Patients' Readiness Towards Teledentistry in the Malaysian Urban Population Attending an Undergraduate Teaching University.

Introduction: COVID-19 disease has resulted in suspension of all nonurgent routine dental treatments. In view of COVID-19 situation, social distancing, movement restriction orders, and affected health care systems, there is an urgent need to resume and deliver oral health care remotely. Hence, alternative means of dental care should be available for both patients and dentists. Therefore, this study aims to assess patients' readiness for teledentistry in Malaysian urban population attending an undergraduate teaching university. Methods: A cross-sectional study was conducted among 631 adult patients visiting the Faculty of Dentistry, SEGi University, from January 2020 to May 2021 in Selangor, Malaysia. A validated, self-administered, 5-point Likert scale online questionnaire comprising five domains was administered. (1) Patients' demographics and dental history, (2) patients' access to teledentistry, (3) patients' understanding towards teledentistry, (4) patients' willingness, and (5) barriers in using teledentistry were used to collect the required information. Results: Six hundred and thirty-one (n = 631) participants responded to the questionnaire. Ninety percent of patients were able to connect to Wi-Fi services independently and 77% participants were comfortable using online communication platforms. Seventy-one percent of the participants agreed that video and telephone clinics can reduce chances of infection rather than face-to-face consultation during the pandemic. Fifty-five percent of patients felt that virtual clinics would save time and 60% thought it could reduce travelling costs. Fifty-one percent showed their willingness to use video or telephone clinics when implemented at onsite clinics. Conclusion: Our study shows the readiness of patients to accept teledentistry as an alternative method of oral care if appropriate training and education are provided. The results of this study have prompted an increase in patients' education and shown a need to train clinicians and patients to integrate this technology at SEGi University. This might facilitate unhindered dental consultation and care in all situations.

Value of Information Based Data Retrieval in UWSNs.

Sensor nodes in underwater sensor networks may acquire data at a higher rate than their ability to communicate over underwater acoustic channels. Autonomous underwater vehicles may mitigate this mismatch by offloading high volumes of data from the sensor nodes and ferrying them to the sink. Such a mode of data transfer results in high latency. Occasionally, these networks need to report high priority events such as catastrophes or intrusions. In such a scenario the expectation is to have a minimal end-to-end delay for event reporting. Considering this, underwater vehicles should schedule their visits to the sensor nodes in a manner that aids efficient reporting of high-priority events. We propose the use of the Value of Information metric in order to improve the reporting of events in an underwater sensor network. The proposed approach classifies the recorded data in terms of its value and priority. The classified data is transmitted using a combination of acoustic and optical channels. We perform experiments with a binary event model, i.e., we classify the events into high-priority and low-priority events. We explore a couple of different path planning strategies for the autonomous underwater vehicle. Our results show that scheduling visits to sensor nodes, based on algorithms that address the value of information, improves the timely reporting of high priority data and enables the accumulation of larger value of information.

Towards a computational model of social norms.

We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the self, peers and public. This model can be used to create realistic social simulations, to explain or predict human behavior in specific scenarios, or as a component of robots or agents that need to interact with humans in specific social-cultural settings. We validate the model by using it to represent a complex deception scenario and showing that it can yield non-trivial insights such as the explanation of apparently irrational human behavior.

Comparative analysis of system identification techniques for nonlinear modeling of the neuron-microelectrode junction.

Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction.