Not A Number Research Articles

Youth Attitudes Toward Commercial Sexual Exploitation and Help-Seeking: An Analysis of Data Collected by the Not a Number Trafficking Prevention Program

This study involved secondary data analysis of baseline and post-program survey data collected from youth ages 10–21, who participated in in the Not a Number (NAN) trafficking prevention program across 24 states (N = 10,915). Analyses examined differences in baseline awareness of CSE dynamics and help-seeking intention across youth subgroups and measured changes pre- and post-program delivery. Older youth knew more about trafficking but reported less intention to seek help from different resources in exploitive situations. Gender minority youth reported lower rates of help-seeking intention compared to other youth. Post-program outcomes were significantly different from baseline across all outcomes.

Association rule mining of aircraft event causes based on the Apriori algorithm

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events.

Enhancing the Accuracy and Predictability of the Oxy Field Optimizer for Dynamic Steam Allocation in the Mukhaizna Steamflood Field

Summary The main challenge for the Mukhaizna steamflood field is to allocate steam dynamically throughout the entire field, which consists of more than 3,200 wells, to obtain the most attractive reservoir performance forecast. To address this challenge, Occidental has developed a state-of-the-art closed-loop optimization solution called the Oxy Field Optimizer (OFO). The aim of this study is to enhance the accuracy, robustness, and predictability of the OFO. Recent advances include connection design, simulation stability, history-matching workflow, model predictability (blind test), and the optimizer. To improve the proxy simulator, 2D connections between wells were introduced and various strategies to handle convergence issues were implemented. The history-matching workflow has been enhanced by automating the temperature match, multistep saturation tuning, and relative permeability tuning. The results show that the implementation of gridblock material balance check, well equation check, and Not a Number (NaN) value check after line search solved multiple convergence problems. The automated temperature match process is five times faster compared with the manual process, and the automated relative permeability tuning decreased average oil mismatch by 55%. The optimizer now utilizes a parallel implementation of a novel ensemble-based optimization scheme (EnOpt) algorithm, which is twice as fast as the original implementation. These proven advances make OFO an essential tool for obtaining optimal steam allocations.

GA-CNN: Analyzing student’s cognitive skills with EEG data using a hybrid deep learning approach

A cognitive state (CS) assessment can be effectively performed using Electroencephalogram (EEG). However, due to the curse of dimensionality issues of EEG, most of the clustering methods often lead to poor performance. Deep neural network-based representation learning transforms high-dimensional data into lower-dimensional feature space, increasing the performance of CS prediction in students. So, in this research, a novel graph-based conventional attention neural network (GA-CNN) is developed to reduce distribution differences while analyzing student performance. To perform this classification, the obtained EEG signal dataset is denoised with the help of Stationary Wavelet Transform (SWT) based Independent Component Analysis (ICA) that filters out the noise signals. Likewise, the behavioural dataset is pre-processed using zero normalization and Not a Number (NaN) value computation method. Then, the effective features are extracted from the pre-processed data using the Long-Short-Term-Memory (LSTM) technique. Finally, the GA-CNN model is initiated to classify the students’ cognitive state (CS). The proposed method is implemented using the MATLAB tool, and the performance of the expected GA-CNN model is compared to other approaches where the analysis is done using the benchmark Kaggle EEG data set. The classification accuracy is significantly improved compared to other methods. The model achieves 87% accuracy, 98% precision, 75% recall, and 85% F1 score, outperforming various methods and making a better compromise.

Peak deconvolution with significant noise suppression and stability using a facile numerical approach in Fourier space

The deconvolution problem for resolution and feature enhancement of analytical signals is known to be a noise-enhancing procedure to the extent that some authors have called it “violently unstable.” The sharpened result is frequently buried in high-amplitude noise in direct deconvolution using the Fourier division method. With a Gaussian deconvolving function, one can get a “Not-a-Number (NaN) error” due to division by near zero (complex) numbers in Fourier space. Herein, a simple numerical method that stabilizes the calculation of direct Fourier deconvolution and ensures that the noise is controlled is proposed. In this calculation, a small positive constant or symmetric positive peak function is added to the denominator during division in Fourier space, resulting in stability and high-frequency noise suppression. The key advantages of this non-iterative method are speed, numerical stability during division, noise suppression, peak area and location invariance, and minimization of the edge effects after deconvolution. Also, ringing effects in the baseline and negative overshoots are significantly mitigated using the modified denominator method. The numerical approach can be applied to enhance the analytical signals consisting of peaks, e.g., in NMR, IR, Raman spectroscopy, or separation sciences. Both symmetric and one-sided (asymmetric) broadening functions can be used for deconvolution in this method. Applications are also demonstrated for various test functions (square pulse, Voigt, or highly overlapped Gaussian peaks) with clear advantages with respect to the standard Fourier division method. The new denominator addition method is compared with two constrained deconvolution procedures in matrix formalism and Fourier domain.

Mixture Density Network Based on Truncated Distribution and Genetic Algorithm For Wind Power Forecasting

Due to the excellent wind power probabilistic prediction performance, Mixture Density Network (MDN) is used in short-term wind power forecasting, but the density leakage problem the Not a Number (NaN) loss problem and the choice of hyperparameters in the MDN seriously affect the model performance. GA-TDMDN is proposed in this paper for wind power probabilistic forecasting. GA-TDMDN uses truncated distribution as kernel function to solve density leakage. For the NaN loss problem that occurs during model training, different output layer activation methods and improved loss function are used for different mixture component parameters, so that the shape of the truncated normal distribution can be better controlled. Genetic Algorithms (GA) is used to optimize key hyperparameters in the MDN structure. The experimental results show that it is feasible to use truncated distribution to solve the density leakage problem, and using the GA algorithm to optimize the model structure can improve the model performance

Aggregation Operators with Undefined Inputs or Outputs

We consider a generalization of aggregation and fusion functions when some of their inputs or outputs are undefined. For simplicity, we represent undefined values by a single NaN (not-a-number) value. We define four main methods of treating undefined values, observe some of their properties, and compare them with several mainstream implementations and norms for handling NaN values. Finally, as a case study demonstrating the apparatus's usefulness, the formalism is applied to the discrete fuzzy transform technique. The formalism allows us to prove a generalized approximation theorem for fuzzy transforms, which removes previously required restrictions on fuzzy partitions for sparse input data.

Partnering for Human Trafficking Prevention: Implementing Love146's Not a Number Curriculum through Minnesota's Safe Harbor Program

The Minnesota Department of Health (MDH) Safe Harbor Program promotes Love146’s Not a Number (NAN) human trafficking for youth. This collaboration reaches youth in a variety of settings through a multidirectional format that integrates issues familiar to young people while increasing their knowledge and skill for navigating situations where trafficking or exploitation is a risk or occurring. Safe Harbor prefers NAN’s curriculum because it is tailored to youth of all genders, is updated to reflect the lived experiences of youth, allows for cultural specificity, includes labor trafficking, and requires program fidelity. The partnership supports Minnesota’s public health, population-level strategies for preventing human trafficking and exploitation (sex and labor). NAN is also a means for responding to youth needs identified by the Minnesota Student Survey and other data to improve health, safety, and identification of protective factors.

Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads

Residential electricity load data can include numerous types of bad data, even clustered bad data, as they that are typically captured by simple measurement instruments. For example, in the case of a time-series of Not-a-Number (NaN) errors, the values before or next to a NaN may appear as the sum of actual values during the times of the NaN series. To utilize load data that includes such erroneous data for prediction or data mining analysis, customized detection and imputation should be conducted. This study proposes a new joint detection and imputation method for handling clustered bad data in residential electricity loads. Examples of these data are known invalid data points, such as consecutive NaN or zero values followed by or being ahead of an outlier. The proposed joint detection and imputation scheme first investigates the neighbors of the invalid data points, using probabilistic forecasting techniques. These techniques are implemented by the next valid neighbors to determine whether there is an anomaly or not. Then, adaptive imputations are applied on the basis of the detection, the candidate point should be imputed simultaneously or not. To assess the potential of the newly proposed scheme to characterize the clustered bad data, we analyzed the electricity loads of 354 households. Moreover, joint detection and imputations are conducted to test with the randomly injected synthesized clustered bad data (containing NaNs of various lengths) that is followed by the summation of the actual NaN values. The proposed scheme succeeded in detecting clustered bad data with an accuracy of 95.5% and a false alarm rate of 3.6% for all households in the dataset. Outlier detection-assisted imputation schemes are evaluated for NaNs with optional outliers. Results demonstrate that these schemes improve the overall accuracy significantly compared to schemes without outlier detection.

A fast and high frame rate adaptive beamforming using DCT‐based RF‐line recovery in line‐by‐line ultrasound imaging

AbstractUltrasound imaging is an important modality used in medical imaging. One of the significant stages in the ultrasound imaging is the beamforming process. This article proposes a new technique for reducing the overall computational time of adaptive linear ultrasound imaging. The method uses the discrete cosine transform‐based reconstruction for missing data imputation. The novelty of the paper is that we do not need to beam‐form the total scan lines, so the time of image construction can be saved significantly. In other words, a fraction of the total scan lines is selected for beamforming and the others are assumed to have values as Not‐a‐Number (NaN). The proposed reconstruction technique tries to assign appropriate values to the NaN ones. We applied the proposed method to the simulated and experimental radio frequency (RF) datasets for resolution and contrast evaluation. Results showed that the proposed technique is near to the minimum variance (MV) method in terms of resolution and contrast, and has less computational time for image formation compared to the MV. As some quantitative examples in some experiments we have formed only 50% and 33% of the total lines and reconstructed the rest, then we have been able to increase the frame rate twice and three times, respectively, which can be very useful in many applications, especially in echocardiography imaging. In addition, since the execution time of the reconstruction algorithm is not very significant, we were also able to increase the speed by two and three times while achieving an error of less than 10% compared to the case of using all image lines.

Improved Deep Mixture Density Network for Regional Wind Power Probabilistic Forecasting

Unsteady motion of the atmosphere incurs nonlinear and spatiotemporally coupled uncertainties in the wind power prediction (WPP) of multiple wind farms. This brings both opportunities and challenges to wind power probabilistic forecasting (WPPF) of a wind farm cluster or region, particularly when wind power is highly penetrated within the power system. This paper proposes an Improved Deep Mixture Density Network (IDMDN) for short-term WPPF of multiple wind farms and the entire region. In this respect, a deep multi-to-multi (m2m) mapping Neural Network model, which adopts the beta kernel as the mixture component to avoid the density leakage problem, is established to produce probabilistic forecasts in an end-to-end manner. A novel modified activation function and several general training procedures are then introduced to overcome the unstable behavior and NaN (Not a Number) loss issues of the beta kernel function. Verification of IDMDN is based on an open-source dataset collected from seven wind farms, and comparison results show that the proposed model improves the WPPF performance at both wind farm and regional levels. Furthermore, a laconic and accurate probabilistic expression of predicted power at each time step is produced by the proposed model.

An area efficient multi-mode quadruple precision floating point adder

Most of the scientific and engineering applications require accurate computations. Double precision floating point computations are not enough for many applications like climate modelling, computational physics, etc. Efficient design of quadruple precision floating point adder is needed for these applications. The proposed multi-mode quadruple precision floating point adder architecture supports four single precision operations in parallel, as well as two double precision operations in parallel and also supports one quadruple precision operation. Compared to existing Quadruple precision floating point adders and Dual mode Quadruple precision floating point adder, the proposed architecture can perform more computations with less area because of resource sharing among different precision operands. The proposed Multi-mode quadruple precision adder supports both normal and subnormal operations and also the exceptional case handling such as infinity, Not a Number (NaN) and zero cases. The proposed adder has been designed and implemented in both ASIC and FPGA. During ASIC implementation with 90nm technology using the synopsis tool, the proposed Multi-mode quadruple precision floating point adder has a 38.57% smaller area compared to the existing quadruple precision floating point adder. Similarly, the proposed design reduces the area by 29.28% and 35.68% when implemented on Virtex 4 and Virtex 5 FPGAs respectively.

Technical Note: Deformable image registration on partially matched images for radiotherapy applications

In radiation therapy applications, deformable image registrations (DIRs) are often carried out between two images that only partially match. Image mismatching could present as superior-inferior coverage differences, field-of-view (FOV) cutoffs, or motion crossing the image boundaries. In this study, the authors propose a method to improve the existing DIR algorithms so that DIR can be carried out in such situations. The basic idea is to extend the image volumes and define the extension voxels (outside the FOV or outside the original image volume) as NaN (not-a-number) values that are transparent to all floating-point computations in the DIR algorithms. Registrations are then carried out with one additional rule that NaN voxels can match any voxels. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. This method makes it possible to perform DIR on partially matched images that otherwise are difficult to register. It may also improve DIR accuracy, especially near or in the mismatched image regions.

SU-FF-I-101: Inverse Consistency Deformable Image Registration On Partially Matched Images

Purpose: For many RT applications, deformable image registrations (DIR) are often carried out between two images that only match partially. In addition, it is often desirable to use inverse consistency DIR to improve accuracy and to compute both the forward and backward deformation vector fields (DVF) simultaneously and consistently. Inverse consistency is especially important for adaptive radiotherapy applications because doses and structures need to be remapped between the planning image and the daily image using both forward and backward DVFs. Image mismatching (superior‐inferior coverage difference, field‐of‐view (FOV) cutoff, etc) makes it difficult for inverse consistency DIR algorithms which normally need both images to have matched image content and dimensions. Therefore, we propose a method in this study to perform inverse consistency DIR for these image mismatch situations. Method: Our method is based on the inverse consistency optical flow algorithm (Yang, PMB, 2008). The idea of allowing image mismatching is to extend the image volumes and define the extension voxel (outside the original image volume) values as NaN (not‐a‐number). NaN values are transparent to all floating‐point arithmetic computations in the DIR computation, including re‐sampling, interpolation, etc. With the NaN voxels, registrations are carried out with one additional rule that a NaN voxel can match any voxel. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. Results: We demonstrate the proposed method with Tomotherapy MVCT images with FOV cutoff problems and smaller volumetric coverage. Our results suggest that the proposed method could significantly improve image registration accuracy, especially near or in the mismatched image regions. Conclusion: This proposed method makes it possible to obtain inverse consistent DVFs on partially matched images that are usually difficult to register otherwise.

On collapsing the [formula omitted] average number of neutrons released per fission from the IAEA.LIB library with the WIMSD-5b code

It was verified after a fuel burnup calculation with the WIMSD-5b code using the IAEA.LIB library that the computed average number of neutrons released per fission of Pu 94 242 shows up as a Not-a-Number (NaN) for some energy groups. As this problem does not permit the use of the generated multigroup microscopic cross sections by a reactor calculation code, the value of 1.0E−38 barns was attributed to all energy groups of the IAEA.LIB library that have null values of multigroup microscopic fission cross sections for this material.