KUFA JOURNAL OF ENGINEERING

In this research, thirteen specimens of reinforced concrete continuous deep beams were cast and tested. One was the main reference without openings, three were a reference for each style depending on the arrangement of the openings, while the other nine were strengthen beams using NSM technology. Three models were used to implement the strengthening (horizontal, vertical and inclined). The results showed an increase in load capacity after applying strengthening to the specimens, as well as an improvement in stiffness and ductility. The specimens with inclined strengthening gave the highest increase in load capacity The increase rates ranged between (19%-30%), The specimens with horizontal strengthening gave an increase ranging between (7%-26%), The specimens with vertical strengthening also gave an increase ranging between (7%-22%).

In this paper, we present an optimization strategy that uses Genetic Algorithms (GA) to reduce Waiting Time, CO2 Emissions, and fuel consumption in a transportation network. The optimization process is performed through simulations using the well-known traffic simulation tool, SUMO (Simulation of Urban MObility). The main objective is to identify the most efficient traffic light timing plan. The proposed GA uses a binary encoding method for signal timing configurations and includes a fitness function to evaluate network performance regarding fuel consumption. The algorithm iteratively builds a set of signal-timing solutions over generations until it converges on the optimal solution. Empirical results show that the GA approach significantly reduces waiting time, reducing CO2 emissions, and reducing fuel consumption compared to standard signal timing plans. This research makes a significant contribution to the domain of traffic management and provides valuable insights for policymakers and transportation planners looking to reduce the environmental footprint of urban transportation networks.

Contextuality is one of the concepts that emerged in the post-modern era and became associated with it as a design strategy. Its purpose is to generate context as a relationship between elements drawn from memory and stored (heritage). Context has both physical and intellectual dimensions in architecture, serving as the language of intellectual and material dialogue between the building and its surroundings. Among its principles are harmony and suitability with the environment, whether through the general form, the materials used in construction and decoration, or through the underlying architectural concept and its alignment with the region's customs and traditions. The absence of context leads to an architectural identity crisis and consequently results in randomness. This is why the research problem revolves around the lack of conceptual clarity regarding the possibility of achieving context in local architecture through the use of nanomaterials to create contemporary architecture. The research aims to elucidate the knowledge about nanomaterials and their potential in achieving contextualism through two direct (as a primary material) and indirect (as a secondary material enabling traditional materials) approaches. Additionally, it examines the environmental context brought about by nanomaterials (harmony with the environment). This is based on the assumption that nanomaterials can be used to enable traditional materials to achieve architectural context for the city. The research employs an analytical and deductive methodology to elucidate knowledge about nanomaterials and their manifestations in architectural production. It derives their influential role in shaping the architectural output by interpreting a set of their properties and characteristics and analyzing a group of buildings where nanomaterials were used. The research concludes that nanomaterials have a direct impact on the final appearance in terms of color, texture, and, hence, the architect must thoroughly study the materials and select the most suitable ones for the region to achieve contextualism.

Roadway pavements constitute a vital component of transportation infrastructure, and their effective maintenance and rehabilitation are paramount to ensure optimal functionality. Efficient road management systems save time and resources and rely on accurate pavement condition surveys. To ensure optimal functionality, it is crucial to establish a pavement management system (PMS) that involves assessing the current state of the road network and predicting future conditions. In this research, we assessed the effectiveness of asphalt pavements using two well-known approaches: The Present Serviceability Index and The Pavement Condition Index (PCI). The PCI, a numerical index, is a reliable indicator to assess the operational condition and structural reliability of pavements based on visual assessments of distress type, severity, and quantity. We aimed to classify the various types of distress observed on Road No.80 and estimate the pavement condition index. We identified nine out of nineteen distress problems in the pavement section under study through a comprehensive pavement distress condition rating approach. The most prevalent distress types included longitudinal and transverse cracking, rutting, potholes, edge cracking, alligator cracking ,patching and weathering and raveling.

The only way for the Blind and visually impaired persons to read the holy Quran is by using a special paper edition of embossed Braille code. A new approach to reading the Holy Quran text for those persons was proposed by the author. One of the main demands for this approach is the classification of the dots and diacritics, which can be abbreviated as DaDs. This paper outlines the creation of a dataset of images for the DaDs of Al-Mushaf. A handheld scanner was developed for this purpose, and MATLAB programs were employed for DaDs segmentation. The final goal is the design of a tactile vision sensory substitution system based on an optical character recognition technique to help blind individuals read the Holy Quran in an alternative way to Braille codes. Approximately 1750 images were taken from two distinct Al-Mushaf versions with the proposed handheld scanner. Using the suggested techniques and algorithms, 6000 DaDs were retrieved from these images; however, only 4710 images of the DaDs, arranged in 22 classes, were selected after the repeated, incomplete, and non-DaDs were eliminated. The dataset was organized by DaD class, prepared to be used directly for machine learning purposes, and made available for public use upon request.

Smartphone devices have become the focus of attention of scholars dealing with GNSS measurements since May 2016 when Google stated using Android operating system version N that can access GNSS raw data. This opened the research door wide to process this data and analyse different issues in positioning by smartphones. Our study contributes to the assessment of GNSS data accuracy by evaluating various Android Application Programs (APPS) using Samsung Galaxy S9PLUS smartphone. While previous research has explored this topic, our study distinguishes itself through a comprehensive comparison of three APPS and two positioning techniques. By highlighting performance variations and considering multiple constellations, we provide valuable insights for smartphone GNSS users. Three different Apps were utilised: G-RitZ Logger, GnssLogger, and rinex ON. In addition, two GNSS positioning techniques were used for assessment: Post Processing Static (PPS) and Precise Point Positioning (PPP). The results revealed that GnssLogger obtain consistent readings in different scenarios. Therefore, GnssLogger is considered as the best logging GNSS data by smartphones. Moreover, accuracy is also discussed based on different constellations: GPS, GLONASS, and GALILEO. As expected, GPS results are the best compared to other constellations owing to availability and tracking priority for smartphone software. The best results obtained from GnssLogger with PPS technique and GPS system which has given RMS as 0.034 m, 0.171 m, and 0.383 m for E-W, N-S, and U-D axes respectively.

4D printing technology represents a major advance in additive manufacturing, in which materials can dynamically change shape or properties over time in response to external stimuli. Recent years have seen phenomenal advances in the field of 4D printing technology, which has revolutionized production and design. This review provides a concise overview of the research progress in 4D printing technology, focusing on its principles, applications, and challenges. Researchers have looked into a variety of materials, such as composites and smart polymers that may change programmably in response to light, humidity, or temperature. Applications for 4D printing are found in a wide range of industries, from aerospace parts to biomedical equipment. The creation of self-assembling and adaptable structures has enormous potential for the development of materials that are useful and responsive. Significant developments in the creation of cutting-edge 4D printing methods, like multi-material printing and exact control over shape-changing processes, are highlighted in this review. Challenges, material constraints, and scalability problems are also addressed, with a focus on continuous efforts to overcome them. The revolutionary effect of 4D printing on manufacturing, design, and many other industries is becoming more and more evident as it develops, opening up previously unimaginable opportunities and creative solutions. Finally, challenges and future directions in the field are discussed, aiming to inspire further research and innovation in the dynamic field of 4D printing technology.

Although DC to DC convertors might be considered as the most widely used circuits in power electronic, where a specific DC output voltage must be stabilized to a specific desired level, yet theses circuits unfortunately exhibit a nonlinear behavior. The nonlinearity in these circuits is primarily caused by the power switch, varying input voltages and loads, resulting in a converter instability, large overshoot, oscillations, and extended settling times. To mitigate these problems, a learning sliding mode control (LSMC) was introduced and compared with the proportional integral derivative sliding mode control (PIDSMC) fuzzy logic controller (FLC) as well. The study demonstrated that the LSMC provides a smooth output voltage, without chattering, compared with PIDSMC and FLC. Moreover, formulating LSMC might be considered a novel controlling method for DC to DC convertors as the LSMC represents an advancement in the control strategies for stabilizing output voltages in power electronic circuits. By comparing LSMC with classical PIDSMC and FLC, the paper provides a novel analysis of the effectiveness of different control approaches in addressing nonlinearity and instability in DC to DC converters.

Object detection and image classification are among the most important areas to which scientific research is directed, which are commonly used in various applications based on computer vision. The development of low-cost embedded devices with powerful processing is leading to a trend in their use in computer vision, which provides reduced access time, reliability, and data security. That's why Tiny Machine Learning (TinyML) technology appeared, which is field that specifically explores the application of machine learning on highly limited edge devices. Deep learning techniques are increasingly employed in data-intensive and time-sensitive IoT applications. Deploying Deep Neural Network (DNN) models on microcontrollers (MCUs) is challenging due to limited resources such as RAM. Recent advancements in the field of TinyML hold the potential to introduce a novel category of peripheral applications. TinyML enables the development of new applications and services by eliminating the reliance on cloud computing, which consumes power and poses risks to data security and privacy. TinyML currently regarded as a promising artificial intelligence (AI( alternative that specifically targets technologies and applications for devices with very low profiles. In this paper, the most important algorithms used in object detection will be presented, in addition to the challenges in using low-resource embedded systems that support TinyML technology.

Epoxy resins are widely used for their strength, chemical resistance, and adhesion, but they are naturally brittle. The objective of this study was to investigate the effects of the addition of a polyurethane (PU) base (polyol) and liquid silicone rubber (LSR (as toughening agents to the epoxy resin. This research focuses on developing a modified flexible epoxy resin by added the toughening agents to epoxy resin was evaluated in various weight ratios (3%, 6%, 9%, and 12%) with (1-methylethyl-1,1'-biphenyl, izoforon diamine) as a hardener. To evaluate the impact of strengthening materials, epoxy samples were tested before and after the addition the toughening agents above. Surprisingly, the mechanical properties exhibited significant improvement according to the results. In case of LSR/Epoxy, (tensile modulus, tensile strength, fracture toughness, and impact strength) were modified at 6wt% LSR. In case of PU/Epoxy, a mechanical property was modified depending on the addition percentage of PU. In conclusion, the 6wt% LSR is the best ratio to modified epoxy. In comparing to PU, each percentage modify a specific property, this difference take place because of the soft elastic flexible nature of PU.

This paper proposes a hybrid T-DEA-P (Taguchi-Data Envelopment Analysis-Pareto) method to optimise and rank parameters of the electrohydraulic forming process using the aluminium (AA1100) sheets for automotive panel application. A linear programming model was formulated based on the integrated Taguchi – DEA data and solved using DEA software. The design variables are the stand-off distance, electrode gap voltage and medium. The constraints are formulated according to the levels specified in the level-factor table. Using literature data, the validation of the method was made. An important result shows that case 1 has the optimal parametric setting is SOD3E2V2M2. This is interpreted as a stand-off distance of 3mm, an electrode gap of 30mm a voltage of 260 volts and the medium as Oil with a viscosity of 0.89cP. The delta values are 16.026, 16.0605, 17.1109 and 37.3587. Delta values measure the rate of change of the average signal-to-noise ratios relative to the changes in levels. Based on the Pareto intervention, the medium is ranked first while voltage, electrode gap and stand-off distance are ranked second, third and fourth, respectively. The method aids in the planning activity for the forming process.

This review discusses the Arabic gum and gelatin-based films used in the food packaging industry. Gelatin is an essential biopolymer derivative of collagen. Various industries widely use it due to its technological and functional characteristics. Several constituents can be used as additives to recover their characteristics and requests. Antioxidants, antimicrobials, and other agents are used to increase customer attention and extend the food products shelf life. Food packaging is planned to protect food products, offer essential information related to the food, and make food handling suitable for consumer delivery. The Arabic gum and gelatin blend is suitable for keeping fruit and vegetables fresh for a long time due to the blended film\'s ability to cover the food and protect it from microbial and environmental effects. The film\'s composition of natural components that are safe to eat forms a protective layer to prevent gas, oxygen, and humidity.