Error (MAE) has reached 0.028 and the Mean Square Error (MSE) has obtained 0.014. In addition, further experimental results on the BIT database showed better Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of 0.002 and 0.119, respectively.

This research delves into the utilization of microwave hybrid heating as a curing technique for producing fine aggregates from fly ash. Various concentrations of alkali activator solutions were employed as binders to pelletize the fly ash (ranging from 0% to 10%). The resultant green fly ash-based geopolymer (FAG) aggregates were subjected to a 15-minute microwave kiln treatment. The microwave-sintered FAG fine aggregates, varying in sizes from 0.60 to 2.36 mm, were then utilized to fabricate 50 mm cubic cement mortar samples. Findings indicated that the density of FAG aggregates was approximately 36% lower than that of natural sand, while the water absorption capacity of FAG aggregates exhibited a fivefold increase compared to natural sand. Notably, cement mortar samples made with FAG aggregate of 4% alkali activator exhibited maximum compressive strengths of 31, 37, and 42 MPa at 7, 14, and 28 days of curing, respectively. These compressive strengths were only 12% lower than those of cement mortars made with natural sand across all curing periods. The use of microwave hybrid heating to transform fly ash into aggregates with sufficient strength appears viable, suggesting that these manufactured FAG aggregates could serve as substitutes for natural aggregates in concrete production

Weirs are hydraulic constructions typically employed in dam spillways and channel control structures. The shape and size of the crest will determine whether the weir is linear or non-linear. Of the two types, non-linear weirs are often selected due to their ability to allow different discharge levels at the minimal heads. A recently introduced variation of the non-linear kind is the piano key weir. This research undertakes a comprehensive analysis of the hydraulic functionality of the type-C piano key weir under conditions of free flow. The research methodology involved modifying the models by adding stages to the sides and monitoring the effect on the efficacy of discharge, represented by the discharge coefficient. The study further extrapolated formulae linking critical variables to the discharge coefficient, using dimensional analysis and SPSS software. The findings of this study revealed that the introduction of stages significantly bolstered the discharge coefficient in the altered models compared to the standard model. This enhancement was reflected in a 6% and 11% ascent with length expansion of the stages and a 6% and 8% rise with height increase of the stages. Additionally, the model's performance was augmented with a drop in the total head ratio

Glass fiber/polymer composites are widely utilized in many structural applications because of their high specific strength and stiffness-to-weight ratios. In this study, woven glass fiber/epoxy was hybridized with short glass fibers of different lengths (2, 4, 6, 2+4, 2+6, and 4+6 mm) and contents (3, 6, 9, and 12 wt%) to produce hybrid woven SGF/epoxy composites. Variations in the thickness, fiber volume fraction, density, and void content of the prepared composites were determined. Mechanical tests such as tensile and Charpy impact tests were conducted. Compared to the woven fabric composites without short glass fibers (control samples), the tensile strength increased by approximately 13% at an optimum weight fraction of 3 wt% using short glass fiber lengths of 4 mm. Meanwhile, the incorporation of short glass fibers into the woven glass composites decreased the tensile modulus for all lengths. The maximum enhancement in the impact strength (approximately 14%) was achieved by the hybrid composite samples reinforced with 4 mm of short glass fibers at 3 wt%. This hybrid composite offers 18% and 20% improvements in the specific tensile and impact strengths, respectively. In summary, filling the woven fabric composites with specified short fiber contents and lengths can increase their mechanical performance when resin-rich regions are reinforced with short fibers without forming relatively thick interleaves between the woven fabric plies

Underwater Acoustic signal denoising is encountering high demand due to the extensive use of acoustic in a lot of underwater applications. Underwater acoustic noise (UWAN) has a high effect on the quality of the acoustic signal therefore, it is always preferred to use a de-noising filter to remove it. In this paper, we propose a filter that utilizes a Complex wavelet transform (CWT) to remove UWAN and help improve the signal-to-noise ratio (SNR) of the detected acoustic signal. CWT is nearly shift-invariant and offers a good directionality in contrast to normal wavelet transform (DWT). The proposed method was tested using a real recorded UWAN for three depths from the Tigris River. The proposed method was compared with a more conveniently used discrete wavelet transform. The test included using Two signals: fixed frequency and linear modulation signal. De-noising was performed using a soft thresholding technique based on level-dependent threshold estimation. The proposed method showed supreme performance in terms of SNR and root mean square error (RMSE). When the input signal was 5.9 dB and -13.2 dB for SNR and RMSE respectively, the results were 10.9 dB for SNR and -15.7 dB for RMSE in the case of fixed frequency.

This paper suggests a new image encryption algorithm based on block compressive sensing (BCS) and chaotic scrambling techniques. With compressive sensing (CS), signals can be sampled much lower than the Nyquist-Shannon rate while preserving their information content. BCS can be used as a one-step process by using a 3D logistic chaotic map, the measurement matrix is used as a secret key for encryption. BCS algorithm used an individual image reconstruction algorithm that leverages l_1norm minimization to promote signal sparsity, and a smoothing operator to enhance image quality. An encrypted image is first decomposed into three sub-images based on the tricolor theory; then, a discrete wavelet transform is used to sparsely process the three decomposed images. A 2D Henon map is used to scramble the compressed image after compression. This process further enhances the security of the system by increasing the complexity of the encryption process. The results showed that the proposed system provides better security and has a lower computational complexity than other methods. Furthermore, the proposed system is resistant to known attacks such as brute force and statistical attacks.

Over the previous three decades, researchers have dedicated significant efforts towards the advancing of glass fiber-reinforced polymer (GFRP) bars, aiming to address the challenges arising from corrosion in traditional steel reinforcement bars. This study intended to find the most efficient relation to compute compression load capacity for concrete columns of circular sections reinforced with GFRP through surveying equations from previous research and comparing the relations of the most popular GFRP codes (JSCE, AS, CSA, and ACI). The statistical analysis of the equations depends on a comparison of practical and theoretical load capacities, Young's modulus, compressive concrete strength, ratios of longitudinal and both types of transverse reinforcement, spirals, and hoops. The results of CSA and AS were close to each other, and they were better than those of JSCE and ACI regarding being efficient, safe, and consistent. In addition, JSCE demonstrated a high level of conservative in all the studied parameters, followed by the ACI, while the CSA and AS showed very low conservative rates. Furthermore, the AS showed superiority in all the studied parameters. In terms of statistical metrics, the AS exhibited a very low error rate. It was concluded that the AS relation is the most efficient and superior relation for computing the axial load carrying capacity (ALCC) of all the studied parameters, followed closely by the CSA code equation, and with a slightly larger margin, the ACI code equation then JSCE code equation with a slightly very large margin.

Human opinions and feelings can be studied and analyzed in various fields. Sentiment analysis divides data into neutral, positive, and negative categories to classify a writer's or speaker's attitude toward various topics or events. This study uses a hybrid approach that combines (Particle_Swarm_Optimization PSO) with machine learning classifiers (Artificial Neural_ Networks ANN, Naïve Base NB, and Support.Vector.Machine SVM). Following the preprocessing phase of each data collection, a Convolution Neural Network (CNN) will be used to create feature vectors, preparing the raw data as a stored labeled dataset with three classes (positive, negative, and natural). After that, with two hidden layers whose parameters were optimized by PSO, this dataset will be prepared for classification using NB, SVM, and ANN. The best results will be obtained with the proposed Artificial Neural Network A.N.N which is close to 99% Acc. value, about SVM Acc. equal to 92%, and NB 89%, better than using the same ML algorithms

The current study focuses on the effects of different weather conditions, including temperature and humidity, on the impact energy properties of composite materials tested using a Charpy impact tester connected to a special hygro-thermal device. This is a unique device, handcrafted and equipped with sensors and control panels The composite materials, used in this study, combine polyester and reinforced with fiberglass in various shapes and loadings. Nanopowder materials including titanium oxide nanoparticles (TiO2) and aluminum oxide nanoparticles (Al2O3) were also added to the composite materials and their effects were assessed. The obtained results showed that all the different factors studied in this research have a positive effect on the impact test, except the nanomaterials in both cases (Al2O3 and TiO2 nanopowders) at 2 wt.% of the sample and at approximately 10 wt.%. Hence, at this percentage, there is not enough effect on the impact energy

The current study focuses on the effects of different weather conditions, including temperature and humidity, on the impact energy properties of composite materials tested using a Charpy impact tester connected to a special hygro-thermal device. This is a unique device, handcrafted and equipped with sensors and control panels The composite materials, used in this study, combine polyester and reinforced with fiberglass in various shapes and loadings. Nanopowder materials including titanium oxide nanoparticles (TiO2) and aluminum oxide nanoparticles (Al2O3) were also added to the composite materials and their effects were assessed. The obtained results showed that all the different factors studied in this research have a positive effect on the impact test, except the nanomaterials in both cases (Al2O3 and TiO2 nanopowders) at 2 wt.% of the sample and at approximately 10 wt.%. Hence, at this percentage, there is not enough effect on the impact energy