Initial Probability Research Articles

Prevalence and Predictors of Waterpipe Smoking Initiation and Progression Among Adolescents and Young Adults in Waves 1-4 (2013-2018) of the Population Assessment of Tobacco and Health (PATH) Study.

Waterpipe tobacco (WPT) smoking has increased among the young population in the United States. This study assessed the extent and predictors of WPT smoking initiation and progression among US adolescents (12-17 years) and young adults (18-24 years) longitudinally. We analyzed data from 4 waves (2013-2018) of the Population Assessment of Tobacco and Health (PATH) study comprising 10 692 respondents (adolescents = 5428 and young adults = 5264). Kaplan-Meier survival method estimated probabilities of WPT initiation and progression. Cox proportional hazards regression models delineated predictors of the outcomes. Between 2013 and 2018, 4.8% of adolescents initiated and 10.6% progressed WPT smoking. Among young adults, 18.5% initiated and 14.1% progressed WPT smoking during the same time interval. Predictors among adolescents included, WPT initiation: Hispanic ethnicity (adjusted odds ratio (aHR) = 1.75, 95% confidence interval (CI) = 1.23-2.49), lower harm perception (aHR = 2.89, 95% CI = 2.10-3.98), and other tobacco products use (aHR = 3.97, 95% CI = 2.73-5.78); WPT progression: illicit drug use (aHR = 4.60, 95% CI = 1.99-10.67). Predictors among young adults included, WPT initiation: non-Hispanic Black (aHR = 2.31, 95% CI = 1.78-3.00), Hispanic (aHR = 1.77, 95% CI = 1.34-2.33), lower harm perception (aHR = 2.77, 95% CI = 2.19-3.50), and other tobacco products use (aHR = 3.14, 95% CI = 2.25-4.38); WPT progression: non-Hispanic Black (aHR = 1.51 95% CI = 1.09-2.10), lower harm perception (aHR = 1.80, 95% CI = 1.41-2.30), and alcohol use (aHR = 1.61, 95% CI = 1.13-2.30). Results indicate a high prevalence of WPT initiation and progression among adolescents and young adults over time, with minority racial/ethnic groups being at greater risk for both. WPT-specific risk communication interventions (eg, educational campaigns and health warning labels) are warranted to limit WPT smoking among young people.

Obtaining the long-term behavior of master equations with finite state space from the structure of the associated state transition network

The master equation describes the time evolution of the probabilities of a system with a discrete state space. This time evolution approaches for long times a stationary state that will in general depend on the initial probability distribution. Conditions under which the stationary state is unique are usually given as remarks appended to more comprehensive theories in the mathematical literature. We provide a direct and complete derivation of a necessary and sufficient criterion for when this steady state is unique. We translate this problem into the language of graph theory and show that there is a one-to-one correspondence between minimal absorbing sets within the state-transition network and linearly independent stationary states of the master equation.

Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy.

Simple SummaryLung cancer is the second most common cancer in men after prostate cancer and in women after breast cancer, but it is the leading cause of cancer death among both genders. This manuscript proposes a new computer-aided diagnosis system that uses only a single computed tomography scan to diagnose the pulmonary nodule as benign or malignant. This system helps in the early detection of the pulmonary nodules and shows its ability to identify the pulmonary nodules precisely.Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves , , and for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.

Agent‐based simulation of cross‐contamination of Escherichia coli O157:H7 on lettuce during processing and temperature fluctuations during storage in a produce facility. Part 2: Model implementation

AbstractAn agent‐based simulation was developed to represent the spatial and temporal Escherichia coli O157:H7 cross‐contamination dynamics in a processing facility for fresh‐cut romaine lettuces using NetLogo. An extension was added to the model to predict E. coli O157:H7 growth due to temperature abuses in a cold storage facility. A user‐friendly interface was created to follow variations in model outputs. The number of contaminated bags and lettuce contamination levels were computed and visualized. Experimental data of cross‐contamination from literature was used to describe the facility and validate the model. The key factor affecting cross‐contamination in the process is the free chlorine (FC) concentration dose rate. The number of contaminated bags per batch decreases nonlinearly as FC is added to the flume tank. The contamination level in the shredded lettuces increases linearly with initial microbial load or the initial contamination probability on lettuce heads. Both the level and probability of contamination in the facility's environment alter the number of contaminated bags. Batch size influences the number of contaminated bags when the first incoming lettuce batch is contaminated. The smaller the batch size the smaller number of contaminated bags. Storage room temperature fluctuations (13 and 19°C and 8 and 12°C) show the importance of real‐time monitoring to avoid pathogen growth and number of contaminated bags. This work provides insight on applications of agent‐based modeling approach using NetLogo to visualize cross‐contamination in fresh‐cut leafy green processing operations. It analyzes cross‐contamination and its impact on processing performance by studying the effect of mitigation strategies.Practical applicationsTo support ready‐to‐eat leafy greens postharvest operations, this work investigated the benefits of using visualization to develop strategies to prevent produce cross‐contamination along processing and the effect of temperature abuse during storage. Output from the model can be used in Quantitative Management Risk Assessment (QMRA) applications. By developing a simulation‐based decision support system, the safety of processing and storage steps of fresh‐cut leafy greens are investigated. Pathogen cross‐contamination and growth models are integrated to observe impacts of varying contamination levels in incoming produce and equipment surfaces, concentrations of free chlorine in the wash water, and storage temperatures on the number of contamination bags over time. Computational experiments study a pilot plant of Romaine lettuces contaminated with E. coli O157:H7. Results indicate that the key factor affecting cross‐contamination is the chlorination concentration dose rate in the wash water. Other factors include the level of contamination on the incoming lettuce heads or in the facility environment (equipment). Storage room temperature fluctuations showed the importance of real‐time monitoring to avoid microorganism growth and thus prevent an increase in the number of contaminated bags.

Hidden Markov Modeling with HMMTeacher.

Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user’s manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.

Abstract WP179: Impact Of Different Cardiac Rhythm Monitoring Strategies On Secondary Stroke Prevention: A Systematic Review And Network Meta-analysis Of Randomized Controlled Clinical Trials

Background and Purpose: Prolonged cardiac rhythm monitoring can reveal a substantial proportion of ischemic stroke (IS) patients with atrial fibrillation (AF). We sought to evaluate the potential utility of available prolonged cardiac rhythm monitoring strategies with respect to secondary stroke prevention. Methods: We searched Medline and Scopus databases to identify randomized controlled clinical trials (RCTs) comparing AF detection, anticoagulation initiation and stroke recurrence rates in patients with history of recent IS or transient ischemic attack (TIA) receiving cardiac rhythm monitoring with implantable loop recorders (ILRs), 30-days external loop recorders or Holter monitors. We performed a network meta-analysis to combine direct and indirect evidence for any given pair of monitoring devices that were evaluated within a trial and reported effect estimates with risk ratios (RRs) and corresponding 95% confidence intervals (95%CIs). Results: We identified 5 RCTs including a total of 2202 patients (mean age 68 years, 40% women). In indirect analyses the likelihood of AF detection and anticoagulation initiation was higher for both ILR (RR=8.48, 95%CI: 3.41, 21.06; RR=3.29, 95%CI: 1.70-6.39) and external loop recorders (RR=3.06, 95%CI: 1.66, 5.61; RR=1.63, 95%CI: 1.03-2.58) compared to Holter devices. The probability of AF detection and anticoagulation initiation was lower for Holter and external loop recorders compared to ILR devices (RR=0.36, 95%CI: 0.15, 0.85 and RR=0.50, 95%CI: 0.25-0.98, respectively). No difference in the risk of stroke recurrence was found in the indirect comparisons of different cardiac rhythm monitoring strategies. Conclusion: The likelihood of AF detection and anticoagulation initiation after an ischemic stroke or TIA is higher with ILRs compared to both external loop recorders and Holter devices.

Application of ant colony and immune combined optimization algorithm in path planning of unmanned craft

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.

Symmetry-Induced Emergence of a Pseudo-Qutrit in the Dipolar Coupling of Two Qubits.

We investigate a system of two identical and distinguishable spins 1/2, with a direct magnetic dipole–dipole interaction, in an external magnetic field. Constraining the hyperfine tensor to exhibit axial symmetry generates the notable symmetry properties of the corresponding Hamiltonian model. In fact, we show that the reduction of the anisotropy induces the invariance of the Hamiltonian in the subspace of the Hilbert space of the two spins in which invariably assumes its highest eigenvalue of 2. By means of appropriate mapping, it is then possible to choose initial density matrices of the two-spin system that evolve in such a way as to exactly simulate the time evolution of a pseudo-qutrit, in the sense that the the actual two-spin system nests the subdynamics of a qutrit regardless of the strength of the magnetic field. The occurrence of this dynamic similitude is investigated using two types of representation for the initial density matrix of the two spins. We show that the qutrit state emerges when the initial polarizations and probability vectors of the two spins are equal to each other. Further restrictions on the components of the probability vectors are reported and discussed.

Predication and Photon Statistics of a Three-Level System in the Photon Added Negative Binomial Distribution

Statistical and artificial neural network models are applied to forecast the quantum scheme of a three-level atomic system (3LAS) and field, initially following a photon added negative binomial distribution (PANBD). The Mandel parameter is used to detect the photon statistics of a radiation field. Explicit forms of the PANBD are given. The prediction of the Mandel parameter, atomic probability of the 3LAS in the upper state, and von Neumann entropy are obtained using time series and artificial neural network methods. The influence of probability success photons and the number of added photons to the NBD are examined. The total density matrix is used to compute and analyze the time evolution of the initial photonic negative binomial probability distribution that governs the 3LAS–field photon entanglement behavior. It is shown that the statistical quantities are strongly affected by probability success photons and the number of added photons to the NBD. Also, the prediction of quantum entropy is achieved by the time series and neural network.

Testing the potential of streamflow data to predict spring migration of ungulate herds.

In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the 'spring pulse.' Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.

Electronic cigarette use in adolescence is associated with later cannabis use

BackgroundResearch is needed to determine whether e-cigarette use during adolescence is associated with higher odds of subsequent cannabis use, net of tobacco cigarette use and childhood confounders. MethodsMultivariable logistic regressions predicting using cannabis by age 17 based upon prospective, intergenerational data from 10,251 youth in a nationally representative UK birth cohort followed from infancy who had not used cannabis by age 14. The focal predictor is e-cigarette use by age 14 in the context of the potential confounder tobacco cigarette use. Regressions include sociodemographic background and risk factors assessed at age 11 (e.g., alcohol initiation, problem behaviors, parental and peer smoking) and during early childhood (e.g., maternal smoking during pregnancy, parental substance use). ResultsYouth use of e-cigarettes by age 14 was associated with 2.8 times higher odds of subsequent cannabis use by age 17 [OR 2.75; 95% CI 1.82,4.15], net of tobacco cigarette smoking and childhood confounders. Similarly, use of e-cigarettes by age 14 was associated with 2.5 times higher odds [OR 2.46; 95% CI 1.48,4.08] of frequent cannabis use at age 17 (>10 times in prior year). If youth used both e-cigarettes and tobacco cigarettes by age 14, the probabilities of cannabis initiation were 75% and of frequent use was 25% by age 17, compared to probabilities of 23% and 6%, respectively, among youth who had used neither product. ConclusionsFindings add to accumulating evidence that adolescent e-cigarette use is associated with higher odds of later cannabis initiation and frequent use, independent of tobacco cigarette use.

Intelligent On-Site Lithology Identification Based on Deep Learning of Rock Images and Elemental Data

To reduce the disadvantages of factors such as limited expression of two-dimensional images, weathering, or man-made pollution on image-based methods and realize rapid and accurate on-site lithology identification, we propose an intelligent lithology identification method based on the deep learning of rock images and element information. The initial lithology probabilities and locations are first determined from the images using a object detection model, then the initial lithology probabilities and element data are used as input to correct the lithology probabilities using self-constructed fusion model, and finally all lithology detection results are integrated and redundant results are removed. The verification experiment show that the accuracy of the proposed method is 13.73% higher than that of the image-based method. The proposed method improves the performance of lithology identification by improving the classification of lithology and influencing the choice of bounding box. The proposed fusion identification method is a real-time and accurate method for the identification of complex lithology and provides a new idea for the rapid and intelligent identification of on-site lithology.

Experimental Study of Concrete Normal Mode Cohesive behavior at the Centimeter Scale

Concrete rupture is mainly influenced by the mechanisms occurring at the scale of the Interfacial Transition Zone (ITZ) between cement matrix and aggregates. However, experimental quantitative assessment of concrete cohesive behavior at that scale (centimeter scale) remains an open issue among the scientific community.In this paper, we present a three-point bending test allowing for a measure of the mechanical response of centimeter concrete samples. CEM I Portland cement paste sample, as well as composites cement-silica and cement-steel samples are tested.Weibull parameters for estimating the probability of crack initiation in concrete at the centimeter scale are computed based on the experimental results.A device for recording crack position on the surface of a sample is introduced. Its use in future studies might bring information on the crack dynamic propagation in concrete at the centimeter scale.

Verification Study of Probabilistic Fracture Mechanics Analysis Code for Reactor Pressure Vessel During Pressurized Thermal Shock

This investigation is to present a verification study of probabilistic fracture mechanics (PFM) analysis code for a reactor pressure vessel (RPV) during pressurized thermal shock (PTS). The probabilistic fracture mechanics code FAVOR, developed by Oak Ridge National Laboratory, is used to calculate the conditional probabilities of crack initiation and penetration for welds that are located in the RPV beltline region. The procedure includes deterministic analyses of the temperature and stress distributions through the vessel wall at the PTS, and probabilistic analyses on the vessel failure probability as a result of PTS transients. The RPV geometries, material properties, and properties related to embrittlement are those in taken from previous studies. Two previously suggested hypothetical transients, which may seriously affect RPV integrity, are also taken into account. To verify the results of PFM round robin analysis of RPV during PTS events, several models and Monte Carlo methods for determining PFM performance are used and they agree on the accuracy of the failure assessment is obtained. The present work can be regarded as various important factors about performing PFM that affect in evaluating the structural safety and operational stability of RPVs. The comparisons of the paper also support the finding that the FAVOR code is very practically useful in assessing failure probability.

Pigeon-inspired optimisation-based cooperative target searching for multi-UAV in uncertain environment

In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon-inspired optimisation (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilising the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimised safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.

Multilayer Global Spectral–Spatial Attention Network for Wetland Hyperspectral Image Classification

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods.

ПОСТРОЕНИЕ АНАЛИТИЧЕСКОЙ ЗАВИСИМОСТИ ТРЕХМЕРНОГО РАСПРЕДЕЛЕНИЯ

The article deals with a problem of the control loop generation with subject to the adjustable parameter optimization of controlled objects and surveillance facilities. It was demonstrated that the parameter optimization leads to substantial decrease of the class of current problems ever looking up objects required to be distributed trivariate. The use of analytical dependence of the maximum test of distribution from initial three-dimensional probability matrix for achieving goals was suggested to solve problem while loop functioning. The construction of dependence is based on a known analytical approximation of the bivariate distribution algorithm. The analytical approximation of the bivariate distribution was detected without rigorous theoretical foundations but confirmed by experiments. Analytical dependence of three-dimensional distribution was examined; the research results accuracy was demonstrated. The control is restricted by the dimension of problem, where the solution carried out by the random search algorithm with required number of permutations becomes impossible.

Contests with Delegation and Initial Probabilities of Winning

Contests with Delegation and Initial Probabilities of Winning

DPNPED: Dynamic Perception Network for Polysemous Event Trigger Detection

Event detection is the process of analyzing event streams to detect the occurrences of events and categorize them. General methods for solving this problem are to identify and classify event triggers. Most previous works focused on improving the recognition and classification networks which neglected the representation of polysemous event triggers. Polysemy is habitually somewhat confusing in semantic understanding and hard to detect. To improve polysemous trigger detection, this paper proposes a novel framework called DPNPED, which dynamically adjusts the network depth between polysemous and common words. Firstly, to measure the polysemy, the difficulty factor is devised based on the frequency of a word as an event trigger. Secondly, the DPNPED utilizes a confidence measure to automatically adjust the network depth by comparing the predicted and initial probability distribution. Finally, our model applies focal loss to dynamically integrate the difficulty factor and confidence measure to enhance the learning of polysemous triggers. The experimental results show that our method achieves a noticeable improvement in polysemous event trigger detection.

Determination of the initial density in nonlocal diffusion from final time measurements

<p style='text-indent:20px;'>This paper is concerned with an inverse problem related to a fractional parabolic equation. We aim to reconstruct an unknown initial condition from noise measurement of the final time solution. It is a typical nonlinear and ill-posed inverse problem related to a nonlocal operator. The considered problem is motivated by a probabilistic framework when the initial condition represents the initial probability distribution of the position of a particle. We show the identifiability of this inverse problem by proving the existence of its unique solution with respect to the final observed data. The inverse problem is formulated as a regularized optimization one minimizing a least-squares type cost functional. In this work, we have discussed some theoretical and practical issues related to the considered problem. The existence, uniqueness, and stability of the optimization problem solution have been proved. The conjugate gradient method combined with Morozov's discrepancy principle are exploited for building an iterative reconstruction process. Some numerical examples are carried out showing the accuracy and efficiency of the proposed method.</p>