Depending on the requirements of specific applications, implanted materials including metals, ceramics, and polymers have been used in various disciplines of medicine. Titanium and its alloys as implant materials play a critical role in the orthopedic and dental procedures. However, they still require the utilization of surface modification technologies to not only achieve the robust osteointegration but also to increase the antibacterial properties, which can avoid the implant-related infections. This article aims to provide a summary of the latest advances in surface modification techniques, of titanium and its alloys, specifically in biomedical applications. These surface techniques include plasma spray, physical vapor deposition, sol-gel, micro-arc oxidation, etc. Moreover, the microstructure evolution is comprehensively discussed, which is followed by enhanced mechanical properties, osseointegration, antibacterial properties, and clinical outcomes. Future researches should focus on the combination of multiple methods or improving the structure and composition of the composite coating to further enhance the coating performance.

/pdf/surface-modification-techniques-of-titanium-and-its-alloys-klwoc8xja1.pdf

Surface Modification Techniques of Titanium and its Alloys to Functionally Optimize Their Biomedical Properties: Thematic Review.

Thermal barrier coating is critical for thermal insulation technology, making the underlying base metal capable of operating at a melting temperature of 1150 °C. By increasing the temperature of incoming gases, engineers can improve the thermal and mechanical performance of gas turbine blades and the piston cylinder arrangement. Recent developments in the field of thermal barrier coatings (TBCs) have made this material suitable for use in a variety of fields, including the aerospace and diesel engine industries. Changes in the turbine blade microstructure brought on by its operating environment determine how long and reliable it will be. In addition, the effectiveness of multi-layer, composite and functionally graded coatings depends heavily on the deposition procedures used to create them. This research aims to clarify the connection between workplace conditions, coating morphology and application methods. This article presents a high-level overview of the many coating processes and design procedures employed for TBCs to enhance the coating’s surface quality. To that end, this review is primarily concerned with the cultivation, processing and characteristics of engineered TBCs that have aided in the creation of specialized coatings for use in industrial settings.

/pdf/processing-and-advancements-in-the-development-of-thermal-rtzvqcgp.pdf

Processing and Advancements in the Development of Thermal Barrier Coatings: A Review

We apply recent advances in machine learning and computer vision to a central problem in materials informatics: the statistical representation of microstructural images. We use activations in a pretrained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone.

Inferring low-dimensional microstructure representations using convolutional neural networks

A real-time evaluation of fire severity inside a building could facilitate decision-making in firefighting and rescue operations. This work explores the real-time prediction of transient fire scenarios by using external smoke images and deep learning algorithms. A big database of 1845 simulated compartment fire scenarios is formed. Three input parameters (constant fire heat release rate, opening size, and fuel type) are paired with the external smoke images, and then trained by Convolutional Neural Network (CNN) model. Results show that by training either the front-view or side-view smoke images, the artificial intelligence (AI) method can well identify the transient fire heat release rate inside the building, even without knowing the burning fuels, and the error is no more than 20%. This work demonstrates that the deep learning algorithms can be trained with simulated smoke images to determine the hidden fire information in real-time and shows great potential in smart firefighting applications. 

Predicting transient building fire based on external smoke images and deep learning

The Role of Machine Learning in Tribology: A Systematic Review

Deep learning based inverse model for building fire source location and intensity estimation

Optimization of control parameters for plasma spraying process is of great importance in thermal spray technology development. Engineers may limit themselves to local optimal solution by considering countable potential design solutions when selecting the plasma spray parameters in practice. This work proposes one decision support model by employing convolutional neural network (CNN) to explore the suitability of preliminary design. The approach aims to help engineers select global optimal solution with short time and low labor cost by invoking the models' capability of extracting potential features of in-flight particle characteristics. Simulation results make it possible to analyze new spraying process and train the designed model under the condition of insufficient experience and data. Firstly, the distributions of particle status obtained from simulation results act as the input and the control parameters are the output. Secondly, the projections between the in-flight particles and the control parameters are built implicitly and analyzed through CNN models. Thirdly, we validate the statistical information of particle state distributions through visualizing the feature maps and filters. Finally, the trained CNN models are verified by the fitted Gaussian distributions with basically consistent results. By building projections of in-flight particles and control parameters, new entrants and apprentices are capable of deducing the control parameters with the help of the pre-trained CNN model, thus cutting down the threshold for new practitioners.

Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks

Application of combined transfer learning and convolutional neural networks to optimize plasma spraying

In this article, the design considerations of W-band planar integrated array antennas for radar applications, i.e., integration, performance, robustness and others, are summarized as a reference for engineers and researchers in the field of civilian radar industry. Three different W-band radar antennas proposed by the authors are also exhibited and discussed as examples.

Design Considerations and Examples of Planar Integrated Array Antennas for W-Band Radars

Wangzaozin A, an ent-kaurene diterpenoid isolated from Isodon racemosa (Hemsl) Hara, promotes the polymerization of intracellular microtubules as well as purified tubulin, which is similar to other known microtubule stabilizers. Our pharmacological results showed that wangzaozin A induced G2/M cell cycle arrest and the significant inhibition of cancer cell proliferation. A molecular docking study indicated that wangzaozin A could bind to both the taxane and laulimalide (lau) sites on β-tubulin, which is a novel binding mode that differs from that of known microtubule stabilizers. Furthermore, molecular dynamics simulation and binding free energy calculations demonstrated that wangzaozin A could stably bind to taxane and lau sites simultaneously and form a double-bonded complex. The binding mode of wangzaozin A to the taxane site was more similar to that of epothilone A than paclitaxel. Our results demonstrate that wangzaozin A represents a novel class of microtubule stabilizers, and may serve as a potential microtubule-targeting lead compound for further structural optimization. 

Wangzaozin A, a potent novel microtubule stabilizer, targets both the taxane and laulimalide sites on β-tubulin through molecular dynamics simulations.

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