The current study was an attempt to examine the new application of the Adaptive Exploration Artificial Bee Colony algorithm in evolutionary clustering. An optimized version of the traditional Artificial Bee Colony (ABC) algorithm, AEABC relies heavily on a probability parameter that is based on distance. In this regard, a balanced relationship of exploration and exploitation leads to the dynamic parameter adaption, resulting in the spatial distribution of solutions. As a result, AEABC provides an efficient performance for solving dynamic clustering tasks, involving consistent data changes and constant reformation of cluster structures. To conduct the present study, the performance of AEABC on evolutionary clustering was compared with that of other metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results confirmed that AEABC produced lower performance error rates than competing algorithms in all three scenarios representing best case, worst case and average case performance. This established both its reliability as well as robust efficiency. AEABC indicated convergence performance because the algorithm initiates with quick development in the first stages then reached stable points effectively, which shows its ability to position itself well in changing data environments. Moreover, the findings could confirm AEABC as an effective means for evolutionary clustering, which makes it a suitable candidate for applications involving dynamic systems, such as social network analysis, customer segmentation, and anomaly detection. By applying AEABC to evolutionary clustering, this paper contributes to expanding the range of applications for swarm intelligence algorithms in handling real-world, dynamic data challenges

In this work, wire mesh metallic fibers and the nanomaterial titanium oxide TiO₂ were used to reinforce the epoxy matrix to create the hybrid composite material specimens by the hand lay-up method. The effects of adding specific fractions of TiO₂ nano material on the dynamic characteristics of wire mesh hybrid composites were investigated. The vibrational characteristics (natural frequency and damping ratio) and mechanical characteristics (tensile and bending stresses) were examined. The numerical analysis for Bending and vibration cases was performed using ANSYS ABDL 2017 R2. Experimental results showed that the addition of TiO₂ nanoparticles significantly improved tensile strength and flexural strength in epoxy-steel wire-TiO₂ composites. However, increasing the nanoparticle ratio to the maximum values led to reduced performance. The composites showed a balance between stiffness and strength when optimized at 5 wt% TiO₂ and moderate layer counts. Natural frequencies and damping characteristics were affected by fiber volume, layering, and nanoparticle content

Cell Formation CF is regarded as critical aspect of a Cellular Manufacturing System CMS. It focuses on the creation of part families and machine cells. There are numerous methods, algorithms and models suggested to deal with solving the CF problem. However, there are several drawbacks to some existing CF methods such as, their inability deal with large size datasets, ill structured datasets, the simultaneous creation of clusters of parts and machines, lack of ability to find optimum solutions, and increased material handling costs. Thus, the suggested polar coordinates effectively address the CF problem by clustering parts and machines to reduce the material handling costs. It offers efficient initial clustering, and adapts to different production environments. While not globally optimal, it provides good starting solutions for further optimization, excelling in cost reduction and efficiency. A polar coordinates-based approach combines the repetition of parts and machines with angles and then the resulting pattern is used for checking similarity criteria. The purpose is to improve the Grouping Efficacy GC, decrease the travel time of parts inside and outside cells and enhance Machine Utilization MU. GC is applied as a performance measure to identify the quality of the suggested CF approach. After the polar-based approach is applied, it enhanced the proportion of exceptional elements and machine utilization. On the other hand, it showed better results for GC when compared with Rank Order Clustering (ROC). For some datasets, it recorded (60, 90) compared with (53.33, 71.93) for the traditional ROC

Water towers play a Key role in supporting urban infrastructure, particularly in seismic regions where their collapse can endanger the safety of the public. In this work, A seismic analysis of elevated water tank is carried out (full, half, and empty) using Newmark-Beta time integration procedure with Summation square root of sum of squares (SRSS) method for multi directional analysis. The analysis uses the Newmark-Beta time integration scheme to interpret the dynamic response of the tank under bi-directional seismic loading. The natural period of the tank goes from 0.412 s (empty) 0.856 s (full), whereas the maximum Lateral displacement is 0.28 mm and Base shear forces of 215.4KN under full condition. Structural calculations verify that drift ratios are below 0.5% (IBC,2021) with uniform stress distribution in the columns. The results highlight the need for factoring of fluid-structure interaction and multi-directional analysis in seismic design codes

Nowadays, Internet of Things (IoT) and Sensor Networks (SNs) consider the transformative technologies that enable ubiquitous connectivity and data collection in various fields. In distributed sensor networks, the nodes are responsible on environments monitoring for sensing and collecting various conditions. The forwarding process for these data gains a significant attention in the researches because it considers the first step for the operation of any algorithm or techniques in routing process in any type of wireless networks. In general, during any routing process, the node with data needs to select its next hop efficiently and thus will affect the optimum route from source to destination. The most common techniques that utilized in this step are the flooding and gossiping. These two techniques suffer from different problems such as implosion, overlaps and blindness of any resources. Therefore, this paper proposed an efficient technique for best next hop selection by taking in consideration the main resources that effect the sensor’s power level and thus will reduce the power consumption and then will improve network lifetime. In the proposed technique, the node that has maximum power level, nearest to the current node and it does not receive or send the required packet will be selected as the next hop node. These assumptions help mitigate flooding and gossiping issues. The proposed technique has been implemented using MATLAB simulator. The results show that the proposed system has reduce the node’s power exhausting in single cycle and thus has improve network’s performance

In this research, thirteen T-shaped hybrid concrete beams were designed to investigate their flexural behavior. Flanges of all beams were cast by 106 N/mm2 fcu of self-compacting high-strength concrete (SC-HSC), while the webs were made by 45 N/mm2 fcu of normal-strength concrete (NSC). One beam was loaded up to failure as a control beam (CB), and the other beams were divided into two groups; the first group consisted of six beams preloaded by 45% of CB's ultimate load (Pu), and the other group of beams subjected to 85% of CB's Pu. Beams were retrofitted along the entire clear span using various numbers and placements of glass fiber-reinforced polymer (GFRP) bars mounted near the bottoms and side(s) surfaces. However, tests resulted in a notable enhancement ranging from 35% to 101% of retrofitted beams. At the same time, the maximum deflections of both groups witnessed a range from 21.9 mm to 34.9 mm beside CB's ultimate deflection (33.8 mm); in addition, ductility ratio, toughness, and stiffness values varied between (55.89% and 73.8% reduction), (15.95% decrement and 31.96% increment), and (2.36% and 47.83% reduction), respectively. Furthermore, the failure mode was mainly GFRP-Epoxy debonding

This study introduces an innovative AI-driven robotic system designed to enhance search and rescue operations in disaster-stricken areas, including earthquakes, floods, and conflict zones. The system integrates a multi-modal sensory framework combining RGB and thermal imaging, processed through a robust pipeline: raw sensor data undergoes validity checks and noise filtering to ensure input integrity, followed by human detection via the You Only Look Once (YOLOv8) algorithm. Concurrently, ultrasonic sensors relay environmental data to a Field-Programmable Gate Array (FPGA) module, enabling real-time obstacle avoidance and dynamic path planning for rapid, precise navigation. Experimental results demonstrate a 96% accuracy rate in survivor detection, outperforming comparable AI-FPGA hybrid systems—particularly in identifying partially obscured humans. The system also exhibits superior response times and operational safety, as quantified in the results section. Future work will focus on expanding sensory modalities, adaptive learning mechanisms, and multi-robot coordination to further enhance mission efficacy

This review aims to carefully analyze how Carbon Fiber Reinforced Polymer (CFRP) bars and sheets are used in reinforced concrete, focusing on their strength, durability and ability to support a structure. The analysis compiles recent research to look into the positives and negatives of using CFRP rather than steel as a reinforcement for structures. The main parts of this field are bonding ways, extra costs, effects on the environment and how CFRP performs in various situations. The summary points out that CFRP can considerably increase flexural and shear strength in concrete, resist corrosion and create objects that are both durable and light in weight. Despite the expensive cost, limited information about durability and some bonding-related issues, CFRP systems exhibit encouraging effects in applications like flexural strengthening, shear reinforcement and confining concrete columns. Research and new technologies will likely allow CFRP to be more widely used in the future, solving many infrastructure problems

The development of Mg and Fe based biomaterials in the past decade has been extensively studied as biodegradable material for medical applications. The development of this material is limited in terms of its suitability for clinical applications. Zn-based alloys began to be an alternative to be studied as a substitute for Mg and Fe based biomaterials. Zn-based alloys have a moderate degradation rate but have low mechanical properties, so other elements need to be added to improve their mechanical properties. In this study, the added elements are silver and Zirconium. Compared to pure Zinc the mechanical strength was enhanced significantly for all tested Zinc alloys. Accordingly, we design four alloys (pure Zinc, Zinc+0.4 Silver, Zinc +0.4 Zirconium and Zinc + 0.4 Silver+ 0.4 Zirconium) wt.%. The alloys are developed by stir casting process and homogenized at 280 C° for 1 hour. The Vickers hardness and compression tests of alloys were investigated. The hardness value of zinc based alloy is (64.28 kg/mm2) increased to (100.53kg/mm2) for (Zn­Ag­Zr) alloy, and the improvement percentage of hardness to (Zn­Ag­Zr) alloy arrived to maximum (56.3%). In addition, the compression value of zinc based alloy is (165.95 MPa) increased to (307.76 MPa) for (Zn­Ag­Zr) alloy, and the improvement percentage of compression test to (Zn­Ag­Zr) alloy is (85.45%). So (Zn­Ag­Zr) alloy is considered the best alloy to enhance compressive strength where achieve higher improvement percentage