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The 2022 Qatar World Cup created massive global attention and generated widespread discussions on different social media platforms, including the X platform. The event was the subject of intense debate after Qatar was announced as the host. Opinions were divided, with supporters and critics weighing in based on political, ethical, cultural, and social considerations. Public sentiments evolved throughout three key phases-before, during, and after the event-shaped by numerous factors. This study aims to analyze these sentiments during these three stages based on a novel hybrid evolutionary approach. Three versions for each stage were produced by applying pre-trained word embeddings with 100 and 400 features and sentiment features combined with word embeddings. In total, nine different versions of datasets were employed to examine the proposed approach. Furthermore, five different metaheuristic algorithms were applied: the multi-verse optimizer (MVO), the genetic algorithm (GA), the particle swarm optimization (PSO), the salp swarm algorithm (SSA), and the whale optimization algorithm (WOA). The five metaheuristic algorithms were combined with the feature selection-support vector machine (FS-SVM) and weighting-support vector machine (WSVM) to examine the newly created dataset versions. The results reveal that people's perspectives shifted from negative before the event to positive during and after the event. Moreover, a comparison of the proposed MVO-WSVM and MVO-SVM-Fs approaches with other metaheuristic algorithms showed the superior accuracy of the proposed approaches in sentiment prediction. [ABSTRACT FROM AUTHOR]

This study aims to investigate the predictive capabilities of artificial neural networks (ANNs) for Al-glass composites, specifically in exploring the effect of glass particle size and content on hardness, porosity, and microstructure in Al-glass composites. Contents between 0-15 wt.% glass particles, with two size ranges-- less than 53µm, and between 53-75µm-- were incorporated within the pure aluminum matrix. Powder metallurgy was employed to produce the composite specimens. Pressing at 400 MPa was applied to the powders to produce the green compacts. The sintering temperature was 550 °C. Three sintering periods were used: one, two, and four hours. The results indicate that the most significant factors affecting the hardness and porosity were glass percentage and sintering time. The highest hardness value of 27.50 HRB was obtained in specimen with 10% glass content sintered for 4 hours, with glass grain size of 53-75 µm. Whereas the highest porosity percentage of 5.4% was recorded for specimen with 15% glass content sintered for 1 hour, with glass grain size of 53-75 µm. For ANN, three inputs and one output were established, where the Levenberg-Marquardt training algorithm neural network had the highest accuracy of prediction. With highest value of R2= 99.96% and 99.99%, and RMSE=0.06855 and 0.007141 for hardness and porosity, respectively. As such a high prediction accuracy was obtained using the ANNs, this study proves that ANN is a significant tools for the prediction of nonlinear relationships. The novelty of this study lies in the combination of glass with aluminum as a new composite material, alongside the high predictive accuracy of the model with very small error margins, demonstrating the potential of ANNs to effectively handle nonlinear relationships in composite materials. Additionally, the ANN approach significantly saves time and costs associated with experimental testing and helps in finding the optimal combination with the best values of the mechanical properties, streamlining the development process for new composite materials. [ABSTRACT FROM AUTHOR]

Recently, metastable β‐type Ti alloys have attracted attention as promising materials for medical implants due to low Young's modulus and superior biocompatibility. The former is dependent on texture developed during the thermomechanical process. Herein, new Ti–Zr–Nb–Sn–Mo alloys with ultralow Young's modulus and excellent biocompatibility are developed as promising materials for medical implants. The effect of Mo on the microstructure and mechanical properties of the Ti–18Zr–5Nb–3Sn system is investigated. X‐ray diffraction (XRD) and scanning electron microscope (SEM) results reveal that Mo acts as a β‐phase stabilizer. Moreover, mechanical behavior and recrystallization texture show strong composition dependence; the 2.5Mo alloy shows the lowest Young's modulus of 40 GPa. This is due to the formation of a recrystallization texture with strong Goss component of <001>. Corrosion behavior of the 2.5Mo alloy in the cell culture medium is similar to that of the commercially pure titanium (cp‐Ti), suggesting excellent corrosion resistance in the biological environment. Both murine fibroblasts and osteoblastic cells show good growth on the 2.5Mo alloy as the same level of that on the cp‐Ti. Implantation into rat subcutaneous tissue confirms no significant difference in the tissue reaction between the 2.5Mo alloy and the cp‐Ti. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to use Lean Six Sigma (LSS) to reduce patient waiting time in a Kuwaiti private hospital obstetrics and gynaecology clinic.Approach: The define, measure, analyse, improve and control methodology was used. The "define" stage involved identifying patients' needs, system capabilities and project objectives. The "measure" stage assessed the system's current state through data collection on waiting times. Dunnett's test, control charts and process capability analysis were used to ensure data accuracy. In the "analyse" stage, an Ishikawa diagram and Pareto chart were constructed, showing that overbooking appointments, doctors' unscheduled breaks and doctors not arriving on time were the root causes of the problem. The "improve" stage used an Arena simulation model to represent current and improved system status. The proposed solutions were implemented and monitored in the "control" stage.Findings: A sigma-level improvement of 300 per cent (0.5-2.0) was realized for appointment patients on Saturdays, with a 67 per cent reduction in waiting time. For walk-ins, the sigma level improved by 288 per cent (0.8-3.1), with a 55 per cent reduction in waiting time. For weekday appointments, the sigma level improved by 111 per cent (0.9-1.9), with a 63 per cent reduction in waiting time. For walk-ins, the sigma level improved by 69 per cent (1.6-2.7), with a 46 per cent reduction in waiting time. A cost-benefit analysis estimated the present project value at $656,459, leading to a total of $5,820,319 in savings by 2025.Originality/value: This paper fulfils the need for process improvement, increasing patients' satisfaction and hospitals' profitability using LSS. [ABSTRACT FROM AUTHOR]

Since gas metal arc welding (GMAW) is a common procedure to join different sheets in the industry, the welding direction dependence on the rolling direction was investigated for a welded DP1000, a cold-rolled dual phase steel. Microstructural investigations and mechanical tests were conducted on two weldments; one being welded parallel to the rolling direction and the second transverse to the rolling direction and the results were compared. It was found that there is a higher degree of softening of the heat-affected zone in the sample welded parallel to the rolling direction. Although this softening did not significantly decrease the tensile strength or the hardness of this sample when compared to the one welded transverse to the rolling direction, the elongation was twice as high in the former sample. That is related to the slower heat dissipation in the sample welded parallel to the rolling direction as confirmed by its microstructural products. [ABSTRACT FROM AUTHOR]