Terrain-based adaption of propagation model loss parameters using non-linear square regression

Reliable and real-time propagation loss modeling play a significant role in the efficient planning, development, and optimization of macrocellular communication networks in a given terrain. Thus, the need to adapt or tune an existing model to enhance its signal prediction accuracy in a specified terrain becomes imperative. In this paper, we proposed and applied a non-linear square regression method based on the Levenberg-Marquart (LM) algorithm to adapt and improve the empirical propagation loss estimation accuracy of the Egli model for two major cities in Nigeria. A comprehensive propagation loss measurement acquired over Long Term Evolution (LTE) mobile broadband networks operating at 2630 MHz for four different cities was collected using TEMS investigation tools to achieve the Egli model adaption. Results indicate that the adapted Egli model displays a high estimation accuracy over the Gauss-Newton (GN) algorithm leveraging the non-linear regression method employed to benchmark the propagation loss estimation. Using six standard statistical indicators, the adapted Egli model displayed lower estimation errors than the classical Egli model across the tested locations in the two cities investigated. Finally, the LM-adapted Egli model was compared with extensive measurements from another eNodeB in Port Harcourt different from the initial four eNodeBs investigated. The results indicate that the adapted model is suitable for deployment in related macrocellular environments.

Volatility Factors of Returns and Risk Analysis Using Quantile Risk Measures in the Gold and Silver Market

The aim of the paper is to identify unobservable factors that may significantly determine the level of gold and silver returns and to assess the risk of investment in these metals. To measure risk, the value at risk and other, less popular measures are used: the ES, MS, Rachev ratio and GlueVaR risk measure. Normal and Student’s t‑distributions are used as theoretical distributions. The results of the study show that we can identify latent factors based on observable variables that have a significant impact on the level of gold and silver returns. In addition, it was observed that the risk measures would vary depending on the period of research. It was shown that the estimates of the risk measures using Student’s t‑distribution have a lower estimation error than those based on the normal distribution.

Assessing Nitrogen Availability in Biobased Fertilizers: Effect of Vegetation on Mineralization Patterns

Biobased nitrogen (N) fertilizers derived from animal manure can substitute synthetic mineral N fertilizer and contribute to more sustainable agriculture. Practitioners need to obtain a reliable estimation of the biobased fertilizers’ N value. This study compared the estimates for pig slurry (PS) and liquid fraction of digestate (LFD) using laboratory incubation and plant-growing experiments. A no-N treatment was used as control and calcium ammonium nitrate (CAN) as synthetic mineral fertilizer. After 100 days of incubation, the addition of PS and LFD resulted in a net N mineralization rate of 10.6 ± 0.3% and 20.6 ± 0.4% of the total applied N, respectively. The addition of CAN showed no significant net mineralization or immobilization (net N release 96 ± 6%). In the pot experiment under vegetation, all fertilized treatments caused N immobilization with a negative net N mineralization rate of −51 ± 11%, −9 ± 4%, and −27 ± 10% of the total applied N in CAN, PS, and LFD treatments, respectively. Compared to the pot experiment, the laboratory incubation without vegetation may have overestimated the N value of biobased fertilizers. Vegetation resulted in a lower estimation of available N from fertilizers, probably due to intensified competition with soil microbes or increased N loss via denitrification.

Robust Cardinality: a novel approach for cardinality prediction in SQL queries

Background Database Management Systems (DBMSs) use declarative language to execute queries to stored data. The DBMS defines how data will be processed and ultimately retrieved. Therefore, it must choose the best option from the different possibilities based on an estimation process. The optimization process uses estimated cardinalities to make optimization decisions, such as choosing predicate order. Methods In this paper, we propose Robust Cardinality, an approach to calculate cardinality estimates of query operations to guide the execution engine of the DBMSs to choose the best possible form or at least avoid the worst one. By using machine learning, instead of the current histogram heuristics, it is possible to improve these estimates; hence, leading to more efficient query execution. Results We perform experimental tests using PostgreSQL, comparing both estimators and a modern technique proposed in the literature. With Robust Cardinality, a lower estimation error of a batch of queries was obtained and PostgreSQL executed these queries more efficiently than when using the default estimator. We observed a 3% reduction in execution time after reducing 4 times the query estimation error. Conclusions From the results, it is possible to conclude that this new approach results in improvements in query processing in DBMSs, especially in the generation of cardinality estimates.

An Improved Variable Kernel Density Estimator Based on L2 Regularization

The nature of the kernel density estimator (KDE) is to find the underlying probability density function (p.d.f) for a given dataset. The key to training the KDE is to determine the optimal bandwidth or Parzen window. All the data points share a fixed bandwidth (scalar for univariate KDE and vector for multivariate KDE) in the fixed KDE (FKDE). In this paper, we propose an improved variable KDE (IVKDE) which determines the optimal bandwidth for each data point in the given dataset based on the integrated squared error (ISE) criterion with the L2 regularization term. An effective optimization algorithm is developed to solve the improved objective function. We compare the estimation performance of IVKDE with FKDE and VKDE based on ISE criterion without L2 regularization on four univariate and four multivariate probability distributions. The experimental results show that IVKDE obtains lower estimation errors and thus demonstrate the effectiveness of IVKDE.

Thermal Properties and Combustion-Related Problems Prediction of Agricultural Crop Residues

The prediction and pre-evaluation of the thermal properties and combustion-related problems (e.g., emissions and ash-related problems) are critical to reducing emissions and improving combustion efficiency during the agricultural crop residues combustion process. This study integrated the higher heating value (HHV) model, specific heat model, and fuel indices as a new systematic approach to characterize the agricultural crop residues. Sixteen linear and non-linear regression models were developed from three main compositions of the ultimate analysis (e.g., C, H, and O) to predict the HHV of the agricultural crop residues. Newly developed HHV models have been validated with lower estimation errors and a higher degree of accuracy than the existing models. The specific heat of flue gas during the combustion process was estimated from the concentrations of C, H, O, S, and ash content under various excess air (EA) ratios and flue gas temperatures. The specific heat of agricultural crop residues was between 1.033 to 1.327 kJ/kg·K, while it was increased by decreasing the EA ratios and elevating the temperature of the flue gas. Combustion-related problems, namely corrosions, PM1.0 emissions, SOx, HCl, and ash-related problems were predicted using the fuel indices along with S and Cl concentrations, and ash compositions. Results showed that agricultural crop residues pose a severe corrosion risk and lower ash sintering temperature. This integrated approach can be applied to a wide range of biomass before the actual combustion process which may predict thermal-chemical properties and reduce the potential combustion-related emissions.

Introducing Parameter Clustering to the OED Procedure for Model Calibration of a Synthetic Inducible Promoter in S. cerevisiae

In recent years, synthetic gene circuits for adding new cell features have become one of the most powerful tools in biological and pharmaceutical research and development. However, because of the inherent non-linearity and noisy experimental data, the experiment-based model calibration of these synthetic parts is perceived as a laborious and time-consuming procedure. Although the optimal experimental design (OED) based on the Fisher information matrix (FIM) has been proved to be an effective means to improve the calibration efficiency, the required calculation increases dramatically with the model size (parameter number). To reduce the OED complexity without losing the calibration accuracy, this paper proposes two OED approaches with different parameter clustering methods and validates the accuracy of calibrated models with in-silico experiments. A model of an inducible synthetic promoter in S. cerevisiae is adopted for bench-marking. The comparison with the traditional off-line OED approach suggests that the OED approaches with both of the clustering methods significantly reduce the complexity of OED problems (for at least 49.0%), while slightly improving the calibration accuracy (11.8% and 19.6% lower estimation error in average for FIM-based and sensitivity-based approaches). This study implicates that for calibrating non-linear models of biological pathways, cluster-based OED could be a beneficial approach to improve the efficiency of optimal experimental design.

Use of Genetic Programming for the Estimation of CODLAG Propulsion System Parameters

In this paper, the publicly available dataset for the Combined Diesel-Electric and Gas (CODLAG) propulsion system was used to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque using genetic programming (GP) algorithm. The dataset consists of 11,934 samples that were divided into training and testing portions in an 80:20 ratio. The training portion of the dataset which consisted of 9548 samples was used to train the GP algorithm to obtain symbolic expressions for estimation of fuel flow, ship speed, starboard propeller, port propeller, and total propeller torque, respectively. After the symbolic expressions were obtained the testing portion of the dataset which consisted of 2386 samples was used to measure estimation performance in terms of coefficient of correlation (R2) and Mean Absolute Error (MAE) metric, respectively. Based on the estimation performance in each case three best symbolic expressions were selected with and without decay state coefficients. From the conducted investigation, the highest R2 and lowest MAE values were achieved with symbolic expressions for the estimation of fuel flow, ship speed, starboard propeller torque, port propeller torque, and total propeller torque without decay state coefficients while symbolic expressions with decay state coefficients have slightly lower estimation performance.

Validation of Image Qualities of a Novel Four-Mice Bed PET System as an Oncological and Neurological Analysis Tool

Background: Micro-positron emission tomography (micro-PET), a small-animal dedicated PET system, is used in biomedical studies and has the quantitative imaging capabilities of radiotracers. A single-bed system, commonly used in micro-PET, is laborious to use in large-scale studies. Here, we evaluated the image qualities of a multi-bed system. Methods: Phantom imaging studies were performed to assess the recovery coefficients (RCs), uniformity, and spill-over ratios (SORs) in water- and air-filled chambers. 18F-FDG and 18F-FPEB PET images of xenograft and normal mice from the multi-bed and single-bed systems were compared. Results: For small diameters (< 3 mm), the RC values between the two systems differed significantly. However, for large diameters (> 4 mm), there were no differences in RC values between the two systems. Uniformity and SORs of both systems were within the tolerance limit of 15%. In the oncological study, the estimation of 18F-FDG uptake in the tumor was significantly lower in the multi-bed system than that in the single-bed system. However, 18F-FDG PET in xenograft mice with tumor size > 4 mm revealed the variation between subjects within the multi-bed system group to be less than 12%. In the neurological study, SUV for the multi-bed group was 25–26% lower than that for the single-bed group; however, inter-object variations within the multi-bed system were below 7%. Conclusions: Although the multi-bed system showed lower estimation of radiotracer uptake than that of the single-bed system, the inter-subject variations were within acceptable limits. Our results indicate that the multi-bed system can be used in oncological and neurological studies.

Small area estimation and childhood obesity surveillance using electronic health records

There is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5–17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007–2012). The prevalence estimates were calculated by aggregating the estimated probability of each individual being obese, which was obtained via a generalized linear mixed model. We incorporated the random effects at the area level into our model. A weighted procedure was employed to account for missingness in EHR data. A non-parametric kernel smoothing method was used to obtain the prevalence estimates for locations with no or little data (<20 individuals) from the EHR. These estimates were compared to results from newly available obesity atlas (2015–2016) developed from various EHRs with greater statewide representation. The mean of the zip code level obesity prevalence estimates for males and females aged 5–17 years is 16.2% (SD 2.72%); 17.9% (SD 2.14%) for males and 14.4% (SD 2.00%) for females. The results were comparable to the Wisconsin Health Atlas (WHA) estimates, a much larger dataset of local community EHRs in Wisconsin. On average, prevalence estimates were 2.12% lower in this process than the WHA estimates, with lower estimation occurring more frequently for zip codes without data in PHINEX. Using this approach, we can obtain estimates for local areas that lack EHRs data. Generally, lower prevalence estimates were produced for those locations not represented in the PHINEX database when compared to WHA estimates. This underscores the need to ensure that the reference EHRs database can be made sufficiently similar to the geographic areas where synthetic estimates are being created.