Compact Dual-Band Dipole Antenna with Asymmetric Arms for WLAN Applications

A dual-band dipole antenna that consists of a horn- and a C-shaped metallic arm is presented. Depending on the asymmetric arms, the antenna provides two −10 dB impedance bandwidths of 225 MHz (about 9.2% at 2.45 GHz) and 1190 MHz (about 21.6% at 5.5 GHz), respectively. This feature enables it to cover the required bandwidths for wireless local area network (WLAN) operation at the 2.4 GHz band and 5.2/5.8 GHz bands for IEEE 802.11 a/b/g standards. More importantly, the compact size (7 mm × 24 mm) and good radiating performance of the antenna are profitable to be integrated with wireless communication devices on restricted RF-elements spaces

Robustness of Physics-Informed Neural Networks to Noise in Sensor Data

Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating physics-based domain knowledge into neural network models for many important real-world systems. They have been particularly effective as a means of inferring system information based on data, even in cases where data is scarce. Most of the current work however assumes the availability of high-quality data. In this work, we further conduct a preliminary investigation of the robustness of physics-informed neural networks to the magnitude of noise in the data. Interestingly, our experiments reveal that the inclusion of physics in the neural network is sufficient to negate the impact of noise in data originating from hypothetical low quality sensors with high signal-to-noise ratios of up to 1. The resultant predictions for this test case are seen to still match the predictive value obtained for equivalent data obtained from high-quality sensors with potentially 10x less noise. This further implies the utility of physics-informed neural network modeling for making sense of data from sensor networks in the future, especially with the advent of Industry 4.0 and the increasing trend towards ubiquitous deployment of low-cost sensors which are typically noisier

Design of Turing Systems with Physics-Informed Neural Networks

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction-diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10\%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design

Northern Hemisphere Urban Heat Stress and Associated Labor Hour Hazard from ERA5 Reanalysis

Increasing surface air temperature is a fundamental characteristic of a warming world. Rising temperatures have potential impacts on human health through heat stress. One heat stress metric is the wet-bulb globe temperature, which takes into consideration the effects of radiation, humidity, and wind speed. It also has broad health and environmental implications. This study presents wet-bulb globe temperatures calculated from the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis and combines it with health guidelines to assess heat stress variability and the potential for reduction in labor hours over the past decade on both the continental and urban scale. Compared to 2010–2014, there was a general increase in heat stress during the period from 2015 to 2019 throughout the northern hemisphere, with the largest warming found in tropical regions, especially in the northern part of the Indian Peninsula. On the urban scale, our results suggest that heat stress might have led to a reduction in labor hours by up to ~20% in some Asian cities subject to work–rest regulations. Extremes in heat stress can be explained by changes in radiation and circulation. The resultant threat is highest in developing countries in tropical areas where workers often have limited legal protection and healthcare. The effect of heat stress exposure is therefore a collective challenge with environmental, economic, and social implications.publishedVersio

Molecular identification for epigallocatechin-3-gallate-mediated antioxidant intervention on the H2O2-induced oxidative stress in H9c2 rat cardiomyoblasts

Epigallocatechin-3-gallate (EGCG) has been documented for its beneficial effects protecting oxidative stress to cardiac cells. Previously, we have shown the EGCG-mediated cardiac protection by attenuating reactive oxygen species and cytosolic Ca2+ in cardiac cells during oxidative stress and myocardial ischemia. Here, we aimed to seek a deeper elucidation of the molecular anti-oxidative capabilities of EGCG in an H2O2-induced oxidative stress model of myocardial ischemia injury using H9c2 rat cardiomyoblasts

Solving The Flexible Job Shop Problem using Multi-Objective Optimizer with Solution Characteristic Extraction

It is difficult to find optimal scheduling solutions for abstract scheduling problems with mass parallel tasks on multiprocessors because they are NP-complete. In this paper, a solution searching strategy called solution characteristic extraction is proposed as a multi-objective optimizer for solving flexible job shop problems (FJSP). These problems are concerned with finishing assigned jobs with minimal critical machine workload, total workload, and completion times. A suitable job assignment must consider processor performance, job complexity, and job suitability for each individual processor simultaneously. To test the efficiency and robustness of the proposed method, the experiments will contain two groups of benchmarks; with, and without release time constraints. Each benchmark includes numbers of heterogeneous processors and different jobs for execution. The results indicate the proposed method can find more potential solutions, and outperform related methods