Wireless Remote Weather Monitoring System Based on MEMS Technologies

This study proposes a wireless remote weather monitoring system based on Micro-Electro-Mechanical Systems (MEMS) and wireless sensor network (WSN) technologies comprising sensors for the measurement of temperature, humidity, pressure, wind speed and direction, integrated on a single chip. The sensing signals are transmitted between the Octopus II-A sensor nodes using WSN technology, following amplification and analog/digital conversion (ADC). Experimental results show that the resistance of the micro temperature sensor increases linearly with input temperature, with an average TCR (temperature coefficient of resistance) value of 8.2 × 10−4 (°C−1). The resistance of the pressure sensor also increases linearly with air pressure, with an average sensitivity value of 3.5 × 10−2 (Ω/kPa). The sensitivity to humidity increases with ambient temperature due to the effect of temperature on the dielectric constant, which was determined to be 16.9, 21.4, 27.0, and 38.2 (pF/%RH) at 27 °C, 30 °C, 40 °C, and 50 °C, respectively. The velocity of airflow is obtained by summing the variations in resistor response as airflow passed over the sensors providing sensitivity of 4.2 × 10−2, 9.2 × 10−2, 9.7 × 10−2 (Ω/ms−1) with power consumption by the heating resistor of 0.2, 0.3, and 0.5 W, respectively. The passage of air across the surface of the flow sensors prompts variations in temperature among each of the sensing resistors. Evaluating these variations in resistance caused by the temperature change enables the measurement of wind direction

Diffusion Model-Augmented Behavioral Cloning

Imitation learning addresses the challenge of learning by observing an expert's demonstrations without access to reward signals from environments. Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e.g., behavioral cloning, BC) or the joint probability p(s, a) (e.g., implicit behavioral cloning). Despite its simplicity, modeling the conditional probability with BC usually struggles with generalization. While modeling the joint probability can lead to improved generalization performance, the inference procedure can be time-consuming and it often suffers from manifold overfitting. This work proposes an imitation learning framework that benefits from modeling both the conditional and joint probability of the expert distribution. Our proposed diffusion model-augmented behavioral cloning (DBC) employs a diffusion model trained to model expert behaviors and learns a policy to optimize both the BC loss (conditional) and our proposed diffusion model loss (joint). DBC outperforms baselines in various continuous control tasks in navigation, robot arm manipulation, dexterous manipulation, and locomotion. We design additional experiments to verify the limitations of modeling either the conditional probability or the joint probability of the expert distribution as well as compare different generative models

Spatiotemporal analysis of air pollution and asthma patient visits in Taipei, Taiwan

[[abstract]]Background: Buffer analyses have shown that air pollution is associated with an increased incidence of asthma, but little is known about how air pollutants affect health outside a defined buffer. The aim of this study was to better understand how air pollutants affect asthma patient visits in a metropolitan area. The study used an integrated spatial and temporal approach that included the Kriging method and the Generalized Additive Model (GAM). Results: We analyzed daily outpatient and emergency visit data from the Taiwan Bureau of National Health Insurance and air pollution data from the Taiwan Environmental Protection Administration during 2000-2002. In general, children (aged 0-15 years) had the highest number of total asthma visits. Seasonal changes of PM10, NO2, O3 and SO2 were evident. However, SO2 showed a positive correlation with the dew point (r = 0.17, p < 0.01) and temperature (r = 0.22, p < 0.01). Among the four pollutants studied, the elevation of NO2 concentration had the highest impact on asthma outpatient visits on the day that a 10% increase of concentration caused the asthma outpatient visit rate to increase by 0.30% (95% CI: 0.16%??.45%) in the four pollutant model. For emergency visits, the elevation of PM10 concentration, which occurred two days before the visits, had the most significant influence on this type of patient visit with an increase of 0.14% (95% CI: 0.01%??.28%) in the four pollutants model. The impact on the emergency visit rate was non-significant two days following exposure to the other three air pollutants. Conclusion: This preliminary study demonstrates the feasibility of an integrated spatial and temporal approach to assess the impact of air pollution on asthma patient visits. The results of this study provide a better understanding of the correlation of air pollution with asthma patient visits and demonstrate that NO2 and PM10 might have a positive impact on outpatient and emergency settings respectively. Future research is required to validate robust spatiotemporal patterns and trends

Konsep Demokrasi Politik Dalam Islam

Coexistence of chronic rhinosinusitis (CRS) with asthma appears to impair asthma control. Type-2 innate lymphoid cells (ILC2s) respond to the cytokines of thymic stromal lymphopoietin (TSLP), interleukin (IL)-25 and IL-33, thus contributing to airway diseases such as CRS and asthma. We investigate whether the augmented Th2-cytokines in CRS might be related to sinonasal tract ILC2s corresponding to enhanced IL-25, IL-33 and TSLP release in severe asthmatics, and be involved in asthma control. Twenty-eight asthmatics (12 non-severe and 16 severe) with CRS receiving nasal surgery were enrolled. The predicted FEV1 inversely associated with CRS severity of CT or endoscopy scores. Higher expression of Th2-driven cytokines (IL-4, IL-5, IL-9, and IL-13), TSLP, IL-25 and IL-33 in nasal tissues was observed in severe asthma. Severe asthmatics had higher ILC2 cell counts in their nasal tissues. ILC2 counts were positively correlated with Th2-cytokines. Nasal surgery significantly improved asthma control and lung function decline in severe asthma and CRS. The higher expression of IL-33/ILC2 axis-directed type 2 immune responses in nasal tissue of CRS brought the greater decline of lung function in severe asthma. ILC2-induced the upregulated activity of Th2-related cytokines in asthmatics with CRS may contribute to a recalcitrant status of asthma control

Quantitative analysis of nanoparticle internalization in mammalian cells by high resolution X-ray microscopy

<p>Abstract</p> <p>Background</p> <p>Quantitative analysis of nanoparticle uptake at the cellular level is critical to nanomedicine procedures. In particular, it is required for a realistic evaluation of their effects. Unfortunately, quantitative measurements of nanoparticle uptake still pose a formidable technical challenge. We present here a method to tackle this problem and analyze the number of metal nanoparticles present in different types of cells. The method relies on high-lateral-resolution (better than 30 nm) transmission x-ray microimages with both absorption contrast and phase contrast -- including two-dimensional (2D) projection images and three-dimensional (3D) tomographic reconstructions that directly show the nanoparticles.</p> <p>Results</p> <p>Practical tests were successfully conducted on bare and polyethylene glycol (PEG) coated gold nanoparticles obtained by x-ray irradiation. Using two different cell lines, EMT and HeLa, we obtained the number of nanoparticle clusters uptaken by each cell and the cluster size. Furthermore, the analysis revealed interesting differences between 2D and 3D cultured cells as well as between 2D and 3D data for the same 3D specimen.</p> <p>Conclusions</p> <p>We demonstrated the feasibility and effectiveness of our method, proving that it is accurate enough to measure the nanoparticle uptake differences between cells as well as the sizes of the formed nanoparticle clusters. The differences between 2D and 3D cultures and 2D and 3D images stress the importance of the 3D analysis which is made possible by our approach.</p