SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Comparison of an experimental single application two-step 25600 ppm Ammonium Fluoride / 34100 - 48600 ppm Nano-Calcium Fluoride solution vs. a one-step 7700 ppm Ammonium Fluoride varnish for Preventing Enamel Loss from Erosion and Attrition in vitro(King’s College London, 2024) Almejrad, Lamya; Austin, Rupert; Bartlett, DavidIntroduction: The potential for high-fluoride treatments to prevent progression of erosion and attrition is not fully understood. This thesis investigated the effect of an experimental two-step ammonium fluoride/nano-calcium fluoride formulation (25600 ppm NH4F plus 34100 - 48600 ppm CaF2) versus a single-application one-step 7700 ppm ammonium fluoride (NH4F) varnish on step height loss (μm) of polished and natural human enamel undergoing erosion and attrition in vitro. Material and methods: Three restorative dental materials and human enamel (Occlusal vs. Buccal) samples were used to validate the attrition simulation method. Samples were subjected to attritional wear using leucite-reinforced CAD/CAM ceramic antagonists in an electrodynamic wear simulator (200 cycles, 80 N load, 0.7 mm horizontal slide). Following validation, polished and natural (unpolished) enamel samples were pre-treated with either deionized water (DIW negative control), NH4F varnish (positive control) or a two-step NH4F/CaF2 solution. After surface wiping, samples were subjected to erosion (0.3% citric acid solution immersion, pH 3.8, for 5, 10, 15, 20 and 60 minutes) and attrition (200 strokes). Enamel wear was measured using non-contact laser profilometry (NCLP). Enamel surface and sub-surface mechanical testing was conducted using micro- and nanoindentation. Enamel surface and sub-surface qualitative examination was conducted using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Results: The attrition method revealed that the glass ionomer material experienced the most wear, measuring 177.8 μm (±16.9). This was significantly more than the fine particle composite, which showed 22.2 μm (±1.3) of wear, and the micro-filled composite, which had the least wear at 13.6 μm (±1.02) (P < 0.001). Enamel samples from buccal vs. occlusal sources experienced non-significantly different step height enamel wear (P<0.05). The attrition wear generation was consistent with co-efficient of variation <10%. Polished enamel samples treated with surface fluoride treatments showed significantly reduced step height enamel loss vs. control (p<0.001): Mean (SD) enamel loss for DIW treatment was 1.97 μm (±0.14) after erosion and 36.55 μm (±1.79) after erosion/attrition. NH4F treatment reduced loss to 0.58 μm (±0.08) after erosion and 32.71 μm (±2.63) after erosion/attrition (p<0.001). NH4F/CaF2 treatment further reduced loss to 0.41 μm (±0.06) after erosion and 24.08 μm (±3.15) after erosion/attrition (p<0.001). This was supported by the microhardness data: fluoride-treated enamel experienced reduced hardness changes following both erosion and erosion/attrition vs. non-fluoride treated enamel (p<0.001). For natural enamel, the experimental NH4F/CaF2 solution significantly reduced enamel loss after all erosion durations (5, 20, 60 minutes) and after 200 strokes of attrition. After 5 minutes of erosion, enamel loss compared to DIW was significantly reduced (p<0.001). Mean (SD) enamel loss in the NH4F/CaF2 treated group was reduced to 0.21 μm (±0.13) after erosion and 9.82 μm (±1.46) after erosion/attrition in comparison with DIW treated groups which was 0.79 μm (±0.32) after erosion and 15.58 μm (±2.49) after erosion/attrition. The surface and sub-surface SEM and EDS data supported these findings. Conclusion: The two-step ammonium fluoride solution (NH4F/CaF2) reduced step height enamel loss and hardness changes occurring in polished and natural enamel during simulated erosion and attrition in vitro.16 0Item Restricted Predicting Customer Attrition in B2B SaaS Using Machine Learning Classification(Saudi Digital Library, 2023-09-15) Alalawi, Zainab; Fiaschetti, MaurizioCustomer retention and customer loss are crucial metrics in subscription-based industries like SaaS companies. Customer discharge is a significant concern for this type of business, as clients have the flexibility to terminate the service at any time. This can lead to adverse effects on the company’s revenue stream. If SaaS businesses can accurately predict the number of customers who will cancel their subscriptions and those who will continue using their services within a specific timeframe, they can more effectively forecast their revenue, cash flow, and any future growth plan accordingly. Predicting subscription renewals and cancelations remains a challenging problem for any SaaS company. However, with the ongoing advancement in machine learning and artificial intelligence, the potential for accurately forecasting this issue has significantly improved. The study examines customer attrition and customer retention prediction in a quantitative method by utilizing several different machine learning algorithms with Python, namely Logistics regression, Naïve Baye, and random forest algorithms. Data was collected from the case company’s database and manipulated to fit the algorithms. The dataset includes the customers' business data such as spend, customer platform usage data, customer service history data, and the date of the next payment. To identify the best hyperparameters for each machine- learning algorithm, A tuning technique, in particular Grid Search, was employed. Subsequently, the algorithm models were trained and assessed using optimized hyperparameters on the fitted data. Once the models were trained, they were applied to test data to obtain the analysis results. The model’s performance was measured on the quantitative model performance metrics. including F1-Score, Area under Curve (AUC), and Accuracy.37 0