Behavioral Barriers of Tuberculosis Notification in Private Health Sector: Policy implication and Practice

Under-reporting of new tuberculosis (TB) cases is one of the main problems in TB control, particularly in countries with high incidence and dominating role of a private sector in TB cases diagnosing. The purpose of this paper was to explore behavioral determinants of under-reporting of new TB cases among private sector physicians in Iran. We conducted a population-based, cross-sectional study of physicians working in private clinics. The data collection tool was designed using the theory of planned behavior. We used structural equation models with maximum likelihood estimation to examine attitude towards the notification behavior. Of 519 physicians, 433 physicians completed the questionnaire. Attitude towards notification had the highest score (mean score=87.65; sd=6.79; range: 0-100). The effect of perceived behavioral controls on the notification behavior ((β ̂)= 0.13; CI: .01-.25) was stronger than the total effect of attitude ((β ̂)=0.06; CI: .00-.12) and subjective norms ((β ̂)=0.01; CI: -.00 -.03) on the behavior. However, the attitude was the main predictor of intention and justified 46% of the intention variance. Intention had a significant effect on the behavior ((β ̂)= 0.09; CI:.01- .16). Considering stronger effect of perceived behavioral control on the behavior, interventions aiming at facilitating notification process would be more effective than those aiming at changing the attitude or enhancing intention among physicians. To the best of our knowledge, no other study previously explored determinants of under-reporting from the behavioral and cognitive perspective. Specifically, we explored the role of the theory of planned behavior constructs in predicting intention to notify new TB cases

Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons

Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons

A new class of integrable diffusion-reaction processes

We consider a process in which there are two types of particles, A and B, on an infinite one-dimensional lattice. The particles hop to their adjacent sites, like the totally asymmetric exclusion process (ASEP), and have also the following interactions: A+B -> B+B and B+A -> B+B, all occur with equal rate. We study this process by imposing four boundary conditions on ASEP master equation. It is shown that this model is integrable, in the sense that its N-particle S-matrix is factorized into a product of two-particle S-matrices and, more importantly, the two-particle S-matrix satisfy quantum Yang-Baxter equation. Using coordinate Bethe-ansatz, the N-particle wavefunctions and the two-particle conditional probabilities are found exactly. Further, by imposing four reasonable physical conditions on two-species diffusion-reaction processes (where the most important ones are the equality of the reaction rates and the conservation of the number of particles in each reaction), we show that among the 4096 types of the interactions which have these properties and can be modeled by a master equation and an appropriate set of boundary conditions, there are only 28 independent interactions which are integrable. We find all these interactions and also their corresponding wave functions. Some of these may be new solutions of quantum Yang-Baxter equation.Comment: LaTex,16 pages, some typos are corrected, will be appeared in Phys. Rev. E (2000

Decomposition-based stacked bagging boosting ensemble for dynamic line rating forecasting

Effective exploitation of overhead transmission lines needs reliable and precise dynamic line rating forecasting. High-accuracy dynamic line rating forecasting, in particular, is an important short-term method for coping with grid congestion, enhancing grid stability, and accommodating high renewable energy penetration. Due to the non-stationarity and stochasticity of the meteorological variables, a single model is often not sufficient to accurately predict the dynamic line rating. Herein, a new stacked bagging boosting ensemble is developed based on multivariate empirical mode decomposition to overcome single models' restrictions and increase the dynamic line rating forecasting performance. The developed ensemble is utilized on the data gathered from a 400 kV aluminum conductor steel-reinforced overhead power line with a length of 32.85 Km between Ghadamgah and Binalood wind farms, located in the northeast of Iran. The simulation results substantiate that the proposed ensemble can capture meteorological variables' non-linear characteristics, yielding more accurate yet to noisy data forecasts than single forecasting models

A new false data injection attack detection model for cyberattack resilient energy forecasting

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant on a more accurate forecasting. The accuracy and generalizability of the forecasting rest to a great extent upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly-protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This paper proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resilience of the energy forecasting systems. The proposed mechanism is based on cross-validation and least-squares providing accurate detection with low computational cost and high scalability without utilizing the model and parameters of the system. Effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models including decision tree, bagging, random forest, boosting, gradient boosting, and XGboost. Experiments indicate that corrupted data is properly located and removed, whereby the accuracy and generalizability of the final forecasts is recovered

Multiple micro-optical atom traps with a spherically aberrated laser beam

We report on the loading of atoms contained in a magneto-optic trap into multiple optical traps formed within the focused beam of a CO_{2} laser. We show that under certain circumstances it is possible to create a linear array of dipole traps with well separated maxima. This is achieved by focusing the laser beam through lenses uncorrected for spherical aberration. We demonstrate that the separation between the micro-traps can be varied, a property which may be useful in experiments which require the creation of entanglement between atoms in different micro-traps. We suggest other experiments where an array of these traps could be useful.Comment: 10 pages, 3 figure

On analog quantum algorithms for the mixing of Markov chains

The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical mixing time. In this article, we deal with analog quantum algorithms for mixing. First, we provide an analog quantum algorithm that given a Markov chain, allows us to sample from its stationary distribution in a time that scales as the sum of the square root of the classical mixing time and the square root of the classical hitting time. Our algorithm makes use of the framework of interpolated quantum walks and relies on Hamiltonian evolution in conjunction with von Neumann measurements. There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense. In this scenario, the quantum mixing time is defined as the time required to sample from a distribution that is close to this limiting distribution. Recently we provided an upper bound on the quantum mixing time for Erd\"os-Renyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we also extend and expand upon our findings therein. Namely, we provide an intuitive understanding of the state-of-the-art random matrix theory tools used to derive our results. In particular, for our analysis we require information about macroscopic, mesoscopic and microscopic statistics of eigenvalues of random matrices which we highlight here. Furthermore, we provide numerical simulations that corroborate our analytical findings and extend this notion of mixing from simple graphs to any ergodic, reversible, Markov chain.Comment: The section concerning time-averaged mixing (Sec VIII) has been updated: Now contains numerical plots and an intuitive discussion on the random matrix theory results used to derive the results of arXiv:2001.0630

Aortic Pulse Wave Velocity as Adjunct Risk Marker for Assessing Cardiovascular Disease Risk : Prospective Study

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