Evaluation of Viscosity Dependence of the Critical Meniscus Height with Optical Fiber Sensors

Conventional means of data extraction using optical fiber interrogators are not adequate for fast-paced detection of a target parameter. In this instance, the relationship between the critical meniscus heights (CMH) of several liquids to the extraction speed of a rod submerged in them, have been analyzed. A limitation of a previous interrogator used for the purpose had been light absorption by the liquid due to the used bandwidth of the readily-available light source, i.e., C-band. The newly proposed technique addresses this limitation by utilizing a broadband light source instead, with a Si-photodetector and an Arduino. In addition, the Arduino is capable of extracting data at a relatively faster rate with respect to the conventional optical interrogator. The use of a different operational wavelength (850 nm instead of 1550 nm) increased the r2 and the sensitivity of the sensor. The new setup can measure surface chemistry properties, with the advantage of being comparatively cheaper than the conventionally available interrogator units, thereby providing a suitable alternative to conventional measurement techniques of liquid surface properties, while reducing material waste, i.e., in terms of the required volume for detection of a target parameter, through the use of optical fiber.

A Uniform Search for Nearby Planetary Companions to Hot Jupiters in TESS Data Reveals Hot Jupiters Are Still Lonely

We present the results of a uniform search for additional planets around all stars with confirmed hot Jupiters observed by the Transiting Exoplanet Survey Satellite (TESS) in its Cycle 1 survey of the southern ecliptic hemisphere. Our search comprises 184 total planetary systems with confirmed hot Jupiters with R p > 8 R ⊕ and orbital period <10 days. The Transit Least Squares algorithm was utilized to search for periodic signals that may have been missed by other planet search pipelines. While we recovered 169 of these confirmed hot Jupiters, our search yielded no new statistically validated planetary candidates in the parameter space searched (P < 14 days). A lack of planet candidates nearby hot Jupiters in the TESS data supports results from previous transit searches of each individual system, now down to the photometric precision of TESS. This is consistent with expectations from a high-eccentricity migration formation scenario, but additional formation indicators are needed for definitive confirmation. We injected transit signals into the light curves of the hot Jupiter sample to probe the pipeline’s sensitivity to the target parameter space, finding a dependence proportional to R p 2.32 P − 0.88 for planets within 0.3 ≤ R p ≤ 4 R ⊕ and 1 ≤ P ≤ 14 days. A statistical analysis accounting for this sensitivity provides a median and 90% confidence interval of 7.3 − 7.3 + 15.2 % for the rate of hot Jupiters with nearby companions in this target parameter space. This study demonstrates how TESS uniquely enables comprehensive searches for nearby planetary companions to nearly all the known hot Jupiters.

Improved fingerprinting performance in indoor positioning by reducing duration of the training phase process

<span>Wireless <span>sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed target parameter (FTP) and moving target parameter (MTP). MTP took 5 seconds faster than FTP in terms of the duration of RSSI data collection from each reference Node. </span></span>

Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method.

Comparative Exergy Analysis of Units for the Porous Ammonium Nitrate Granulation

The article deals with the study on the efficiency of units for porous ammonium nitrate production. The ways which increase the effective implementation of energy resources are determined by including the ejector recycling module, heat and mass exchangers that utilize principles of regenerative indirect evaporative cooling, and the sub-atmospheric inverse Brayton cycle. Mixed exergy analysis evaluates all flows of the system contour as those of the same value. The target parameter for determining the efficiency of both systems is the ratio of the unit’s productivity to the exergy expenditures to produce the unit mass of the product. As a result, it is found that the mentioned devices and units enable to increase the efficiency of the basic scheme by 87%.

Statistical Learning with a Nuisance Component (Extended Abstract)

We provide excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate a target parameter depends on an unknown parameter that must be estimated from data (a "nuisance parameter"). We analyze a two-stage sample splitting meta-algorithm that takes as input two arbitrary estimation algorithms: one for the target parameter and one for the nuisance parameter. We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the nuisance estimation error on the excess risk bound achieved by the meta-algorithm is of second order. Our theorem is agnostic to the particular algorithms used for the target and nuisance and only makes an assumption on their individual performance. This enables the use of a plethora of existing results from statistical learning and machine learning literature to give new guarantees for learning with a nuisance component. Moreover, by focusing on excess risk rather than parameter estimation, we can give guarantees under weaker assumptions than in previous works and accommodate the case where the target parameter belongs to a complex nonparametric class. We characterize conditions on the metric entropy such that oracle rates---rates of the same order as if we knew the nuisance parameter---are achieved. We also analyze the rates achieved by specific estimation algorithms such as variance-penalized empirical risk minimization, neural network estimation and sparse high-dimensional linear model estimation. We highlight the applicability of our results in four settings of central importance in the literature: 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.