Background: Polymethylmethacrylate (PMMA) is the most ‎commonly used mâ€aterial in denture construction. This material is ‎far from ideal in fulfilling the‎ mechanical requirements, like low impact and transverse strength and poor thermal conductivity are present in this material. The purpose of this study was to study the effect of addition a composite which include 1%wt silanized silicone dioxide nano fillers (SiO2) and 1wt% oxygen plasma treated polypropylene fiber (PP) on some properties of heat cured acrylic resin denture base material (PMMA). Materials and methods: One hundâ€red (100) prepared specimens were divided into five groups according to the tests, each group consisted of 20 specimens and these were subdivided into two groups (unreinforced heat cured acrylic resin as control group)and reinforced acrylic resin with ( 1%wt Nano SiO2 and 1% wt oxygen plasma treated polypropylene ‎fibers) ‎group. The transverse strength¸ impact ‎strength, indentation hardness (shoreD), surface roughness and water sorption and solubility were investigated. The results were statistically analyzed using descriptive and t-test. Results: The results of this study show that a highly significant increase in impact strength (10.4939 Kj/m2),surface hardness (89.9375) surface roughness (0.9498) and water sorption (0.0171mg/cm2) was observed with the addition of 1%wt silanized (SiO2) nanoparticles and 1%wt oxygen plasma treated polypropylene fibers to (PMMA) , also significant decrease in transverse strength (103.4753 N/mm2), nonsignificant decrease occurred in water solubility which was (0.0005mg/cm2). Conclusion: The incorporation of 1%wt silanized SiO2 nanoparticles and 1%wt oxygen plasma treated polypropylene fiber to heat cure PMMA form a composite improves the impact strength, surface hardness and surface roughness of acrylic resin, at the same time this addition increase the water sorption and decrease water solubility; while significant decrease in transverse strength.
Construction contractors usually undertake multiple construction projects simultaneously. Such a situation involves sharing different types of resources, including monetary, equipment, and manpower, which may become a major challenge in many cases. In this study, the financial aspects of working on multiple projects at a time are addressed and investigated. The study considers dealing with financial shortages by proposing a multi-project scheduling optimization model for profit maximization, while minimizing the total project duration. Optimization genetic algorithm and finance-based scheduling are used to produce feasible schedules that balance the finance of activities at any time w
Nowadays, the use of natural bio-products in pharmaceuticals is gaining popularity as safe alternatives to chemicals and synthetic drugs. Algal products are offering a pure, healthy and sustainable choice for pharmaceutical applications. Algae are photosynthetic microorganisms that can survive in different environmental conditions. Algae have many outstanding properties that make them excellent candidate for use in therapeutics. Algae grow in fresh and marine waters and produce in their cells a wide range of biologically active chemical compounds. These bioactive compounds are offering a great source of highly economic bio-products. The prese
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Optimizing system performance in dynamic and heterogeneous environments and the efficient management of computational tasks are crucial. This paper therefore looks at task scheduling and resource allocation algorithms in some depth. The work evaluates five algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FA) and Simulated Annealing (SA) across various workloads achieved by varying the task-to-node ratio. The paper identifies Finish Time and Deadline as two key performance metrics for gauging the efficacy of an algorithm, and a comprehensive investigation of the behaviors of these algorithms across different workloads was carried out. Results from the experiment
... Show MoreThe rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
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