Muthanna Journal of Engineering and Technology(MJET)

Urban livability is a very important aspect of sustainable urban development especially when it comes to historic cities thatare being affected by climatic changes and extreme growth of urban areas. The researchassessesthe livability of Al Jamiya Street, a major commercial-recreational street in Babylon, Iraq, using a mixed-method which incorporates morphological and pedestrian survey (n = 50). Its results demonstrate aninherent contradiction namely the street is a well-developed economic tourism spot, but its livability is grossly limited by the environmental pressures (excess heat with poor shading, surface temperatures above50 °C), infrastructure shortcomings (pedestrian-vehicle interactions, lack of crossings), and social restraints (lack of diversity among users). The study proves that the existing situations result in seasonally contingent livability, which puts commercial activities ahead of social equality and environmental comfort. Some of the recommendations can be immediate shading interventions, strategic greening, traffic calming, and public policy initiatives that can be implemented to ensure the city is more inclusive and resilient. This research adds to the literature on livability because it offers a contextual framework of evaluating and enhancing urban streets in historic Middle Eastern cities through the requisite balance of economic vitalityand holistic quality ofexistence in hot arid climates.

Modern power systems are more vulnerable to cascading failure due to the increasing complexity and renewable integration in modern power systems. Cooperative, adaptive protection schemes are considered fundamental to improving grid resilience, but as a matter of fact, the implementation of such schemes is inherently limited by the data privacy and ownership rules on multi-entity grids. The current centralized and multi-agent deep reinforcement learning (DRL) solutions require consolidation of sensitive operational information, which forms critical single points of failures and privacy violations. To address this gap, this paper will present a new Federated Deep Reinforcement Learning (FDRL) framework. The methodology develops relay coordination as a Partially Observable Markov Decision Process (POMDP) and uses a Federated Deep Deterministic Policy Gradient (F-DDPG) algorithm such that distributed relay agents learn local models with privately available data and just provides encrypted model parameters to a central aggregator to securely train the federating process. Simulation outcomes on the IEEE 39-bus system show that the proposed scheme decreases the cascade size by 52.5% and load shedding by 54.7% relative to traditional protection, and has fault discrimination accuracy equal to 95.8, and can operate at 13% of the speed of a privacy-violating centralized DRL benchmark. The framework manages to accomplish intelligent and collaborative protection without data confidentiality.

High-performing authenticated encryption schemes are essential for modern communication systems to provide both confidentiality and authentication. However, although many schemes have been evaluated for their performance and are in wide-spread use today, there are relatively few examples of comparative studies showing how authenticated encryption schemes compare against each other with respect to their security bounds or resistance to forgery. Furthermore, most performance comparisons have been made between abstract modes of operation that do not provide guaranteed security from either authenticated encryption or integrity protection; and thus there is no definitive security assessment for these modes of operation. In this paper, we provide a comprehensive analytical and experimental comparison between three algorithms and three modes of operation (GCM, CCM, and EAX), using multiple variables related to performance (encryption time, decryption timeand throughput), as well as formal performance metrics (authentication strength, probability bounds of forgery, and integrity through tag manipulation). The results of the studyillustratesthat there are significant differences in performance between GCM, CCM, andEAX regarding their throughput. The EAXvalue recorded using the AES algorithm was the best performer, while Blowfish had the second best performance, and the CAST algorithm had the lowest. For all comparisons, the authentication failure probability was always below the established cryptographic thresholdfor authentication success. Thisresearchprovidesa comparative performance-security outlinefor each algorithm-mode, and the findings can serve as a reference for choosingthe appropriate algorithm-mode for authenticated encryption systems.

Reinforced concrete (RC) columns are fundamental components in structural systems. Vertical loads are transferred from the top of the structure to the foundation. Due to exposure to various environmental and structural factors such as bearing capacity,seismicity, and corrosion of rebars, columns are susceptible to performance degradation or loss of load-bearing capacity. Therefore, the application of strengthening techniques has become crucial for restoring column efficiency and improving theirstructural behavior. Recent years have witnessed significant advancements in strengthening methods, including the utilize of advanced combined like fiber-reinforced polymer (FRP), in addition to ferrocement and other strengthening techniques. These modern methods offer an attractive alternative to traditional methods involving concrete and steel jackets. All these techniques increase the column's resistance to compressive, shear, and buckling forces, and also aim to improve the ductility and stiffness of the element as a whole. Environmental conditions, the nature of the damage, design requirements, feasibility of implementation, and cost are important factors in selecting the appropriate techniques. This review aims to study traditional and modern techniques used in RC columns, analyze the operating principle of each technique, and examine the factors affecting its effectiveness. Its performance and usage limits provide a knowledge base that contributes to improving design and engineering decisions in this vital field.The literature shows that modern reinforcement technologies, especially using FRP, fundamentally transform the seismic response of RC columns by promote lateral confinement, increasing energy dissipation and ductility, while transforming the failure pattern from brittle attitude to safer ductile attitude, but their efficiency remains limited by the quality of bonding with concrete and the effects of the environment, where the separation (Debonding) is the most prominent challenge in long-term performance.

Thermosyphon heat pipes are highly effective two-phase thermal transport devices widely used in electronics cooling, solar energy systems, and battery thermal management. This study presents a numerical investigationof the thermal performance of a thermosyphon heat pipe using ANSYS Fluentwith a VOF (Volume of Fluid) two-phase model. The simulation con-siders temperature distribution, phase interactions, and equivalent thermal resistance under varying fill ratios, tube diame-ters, and heat flux conditions.The results show that an optimum fill ratio of 70%minimizes thermal resistance, while devi-ations increase it by 18–25%. Tubes with smaller diameters have 12% lower thermal resistance to large diameter tubes be-cause of increased vapor velocity and enhanced heat transfer. A 20 percent increase in the height of the liquid-column in-creases the resistance to heat transfer, and a 50 percent increase in the rate of heat transfer decreases it by 15 percent. The fill ratio, tube size and heat flux affect start-up times to steady-state operation by 25-40 percent with the higher heat fluc-tuation accelerating stabilization by 35 percent. The results of this quantitative study size up the interdependence of geo-metric and operating parameters on thermosyphon performance. The paper offers a model of optimization of the design to determine the best choice of tube size, fill ratio, and heat injection to obtain the best heat transfer efficiency.

Indoor positioning has become essential for location-based services in smart buildings, yet Global Navigation Sat-ellite Systems cannot provide reliable positioning indoors due to signal attenuation and multipath interference. Existing ma-chine learning approaches for WiFi fingerprinting suffer from fragmented evaluation: studies compare limited algorithm subsets using inconsistent hyperparameter optimization and varied metrics, preventing reliable conclusions about algorithm selection. This paper presents a systematic comparison of six machine learning algorithms spanning four distinct paradigms—K-Nearest Neighbors (KNN), Weighted KNN (WKNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Deep Neural Network (DNN)—for WiFi Received Signal Strength Indicator fingerprint-based indoor positioning under consistent Bayesian optimization. All algorithms undergo identical Gaussian Process optimization with 50-iteration budgets, ensuring fair comparison under consistent conditions. Evaluation encompasses a 2,632 m² real-world environment with 517 reference points and 12 access points. Results demonstrate that Random Forest achieves optimal performance with 0.7170 m Root Mean Square Error (RMSE) and 94.73% classification accuracy, representing 21.7% improvement over baseline KNN (0.9152 m) and 42.2% improvement over DNN (1.2404 m). Statistical significance testing using Wilcoxon signed-rank test with Bonferroni correction confirms all pairwise differences (p < 0.001) except WKNN-KNN. These findings demonstrate that traditional machine learning can outperform deep learning for moderate-scale WiFi fingerprinting, providing evidence-based guidance for indoor positioning system deployment.