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The shift towards conservative, adhesive, post-endodontic ceramic restorations represents a significant advance in restorative dentistry. By focusing on minimally invasive techniques and the use of adhesive materials, clinicians can achieve better long-term outcomes in terms of tooth preservation, function and aesthetics. The key lies in the careful assessment of each case, ensuring that the chosen restoration aligns with the principles of biomimetic dentistry, which seeks to restore the tooth in a manner that closely mimics its natural form and function. The purpose of this case series is to underscore the significance of adhesive post-endodontic ceramic restorations in the rehabilitation of the form, function and aesthetics of mutilated posterior teeth. CPD/Clinical Relevance: The use of conservative posterior indirect adhesive restorations can enhance the long-term survival of endodontically treated teeth. [ABSTRACT FROM AUTHOR]

There are diverse parameters available for measuring performance of an academic institute. Graduation rate of an institute is an important indicator of institute's success. It is essential to understand which factors lead to better graduation rates. Hence, a prediction system which helps institutes well in advance to avoid poor graduation rate is required. In this study, a novel adaptive dimensionality reduction model is proposed using evolutionary computing and machine learning to better predict institute graduation rate. This work has explored the feature optimization capacity of evolutionary algorithm with weight assignment approach to each dimension. A high dimensional dataset is considered for analyzing attributes that affect institute graduation rates. Proposed model uses adaptive approach of incrementing weights of contributing features which lead to minimum error. Experimental results show that proposed model yields optimum dimensions, low execution time and minimum error. Predictive analysis presented could lead to useful future directions for education domain stakeholders. [ABSTRACT FROM AUTHOR]

Utilising the acoustic modality for passive detection and localisation of low-flying aircraft and gunshots is vital for border security and situational awareness. This paper presents a comprehensive experimental approach for detecting and estimating the direction of arrival of a single acoustic source using a single vector sensor and two different algorithms: acoustic intensity and velocity covariance. The study includes a thorough comparison of both algorithms for the direction of arrival estimation of a stationary continuous sound source, a hovering drone, and a propeller-driven two-seater manned aircraft flying at low altitudes in various environments. The research findings, which show that both algorithms provide similar estimates for the direction of arrival of the acoustic target in the frequency and time domains, provide a solid foundation for further exploration. Additionally, the results of an array of scalar sensors towards the direction of arrival estimation, using the cross-correlation method at the lab level, are also presented to complement the acoustic vector sensor. A system built around acoustic vectors and scalar sensors can serve as a passive surveillance and target detection system, providing a comprehensive solution for defence and acoustics. [ABSTRACT FROM AUTHOR]

• To develop a novel early-stage brain tumor prediction model using ensemble deep networks with enhanced optimization for forecasting the brain tumors in the patients to treat them with the right medications, which helps to save their lives. • To choose the optimal features, where the weight of the features is optimized by the designed Enhanced Artificial Rabbits Optimizer (EARO) algorithm for attaining the best weight value by maximizing the correlation coefficient among divergent classes and minimizing the correlation value among similar classes. • To design an EARO algorithm for optimizing the weights during the feature selection process to increase the performance over brain tumor prediction. • To ensemble 1-Dimensional Convolutional Neural Network (1DCNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Deep Temporal Convolution Network (DTCN) using high ranking method along with the dilation and attention network for designing Dilated and Attention-based Ensemble Learning Network (DAELNet) for predicting the brain tumors effectively in the prior stage. • To validate the performance of the developed early-stage brain tumour prediction model over the existing models and algorithms concerned with different effectiveness measures. The integration of deep learning into brain data analysis has notably boosted the field of biomedical data analysis. In the context of intricate conditions like cancer, various data modalities can reveal distinct disease characteristics. Brain data has the potential to expose additional insights compared to using the data sources in isolation. Moreover, techniques are selected and prioritized based on the speed and accuracy of the data. Therefore, a new deep learning technique is assisted in predicting the brain tumor from the brain data to provide accurate prediction outcomes. The brain data required for predicting the brain tumor is garnered through various online sources. Then, the collected data are applied to the data preprocessing phase for cleaning the collected brain data and then applied to the data transformation method to improve the efficiency for providing better decision-making over prediction. The transformed data is then offered to the weighted feature selection process, where the weights of the features are optimized through the proposed Enhanced Artificial Rabbits Optimizer. The selection of weighted features is primarily adopted for solving the data dimensionality issues and these resultant features are given to the Dilated and Attention-based Ensemble Learning Network to provide the effective prediction outcome, where the deep learning structures like 1-Dimensional Convolutional Neural Networks, Bidirectional Long Short-Term Memory (BiLSTM), Deep Temporal Convolution Network are ensembled in the DAEL network. Finally, the prediction outcome attained from the proposed model is validated through the existing brain tumor prediction frameworks to ensure the efficacy of the implemented scheme. [ABSTRACT FROM AUTHOR]