Multi-Mode Resource Constrained Project Scheduling Using Differential Evolution Algorithm

Project scheduling is a tool that manages the work and resources associated with delivering a project on time. Project scheduling is important to organize, keep track of the finished and in-progress tasks and manage the quality of work delivered. However, many problems arise during project scheduling. Minimizing project duration is the primary objective. Project cost is also a critical matter, but there will always be a trade off between project time and cost (Ghoddousiet et al., 2013), so scheduling activities can be challenging due to precedence activities, resources, and execution modes. Schedule reduction is heavily dependent on the availability of resources (Zhuo et al., 2013). There have been several methods used to solve the project scheduling problem. This dissertation will focus on finding the optimal solution with minimum makespan at lowest possible cost. Schedules should help manage the project and not give a general estimate of the project duration. It is important to have realistic time estimates and resources to give accurate schedules. Generally, project scheduling problems are challenging from a computational point of view (Brucker et al., 1999). This dissertation applies the differential evolution algorithm (DEA) to multi mode, multi resource constrained project scheduling problems. DEA was applied to a common 14- task network through different scenarios, which includes Multi Mode Single Non Renewable Resource Constrained Project Scheduling Problem (MMSNR) and Multi Mode Multiple Non Renewable Resource Constrained Project Scheduling Problem (MMMNR). DEA was also applied when each scenario was faced with a weekly constraint and when cost and time contingencies such as budget drops or change in expected project completion times interfere with the initial project scheduling plan. A benchmark problem was also presented to compare the DEA results with other optimization techniques such as a genetic algorithm (GA), a particle swarm optimization (PSO) and ant colony optimization (ACO). The results indicated that our DEA performs at least as good as these techniques as far as the project time is concerned and outperforms them in computational times and success rates. Finally, a pareto frontier was investigated, resulting in optimal solutions for a multi objective problem focusing on the tradeoff of the constrained set of parameters

Vision-based Online Defect Detection of Polymeric Film via Structural Quality Metrics

Nondestructive and contactless online approaches for detecting defects in polymer films are of significant interest in manufacturing. This paper develops vision-based quality metrics for detecting the defects of width consistency, film edge straightness, and specks in a polymeric film production process. The three metrics are calculated from an online low-cost grayscale camera positioned over the moving film before the final collection roller and can be implemented in real-time to monitor the film manufacturing for process and quality control. The objective metrics are calibrated to correlate with an expert ranking of test samples, and results show that they can be used to detect defects and measure the quality of polymer films with satisfactory accuracy

Optimization of Geometry Parameters of Inkjet-Printed Silver Nanoparticle Traces on PDMS Substrates Using Response Surface Methodology

Inkjet printing is an emerging technology with key advantages that make it suitable for the fabrication of stretchable circuits. Specifically, this process is cost-effective and less complex compared to conventional fabrication technologies. Inkjet printing has several process and geometry parameters that significantly affect the electromechanical properties of the printed circuits. This study aims to optimize the geometry parameters of inkjet-printed silver nanoparticle traces on plasma-treated polydimethylsiloxane (PDMS) substrates. The optimization process was conducted for two printed shapes, namely straight line and horseshoe patterns. The examined input factors for the straight line traces were: the number of inkjet-printed layers and line width. On the other hand, the number of cycles and amplitude were the examined input parameters for the horseshoe shape. First, the optimal number of layers and line width were found from the straight line analysis and subsequently were used in the optimization of the horseshoe pattern parameters. The optimization of the input parameters was carried out using the response surface methodology (RSM), where the objective of the optimization was to maximize the breakdown strain of the traces while maximizing the gauge factor and minimizing the ink cost. The results indicate that a 1.78 mm line width and one layer are the optimal geometry parameters for the straight line traces, while for the horseshoe pattern, the optimal parameters are one layer, a line width of 1.78 mm, amplitude of 4 mm and one cycle. The optimal straight line was designed to sustain up to 10% strain while the horseshoe pattern was designed to sustain up to 15% strain

Assessing the physical and mechanical properties of 3D printed acrylic material for denture base application

Background: Three-dimensional (3D) printing is increasingly being utilized in the dental field because of its time-saving potential and cost effectiveness. It enables dental practitioners to eliminate several fabrication steps, achieve higher precision, and attain consistency in complex prosthetic models. The properties of 3D-printed resin materials can be affected by many factors, including the printing orientation (PO) and insufficient post-curing time (CT). Purpose: This study aimed to investigate the effect of PO and CT on the mechanical and physical properties of a 3D-printed denture base resin (NextDent). Methods: 3D-printed specimens were fabricated in 0°, 45°, and 90° POs, followed by three CTs (20, 30, and 50 min). The microhardness was tested using a Vickers hardness test, while the flexural property was evaluated using a three-point bending test. Sorption and solubility were measured after the specimens had been stored in an artificial saliva for 42 days, and the degree of conversion during polymerisation was analysed using Fourier Transform Infrared (FTIR) spectroscopy. Results: The flexural strength of the material significantly increased (p 0.05) in any of the tested properties was found when the post-curing times were increased from 20 to 50 min. Significance: The highest physical and mechanical properties of the 3D-printed denture base resin can be obtained by printing vertically (90° angle to the platform base). The minimal post-curing time to achieve ideal results is 30 min, as further curing will have no significant effect on the properties of the material

A CLIPS-Based Expert System for Heart Palpitations Diagnosis

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes

3D printed denture base material: the effect of incorporating TiO2 nanoparticles and artificial ageing on the physical and mechanical properties

Objectives To evaluate the physical and mechanical properties of three-dimensional (3D) printed denture base resin incorporating TiO2 nanoparticles (NPs), subjected to a physical ageing process. Methods Acrylic denture base samples were prepared by a Stereolithography (SLA) 3D printing technique reinforced with different concentrations (0.10, 0.25, 0.50, and 0.75) of silanated TiO2 NPs. The resulting nanocomposite materials were characterized in terms of degree of conversion (DC), and sorption/solubility flexural strength, impact strength, Vickers hardness and Martens hardness and compared with unmodified resin and conventional heat-cured (HC) material. The nanocomposites were reassessed after subjecting them to ageing in artificial saliva. A fractured surface was studied under a scanning electron microscope (SEM). Results The addition of TiO2 NPs into 3D-printed resin significantly improved flexural strength/modulus, impact strength, Vickers hardness, and DC, while also slightly enhancing Martens hardness compared to the unmodified resin. Sorption values did not show any improvements, while solubility was reduced significantly. The addition of 0.10 wt% NPs provided the highest performance amongst the other concentrations, and 0.75 wt% NPs showed the lowest. Although ageing degraded the materials’ performance to a certain extent, the trends remained the same. SEM images showed a homogenous distribution of the NPs at lower concentrations (0.10 and 0.25 wt%) but revealed agglomeration of the NPs with the higher concentrations (0.50 and 0.75 wt%). Significance The outcomes of this study suggested that the incorporation of TiO2 NPs (0.10 wt%) into 3D-printed denture base material showed superior performance compared to the unmodified 3D-printed resin even after ageing in artificial saliva. The nanocomposite has the potential to extend service life of denture bases in future clinical use

A CLIPS-Based Expert System for Brain Tumor Diagnosis

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges