Comparative Analysis of Abrasive Materials and Polishing System on the Surface Roughness of Heat-Polymerized Acrylic Resins

Objective The aim of this in vitro experiment was to see how the operator's manual skills, polishing equipment, and abrasive materials affected the surface roughness of denture base resins. Materials and Methods Forty polymethyl methacrylate (PMMA) specimens were created and polished by using two different polishing systems, namely hand and automatic polishing machines. Three operators hand-polished 30 of specimens with eggshell powder and pumice, while 10 were automatically polished (n = 5). A profilometer was used to determine the average surface roughness (Ra) after polishing. The Ra values for the specimens hand-polished were analyzed by using paired sample testing. The Ra values for all polished specimens were analyzed by using a one-way ANOVA. Differences between the two abrasive materials as well as the polishing system were determined by using the Bonferonni tests (p = 0.05). Results and Conclusion For the PMMA specimens hand-polished, there was a strong connection in the Ra values. There were also significant variations in the Ra values across the three operators (p < 0.001). The automated technique created a substantially smoother surface than the traditional technique (p = 0.001). The greatest Ra values (0.20 µm) were found in specimens polished traditionally by using pumice, whereas the lowest Ra values (0.04 µm) were found in specimens polished mechanically with eggshell powder. The automated polishing system was the most effective polishing method when the Ra values were connected to the level of smoothness.

Quantitative characterization of the microstructure of in situ aluminum matrix composites

A quantitative assessment of the microstructure parameters is necessary for making informed decisions on the development and adjustment of technological parameters for the production of cast metal matrix composites. This study gives an estimate of the size and distribution of the reinforcing phases in the structure of in-situ Al-Mg2Si aluminum matrix composites using an automated technique for analyzing metallographic images realized in the ImageJ open-source software with developed macros. A comparison of the quantitative parameters of the microstructure of composites in different parts of the ingot is carried out. The central regions of the ingot are distinguished by higher values of the average quantity of particles per unit of the microsection surface area in comparison with the peripheral regions. The average size of the synthesized Mg2Si reinforcing particles was 16 μm and practically did not vary in different areas.

Modified deep learning ResNeXt model for Human long bone fracture detection and classification

The field of medical science is going to take advantage of Machine learning. It has increased dramatically over the last decade. Nowadays, you can see other innovations used in medical sciences, such as machine learning and deep learning. They can help to diagnose the illness or cause. It can also aid in the healing process by keeping notes. At a similar pace, an upper hand has been provided to the physicians for image processing by incorporating computers. Bone fractures are normal these days, and the identification of fractures is a critical part of orthopedic X-ray imaging. The automated technique lets the doctor quickly begin medical treatment. Using Machine Learning and CNN (Convolutional Neural Network), we suggest a new deep learning model perform bone diagnosis by eliminating discontinuity followed by segmentation of the image in a system that detects bone fractures. It overcomes the shortcomings of the previous approach that operates only on examination of the texture features. The proposed deep learning modified ResNeXt model performs much better than the state-of arts.

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

Specification Guided Automated Synthesis of Feedback Controllers

The growing use of complex Cyber-Physical Systems (CPSs) in safety-critical applications has led to the demand for the automatic synthesis of robust feedback controllers that satisfy a given set of formal specifications. Controller synthesis from the high-level specification is an NP-Hard problem. We propose a heuristic-based automated technique that synthesizes feedback controllers guided by Signal Temporal Logic (STL) specifications. Our technique involves rigorous analysis of the traces generated by the closed-loop system, matrix decomposition, and an incremental multi-parameter tuning procedure. In case a controller cannot be found to satisfy all the specifications, we propose a technique for modifying the unsatisfiable specifications so that the controller synthesized for the satisfiable subset of specifications now also satisfies the modified specifications. We demonstrate our technique on eleven controllers used as standard closed-loop control system benchmarks, including complex controllers having multiple independent or nested control loops. Our experimental results establish that the proposed algorithm can automatically solve complex feedback controller synthesis problems within a few minutes.

Automated Filling of Dry Micron-Sized Particles into Micro Mold Pattern within Planar Substrates for the Fabrication of Powder-Based 3D Microstructures

Powder-based techniques are gaining increasing interest for the fabrication of microstructures on planar substrates. A typical approach comprises the filling of a mold pattern with micron-sized particles of the desired material, and their fixation there. Commonly powder-loaded pastes or inks are filled into the molds. To meet the smallest dimensions and highest filling factors, the utilization of dry powder as the raw material is more beneficial. However, an appropriate automated technique for filling a micro mold pattern with dry micron-sized particles is missing up to now. This paper presents a corresponding approach based on the superimposition of high- and low-frequency oscillations for particle mobilization. Rubber balls are utilized to achieve dense packing. For verification, micromagnets are created from 5 µm NdFeB powder on 8” Si substrates, using the novel automated mold filling technique, as well as an existing manual one. Subsequent atomic layer deposition is utilized to agglomerate the loose NdFeB particles into rigid microstructures. The magnetic properties and inner structure of the NdFeB micromagnets are investigated. It is shown that the novel automated technique outperforms the manual one in major terms.

Inferring entire spiking activity from local field potentials

Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based technique which may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performing different tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPs with good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUA and MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate that LFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spike relationship and for the development of LFP-based BMIs.

Deep Learning Applications in Geosciences: Insights into Ichnological Analysis

Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0–6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field.