Digital therapeutics (DTx) in hearing research have emerged as a new category of therapies providing evidence-based intervention via digital means, such as software, smartphone apps, or websites. However, although relatively new, they are not well-established. In this article, we review DTx technologies in hearing research fields, focusing on three categories: prevention and diagnosis, aid (assistance), and cure (digital medicine). We observe that most DTx systems require interactions with users (or patients) without the direct support of clinical professionals to obtain or collect medical evidence; this makes training (or education) features crucial to the therapy’s success. In this view, this article discusses the education or training functions of the current DTx and their contribution and purposes. The impact of emerging artificial intelligence (AI) on DTx in hearing research is being explored, and the future of DTx concerning AI integration is being discussed. We believe that this work will contribute to a better understanding of the current and future DTx technological advancements and, in particular, shed light on the field of hearing research.

Micromixers are critical components in the lab-on-a-chip or micro total analysis systems technology found in micro-electro-mechanical systems. In general, the mixing performance of the micromixers is determined by characterising the mixing time of a system, for example the time or number of circulations and vibrations guided by tracers (i.e., fluorescent dyes). Our previous study showed that the mixing performance could be detected solely from the electrical measurement. In this paper, we employ electromagnetic micromixers to investigate the correlation between electrical and mechanical behaviours in the mixer system. This work contemplates the “anti-reciprocity” concept by providing a theoretical insight into the measurement of the mixer system; the work explains the data interdependence between the electrical point impedance (voltage per unit current) and the mechanical velocity. This study puts the electromagnetic micromixer theory on a firm theoretical and empirical basis.

A low-powered acoustic micromixer has been presented in this paper. This newly invented system consists of an open-top mixing chamber and a permanent magnet mixer base. A voice coil is attached to the bottom of the mixing chamber with a polydimethylsiloxane membrane. The optimal driving frequency of each mixing test can be co-determined during (or prior to) the mixing via an electrical impedance (Ze) measurement owing to inherent electro-acoustic coupling of the system. When the mixer is driving at its resonance frequency found from the Ze, mixing efficiency is maximized characterizing millie-watt efficiency to achieve a mixing time scale comparable to other acoustic mixers. In addition, the present mixer demonstrates feasibility of a portable microfluidic system, wherein the degree of mixing may be tracked by Ze monitoring from a smart-phone, which may allow each experiment to be controlled remotely over existing network environment. We believe that our study demonstrates a feasible application of microelectromechanical systems technology for the Internet of Things. [2017-0119]

This article reviews the development of metamaterials (MM), starting from Newton's discovery of the wave equation, and ends with a discussion of the need for a technical taxonomy (classification) of these materials, along with a better defined definition of metamaterials. It is intended to be a technical definition of metamaterials, based on a historical perspective. The evolution of MMs began with the discovery of the wave equation, traceable back to Newton's calculation of the speed of sound. The theory of sound evolved to include quasi-statics (Helmholtz) and the circuit equations of Kirchhoff's circuit laws, leading to the ultimate development of Maxwell's equations and the equation for the speed of light. Be it light, or sound, the speed of the wave-front travel defines the wavelength, and thus the quasi-static (QS) approximation. But there is much more at stake than QSs. Taxonomy requires a proper statement of the laws of physics, which includes at least the six basic network postulates: (P1) causality (non-causal/acausal), (P2) linearity (non-linear), (P3) real (complex) time response, (P4) passive (active), (P5) time-invariant (time varying), and (P6) reciprocal (non-reciprocal). These six postulates are extended to include MMs.