Titanium Dioxide Nanoparticle Humidity Microsensors Integrated with Circuitry on-a-Chip

A humidity microsensor integrated with a readout circuit on-a-chip fabricated using the commercial 0.18 μm CMOS (complementary metal oxide semiconductor) process was presented. The integrated sensor chip consists of a humidity sensor and a readout circuit. The humidity sensor is composed of a sensitive film and interdigitated electrodes. The sensitive film is titanium dioxide prepared by the sol-gel method. The titanium dioxide is coated on the interdigitated electrodes. The humidity sensor requires a post-process to remove the sacrificial layer and to coat the titanium dioxide. The resistance of the sensor changes as the sensitive film absorbs or desorbs vapor. The readout circuit is employed to convert the resistance variation of the sensor into the output voltage. The experimental results show that the integrated humidity sensor has a sensitivity of 4.5 mV/RH% (relative humidity) at room temperature

Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. In a first step, treatment propensity scores given the mediator and observed covariates or given covariates alone are estimated by nonparametric series logit estimation. In a second step, they are used to reweigh observations in order to estimate the effects of interest. We establish root-n consistency and asymptotic normality of this approach as well as a weighted version thereof. The latter allows evaluating effects on specific subgroups like the treated, for which we derive the asymptotic properties under estimated propensity scores. We also provide a simulation study and an application to an information intervention about male circumcisions

Support Vector Machines Parameter Selection Based on Combined Taguchi Method and Staelin Method for E-mail Spam Filtering

Support vector machines (SVM) are a powerful tool for building good spam filtering models. However, the performance of the model depends on parameter selection. Parameter selection of SVM will affect classification performance seriously during training process. In this study, we use combined Taguchi method and Staelin method to optimize the SVM-based E-mail Spam Filtering model and promote spam filtering accuracy. We compare it with other parameters optimization methods, such as grid search. Six real-world mail data sets are selected to demonstrate the effectiveness and feasibility of the method. The results show that our proposed methods can find the effective model with high classification accuracy