The influence of flap design and technique on dental implant success, prognosis and morbidity: Mini review

The importance of flap design come from the need of healthy, integrated and esthetically acceptable soft tissue around the implant. The aim of this mini review tries to address the factors that may affect the flap design in dental implant. The references that included in this study that focus on the flap design, types of flaps and flaps technique from incision to closure. Some of the factors have significant and direct impact on the implant success. Although other factors still mandatory to achieve optimum results especially in esthetic zone. This mini review concluded that factors aff ecting the flap design some related to the patient (patient factor) and more factors related to operator skill and proper treatment plane (operator factor)

Hydrothermal liquefaction of biosolids

Hydrothermal liquefaction (HTL) is a promising thermochemical conversion process to convert biosolids into renewable crude oil. HTL process can be achieved at temperatures between 200 to 350°C, pressures between 50 to 250 bar, and residence time between 1 and 60 minutes. The HTL produces four phases: renewable crude oil, aqueous, gaseous and solid phases. For the process to be upgraded to an industrial scale, it is needed to gain a better understanding of the HTL of biosolids. However, there is limited information to validate the effects of the interactions between the biosolid content under HTL reaction conditions on the yield and the composition of the produced renewable crude oil. The primary objective of this research is to provide a better understanding of the HTL of biosolids, which was achieved through the following detailed objectives. The first objective is to quantify the variability in the biosolids composition to determine the chemical compositions of biosolids. The second objective is to understand how this variable biosolids feedstock behaves through HTL, especially to measure the effects of organic compounds of biosolids: lipids, proteins, carbohydrates, and lignins on the HTL yields. The third objective is to provide a new understating of the characterisation of HTL products from biosolids by identifying the effects of biosolid components and the HTL conditions on both the distributions of the HTL products’ yields and on the qualities of renewable crude oil. The fourth objective is to assess the use of biosolids with dominant organic fraction via different reaction temperatures and residence times on the composition and fractions of the produced renewable crude oil. From the results of the experiments, biosolids have different characters that affect the yield and quality of renewable crude oil. Applying a Van Krevelen diagram to compare biosolids with other biomass indicated that only some biosolids samples have similar characteristics to that of biomass. The difference in the characteristic of the organic content of biosolid samples could depend on several reasons, such as the sources of the biosolids and the treatment process. The effects of the biosolids’ composition on the HTL yield show that lipids and proteins have positive impacts on the renewable crude oil yield, while carbohydrates and insoluble lignin led to an increase in the solid residue. The renewable crude oil contained a high amount of high-boiling point materials in comparison with low-boiling point materials for all biosolids samples used in this study. The effect of the operating conditions, such as temperature was significant. The renewable crude yield usually increases with an increase in temperature until a specific temperature is reached, at which point the renewable crude yield starts to decrease. Various residence times also affected renewable crude oil yields significantly. The optimal residence times depended on the biosolids content and temperature. The HTL of biosolids with different organic fractions resulted in different renewable crude oil compositions, which contained a complex mixture of >300 major compounds that were identified using Gas chromatography-mass spectroscopy analyser. The predominant components identified from the lipid, protein, carbohydrate and lignin constituents were cyclic terpanes and terpenes, along with nitrogenous, oxygenated, and phenolic components. Based on the boiling point of the produced compounds, high gasoline and naphtha-like and high diesel-like yields were produced from biosolid samples with high lipid and protein content, while the kerosene-like best yield was generated from a high lipid sample. A significant gas oil-like yield was produced from the high lipid and carbohydrate biosolid samples, while a high yield of wax, lubricating oil and vacuum gas oil-like contents were generated from the high lignin sample. In summary, the results of the outcomes of this work and the methods used to analyse the chemical compositions of biosolids can form a significant facet of future industrial development of HTL of biosolids, particularly in commercial plants design and management. Finally, it is hoped that the methods presented here, especially the methods used to analyse the chemical compositions of biosolids and the outcomes of this work, especially regarding the composition of the produced renewable crude oil, can form a significant facet of future industrial development of the HTL of biosolids.Thesis (Ph.D.) -- University of Adelaide, School of Chemical Engineering and Advanced Materials, 202

Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis

Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%)

A Hybrid Cracked Tiers Detection System Based on Adaptive Correlation Features Selection and Deep Belief Neural Networks

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%)