Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

Applications involving high-power ultrasound are expanding rapidly as ultrasonic intensification opportunities are identified in new fields. This is facilitated through new technological developments and an evolution of current systems to tackle challenging problems. It is therefore important to continually update both the scientific and commercial communities on current system performance and limitations. To achieve this objective, this paper addresses two key aspects of high-power ultrasonic systems. In the first part, the review of high-power applications focuses on industrial applications and documents the developing technology from its early cleaning applications through to the advanced sonochemistry, cutting, and water treatment applications used today. The second part provides a comprehensive overview of measurement techniques used in conjunction with high-power ultrasonic systems. This is an important and evolving field which enables design and process engineers to optimize the behavior and/or operation of key metrics of system performance, such as field distribution or cavitation intensity.

We demonstrate an in situ ultrasonic approach to influence self-assembly across the supramolecular to micron length scales, showing enhancement of supramolecular interactions, chirality and orientation, which depends on the peptide sequence and solvent environment. This is the first successful demonstration of using oscillating pressure waves to generate anisotropic organo- and hydrogels consisting of oriented tripeptides structures.

A combination of theoretical modeling and experimental analysis is used to predict and assess the performance of 1-3 composite transducers for operation in an air-coupled environment. Specifically, finite element analysis, supported by linear systems modeling, is employed to evaluate transmission and reception characteristics over the complete volume fraction range, for operating frequencies in the region of 500 kHz, and the;theory confirmed using novel experimental techniques. The theoretical approach is then extended to assess the influence of matching and backing on transducer sensitivity and bandwidth. Throughout, the influence of air propagation is included in the simulation approach and highlighted where this factor is significant. The work is expected to provide useful design guidelines for the practical ultrasonic engineer

Ultrasound, i.e. high frequency oscillating pressure waves, is commonly used to overcome kinetic barriers associated with dissolution, assembly and gelation. We demonstrate that ultrasound energy may also be used to achieve transient reorganization of supramolecular nanostructures, which revert back to the original state when sound is switched off. Aromatic peptide amphiphiles, Fmoc-FL and -YL were used to study the transient acoustic response. These systems showed temporary supramolecular transitions that were sequence dependent. The changes observed were due to an altered balance between H-bonding and π-stacking, giving rise in changes in chiral organisation of peptide building blocks. Transient reconfiguration was visualized by TEM and changes in supramolecular interactions characterized by fluorescence, FT-IR and CD. Remarkably, significant differences are observed when compared to thermal heating, which relates to the oscillating and directional characteristics of ultrasound when delivering heat to a system.

The growth of the automated welding sector and emerging technological requirements of Industry 4.0 have driven demand and research into intelligent sensor-enabled robotic systems. The higher production rates of automated welding have increased the need for fast, robotically deployed Non-Destructive Evaluation (NDE), replacing current time-consuming manually deployed inspection. This paper presents the development and deployment of a novel multi-robot system for automated welding and in-process NDE. Full external positional control is achieved in real time allowing for on-the-fly motion correction, based on multi-sensory input. The inspection capabilities of the system are demonstrated at three different stages of the manufacturing process: after all welding passes are complete; between individual welding passes; and during live-arc welding deposition. The specific advantages and challenges of each approach are outlined, and the defect detection capability is demonstrated through inspection of artificially induced defects. The developed system offers an early defect detection opportunity compared to current inspection methods, drastically reducing the delay between defect formation and discovery. This approach would enable in-process weld repair, leading to higher production efficiency, reduced rework rates and lower production costs.