Backward Technique Research Articles

Modifikasi Perilaku Peningkatan Kemampuan memakai Kaus pada Anak dengan Intellectual Dissability Tingkat Sedang.

Self-help skills are an important ability to support daily life activities. One of the skills is self-dressing, especially for children which entering puberty. Yet, children with intellectual disability have difficulties in conducting self-help skills. A child with intellectual disability, especially children with moderate-severe intellectual disability, needs support in almost everyday. Eventhough, we have to teaching them to be independent and improves their quality of life. The subject for this research was a boy aged 10 years who were diagnosed with moderate intellectual disability. The purpose is to improve the ability of wearing sleeves independently. The method is single AB design. Technique used is backward chaining, prompting, and positive reinforcement. This research was conducted in 9 sessions, each having 5 trials. The results of this research is behavior modification program of wearing sleeves independently using backward chaining techniques can improve the ability of self-dressing in children with intellectual disability.

A Novel Unitary PARAFAC Algorithm for Joint DOA and Frequency Estimation

A novel joint direction of arrival (DOA) and carrier frequency estimation of narrow-band sources is proposed via the unitary parallel factor (PARAFAC) method. By exploiting the unitary transformation and forward–backward techniques, the real-valued PARAFAC signal model is constructed. Then, the real-valued trilinear decomposition can be used to obtain the time delay and direction matrices. Finally, the DOA and carrier frequency estimation can be extracted by using normalization and least squares without additional pair matching. The proposed algorithm provides more accurate parameter estimation than the existing algorithms. Moreover, it is applicable for coherent or closely spaced sources. The simulation results verify the effectiveness of the proposed algorithm.

Bayesian Inference on Dynamic Linear Models of Day-to-Day Origin-Destination Flows in Transportation Networks

Estimation of origin–destination (OD) demand plays a key role in successful transportation studies. In this paper, we consider the estimation of time-varying day-to-day OD flows given data on traffic volumes in a transportation network for a sequence of days. We propose a dynamic linear model (DLM) in order to represent the stochastic evolution of OD flows over time. DLMs are Bayesian state-space models which can capture non-stationarity. We take into account the hierarchical relationships between the distribution of OD flows among routes and the assignment of traffic volumes on links. Route choice probabilities are obtained through a utility model based on past route costs. We propose a Markov chain Monte Carlo algorithm, which integrates Gibbs sampling and a forward filtering backward sampling technique, in order to approximate the joint posterior distribution of mean OD flows and parameters of the route choice model. Our approach can be applied to congested networks and in the case when data are available on only a subset of links. We illustrate the application of our approach through simulated experiments on a test network from the literature.

Object-based feature selection for crop classification using multi-temporal high-resolution imagery

ABSTRACTWith high-resolution remote-sensing data, there are numerous possible features for object description, making the selection of optimal features a time-consuming and subjective process. While substantial efforts have been made to compare the utility of feature selection metrics, less attention has been paid to the efficiency of such in the context of object-based image analysis. In this study, the statistical measurement z-score was used to ensure compatibility with objects. We assessed the feasibility of a z-score method, and then ranked and reduced input features using a backward elimination technique. The results showed that separability can be efficiently estimated based on z-score values, and the near-infrared band performed the best for crop classification. A straightforward trend was observed, and the optimal feature set was created, which was a combination of spectral, temporal, texture information and vegetation indices. These features complement one another to help increase crop map accuracy. For the 40% of the entire sample sizes, the optimal feature sets produced the best trade-off between the number of inputs and classification accuracy, with the misclassification error of 7.09%. Additionally, reliable crop maps were obtained, with the overall accuracy of 92.64%, and the z-score method showed great potential for the separability of crops at object scale using remotely sensed multi-temporal data.

Evaluation of Table Tennis Skills Based on Whole Brain Teaching

To study the table tennis technique assessment in the whole brain teaching, two classes of 2007 in Xuzhou Medical College were taken as the research objects. From the perspective of sports technology teaching, this paper studies the effect of the whole brain teaching on the table tennis scores through teaching. The results show that after learning the skills, the students in the experimental group achieved a significantly higher score than the control group in terms of both forward and backward techniques and standard test scores. It indicates that in the whole brain teaching, consciously strengthening the weak side limb technique of the experimental group is beneficial to master the dominant side limb movement skill, thereby improving the performance in the table tennis examination.

RIESGOS INHERENTES EN LA PRIVACIDAD DE LOS USUARIOS POR EL USO DE LAS TIC

Information and communication technologies (ICT) are an integral part of our lives, and they bring along risks that are inherent to their use, being the intrusion to the privacy of the users one of the most common ones. This work seeks to estimate the effectiveness of the application of security measures by ICT users to mitigate the inherent use risks, departing from a set of variables drawn from the existing body of knowledge, such as digital literacy, and network activities and behaviors, like compulsion and permissibility. To attain this goal, an observational and cross-sectional study was conducted, through the design of a survey instrument that was applied to 159 internet users who were members of social networks and belonged to a community of videogame players. The data obtained from the survey was analyzed using a binary multivariate model of stepwise logistic regression, by the maximum likelihood method, and selecting explanatory variables through forward and backward techniques, as well as undertaking goodness of fit tests. Amongst the most important results, it is shown that, despite fostering the use of better security practices and showing a lower ratio of intrusions to privacy, digital literacy has no significant impact on the likelihood of becoming a victim of intrusion. When stratifying the sample by age, a trend was clear for having a higher vulnerability when age increased. However, the difference between the groups was not as significant as illustrative.

Acoustic Wave Based Time-Domain Full Waveform Inversion and Its Application

In this paper, the acoustic wave-based time-domain full waveform inversion (FWI) is investigated. The FWI is a typical nonlinear ill-posed problem. In order to overcome its ill-posedness, the strategy of stepwise inversion is adopted. The inversion is carried out from low frequency to high frequency, and the inverse result at low frequency is chosen as the initial model for the high frequency inversion. This strategy effectively strengthens the robustness of inversion. The forward simulation is implemented efficiently with the explicit finite difference scheme while the inverse iteration is based on the LBFGS algorithm. The gradient of the objection function is computed by the wavefield backward technique. Numerical computations are not only completed for the benchmark Overthrust model but also for a real data set and good inversion results are yielded.

Gouty arthritis: decision-making following dual-energy CT scan in clinical practice, a retrospective analysis

To establish whether dual-energy CT (DECT) is a diagnostic tool, i.e., associated with initiation or discontinuation of a urate lowering drug (ULD). Secondly, to determine whether DECT results (gout deposition y/n) can be predicted by clinical and laboratory variables. Digital medical records of 147 consecutive patients with clinical suspicion of gout were analyzed retrospectively. Clinical data including medication before and after DECT, lab results, and results from diagnostic joint aspiration and DECT were collected. The relationship between DECT results and clinical and laboratory results was evaluated by univariate regression analyses; predictors showing a p < 0.10 were entered in a multivariate logistic regression model with the DECT result as outcome variable. A backward stepwise technique was applied. After the DECT, 104 of these patients had a clinical diagnosis of gout based on the clinical judgment of the rheumatologist, and in 84 of these patients, the diagnosis was confirmed by demonstration of monosodium urate (MSU) crystals in synovial fluid (SF) or by positive DECT. After DECT, the current ULD was modified in 33 (22.4%) of patients; in 29 of them, ULD was started and in 1 it was intensified. Following DECT, the current ULD was stopped in three patients. In the multivariable regression model, cardiovascular disease (OR 3.07, 95% CI 1.26–7.47), disease duration (OR 1.008, 95% CI 1.001–1.016), frequency of attack (OR 1.23, 95% CI 1.07–1.42), and creatinine clearance (OR 2.03, 95% CI 0.91–1.00) were independently associated with positive DECT results. We found that the DECT result increases the confidence of the prescribers in their decision to initiation or discontinuation of urate lowering therapy regimen in of mono- or oligoarthritis. It may be a useful imaging tool for patients who cannot undergo joint aspiration because of contraindications or with difficult to aspirate joints, or those who refuse joint aspiration. We also suggest the use of DECT in cases where a definitive diagnosis cannot be made from signs, symptoms, and MSU analysis alone.

Elimination of Backward Browsing using Decomposition and Compression for Efficient Navigation Prediction

Analysing the user's browsing patterns stored in weblog file can help in providing the personalised environment, improving website structure and recommending the suitable navigation pattern. While browsing the web, the user navigates in a forward direction following the web topology, results in the session having correlated web pages. Backward navigation takes place when the user returns to the previously visited page. This traversal results in increased session length, complexity and reduction in the prediction accuracy. The frequent backward movement also infers that the web community is not well structured. Therefore, to discover the meaningful browsing pattern and improve the website structure, it is essential to filter the repeated web pages from the session. This paper proposes two novel backward elimination techniques: decomposed backward browsing (BBDcom) and compressed backward browsing (BBCom). BBDcom reduces the length of a session using decomposition. It can restore the original session without losing data. BBCom compresses the session length by eliminating web page(s) lying in between the redundant web pages. The experimental result shows that proposed techniques improve the prediction accuracy and reduces the state-space complexity.

Integrated Big Data Analytics Technique for Real-Time Prognostics, Fault Detection and Identification for Complex Systems

Real-time prediction of the state of complex systems is vital for integrity management since it is easier to plan for asset maintenance, reduce risks associated with unplanned downtime and reduce the cost of maintenance. This study utilized a four-fold cross-validation ensemble for an Artificial Neural Network (ANN) that used Multi-Layer Perceptron (MLP) in a backward propagation technique for haul crane prognosis. Big data on components’ degradation states obtained from the Supervisory Control And Data Acquisition (SCADA) systems were used to implement the study. After preprocessing the dataset, importance scoring was used to compute the Cumulative Target-component Percentage-influence (CTP) of the input variables (source components) on the output variable (the target component) at the 95.5%, 99.3%, 99.9% and 100% levels. The specific source components responsible for the CTP levels of the target component were later used for the ANN network training that followed the cross-validation ensemble technique. The cross-validation ensemble ANN technique was also compared to the classic ANN and other machining learning algorithms. Finally, the best-trained cross-validation ensemble ANN network, which was obtained at the 99.9% CTP level, was used for future estimation of the time of failure of the system to enhance planning for the expected maintenance program that will be required at such times.

Stochastic Unit Commitment in the Presence of Demand Response Program under Uncertainties

In this paper, impacts of various uncertainties such as random outages of generating units and transmission lines, forecasting errors of load demand and wind power, in the presence of Demand response (DR) programs on power generation scheduling are studied. The problem is modelled in the form of a two-stage stochastic unit commitment (UC) which by solving it, the optimal solutions of UC as well as DR are obtained. Generating units’ constraint, DR and transmission network limits are included. Here, DR program is considered as ancillary services (AS) operating reserve which is provided by demand response providers (DRPs. In order to implement the existent uncertainties, Monte Carlo (MC) simulation method is applied. In this respect, scenarios representing the stochastic parameters are generated based on Monte Carlo simulation method which uses the normal distribution of the uncertain parameters. Backward technique is used to reduce the number of scenarios. Then, scenario tree is obtained by combining the reduced scenarios of wind power and demand. The stochastic optimization problem is then modelled as a mixed-integer linear program (MILP). The proposed model is applied to two test systems. Simulation results show that the DR improves the system reliability and also reduces the total operating cost of system under uncertainties.

Identify source location and release time for pollutants undergoing super-diffusion and decay: Parameter analysis and model evaluation

Backward models have been applied for four decades by hydrologists to identify the source of pollutants undergoing Fickian diffusion, while analytical tools are not available for source identification of super-diffusive pollutants undergoing decay. This technical note evaluates analytical solutions for the source location and release time of a decaying contaminant undergoing super-diffusion using backward probability density functions (PDFs), where the forward model is the space fractional advection-dispersion equation with decay. Revisit of the well-known MADE-2 tracer test using parameter analysis shows that the peak backward location PDF can predict the tritium source location, while the peak backward travel time PDF underestimates the tracer release time due to the early arrival of tracer particles at the detection well in the maximally skewed, super-diffusive transport. In addition, the first-order decay adds additional skewness toward earlier arrival times in backward travel time PDFs, resulting in a younger release time, although this impact is minimized at the MADE-2 site due to tritium’s half-life being relatively longer than the monitoring period. The main conclusion is that, while non-trivial backward techniques are required to identify pollutant source location, the pollutant release time can and should be directly estimated given the speed of the peak resident concentration for super-diffusive pollutants with or without decay.

Application of various control strategies to Japanese encephalitic: A mathematical study with human, pig and mosquito

Japanese encephalitis (JE) is a public health problem that threats the entire world today. Japanese Encephalitis virus (JEV) mostly became a threat due to the significant number of increase of susceptible mosquito vectors and vertebrate hosts in Asia by which around 70,000 cases and 10,000 deaths per year took place in children below 15 years of age. In this paper, a mathematical model of JE due to JEV from the vector source (infected mosquito) and two vertebrate hosts (infected human and infected pig) is formulated. The disease can be controlled by applying several control measures such as vaccination, medicine and insecticide to the JE infection causing species. The model has been formulated as an optimal control problem and has been solved using Pontryagin’s maximum principle. Also, the stability of the system has been studied with the help of basic reproduction number for disease free and endemic equilibrium. The results of fixed control for endemic equilibrium is presented numerically and depicted graphically. The effects of different control strategies on human, pig and mosquito has been analyzed using Runge–Kutta 4th order forward and backward techniques and presented thereafter graphically.

Use of backward masking test for the study of visual information processing in healthy subjects and schizophrenic patients

The study of 14 healthy subjects and 15 schizophrenic patients was conducted under visual backward masking conditions. Sensory thresholds were identified using the method of constant stimuli. A special modification of the backward masking technique with lateralized presentation of test and masking stimuli was used to study the lateral characteristics of visual attention. It was found that the thresholds of letter stimulus identification were significantly higher in patients with schizophrenia than in healthy subjects. Only in patients the asymmetry of visual perception was revealed with the higher recognition thresholds in the left visual hemifield. The overall data analysis suggests an association between increased recognition thresholds in schizophrenic patients and changes in the interruption mechanism functioning at the neocortex level.

Personality, risk tolerance and social network use: an exploratory study

Purpose – The vast amount of information available via online social networks (OSN) makes it a very good avenue for understanding human behavior. One of the human characteristics of interest to financial practitioners is an individual’s financial risk tolerance. The purpose of this paper is to look at the relationship between an individual’s OSN behavior and his/her financial risk tolerance. Design/methodology/approach – The study uses data collected from a sample of 220 university students and the backward variables selection ordinary least squares regression analysis technique to achieve its objective. Findings – The results of the study find that the frequency of logging on to social network sites indicates an individual who has higher financial risk tolerance. Additionally, the increasing use of social networks for social connection is found to be associated with lower financial risk tolerance. The results are mostly consistent when the sample is split based on prior financial knowledge. Originality/value – To the authors’ knowledge this is the first study which documents the possibility of understanding an individual’s financial risk tolerance via his/her social network activity. This provides investment/financial consultants with more avenues for gathering information in order to understand their current or potential clients hence providing better services.

Sensor Array Processing for High Resolution of DOA Estimation of Spatial Subspace using Smoothing Technique of Beamspace MUSIC in Coherent Channels

MUSIC algorithm has often used to solve the direction of arrival estimation problem to take advantage of the benefits of beamspace operations, such as reduced computation complexity, reduced sensitivity to system errors, good resolution, reduced bias in the estimation. In this paper, a variant of the beamspace MUSIC algorithm is developed, taking advantage of unitary transformations. In general, the beamspace MUSIC algorithm improves direction of arrival estimation by using reduced order characteristic polynomial rooting though Gaussian column reduction. The unitary beamspace MUSIC algorithm reduces the computational complexity of MUSIC algorithm by using real valued forward and backward, while maintaining the original precision. We use forward backward spatial smoothing technique as the preprocessor of beamspace MUSIC algorithm to recover the reduced rank of the covariance matrix due to the coherence of source signals. Through computer simulations show that the combination of spatial smoothing a beamspace MUSIC can achieve good resolution performance.

Time prediction models of grapple skidder HSM 904 using multiple linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS)

The majority of productivity studies in forest operations use the ordinary least square regression in order to calculate time prediction models. Objective of this paper was to compare two methods, the adaptive neuro fuzzy inference system (ANFIS) and the multiple linear regression (MLR) pertaining to the modeling the loading time with the grapple skidder HSM 904. In order to investigate the influence of changes in membership functions (MF), inference system (IS) and optimization methods on the performance of the ANFIS model, four types of MF, two types of IS and two types of optimization methods were applied. Two stepwise (forward and backward) techniques were applied in the development of the MLR models. The data originated from a time study of 35 truckloads including 238 loading cycles with the specific forest machine. The comparison of the various modeling approaches indicated that the generated ANFIS with constant IS, Backpropagation training algorithm, and Gaussian membership function had a greater predictive power (R2 = 0.84) and higher performance than all calculated MLR models (R2 = 0.61). The results also showed that ANFIS could predict with a relatively high accuracy (R2 = 0.74) the loading time by adopting the “number of loaded logs” as the single input variable.

Backward mapping solutions of the Boltzmann equation in cylindrically symmetric, uniformly charged auroral ionosphere

By applying a backward mapping technique, we solve the Boltzmann equation to investigate the effects of ion-neutral collisions on the ion velocity distribution and related transport properties in cylindrically symmetric, uniformly charged auroral ionosphere. Such a charge geometry introduces a radial electric field which increases linearly with distance from the axis of symmetry. In order to obtain complete analytical solutions for gaining physical insights into more complicated problems, we have substituted a relaxation collision model for the Boltzmann collision integral in the Boltzmann equation. Our calculations show that collisions drive the velocity distribution to a “horseshoe” shape after a few collision times. This feature extends to all radial positions as long as the electric field keeps increasing linearly versus radius. If the electric field is introduced suddenly, there is a transition from the collision-free pulsating Maxwellian distributions obtained in previous work (Ma and St.-Maurice, J. Geophys. Res., 113:A05312, 2008) to the “horseshoe” shapes on a time scale of within the few collision times. We also show how the transport properties evolve in a similar fashion, from oscillating to a non-oscillating features over the same time interval.

A knowledge-based consultancy system using ICT Newhouse indicators with reasoning techniques for consultants in e-learning

In e-learning evaluation plays an important role in learning management systems. In this article we present a knowledge-based consultancy system designed to enable novel methods for web-based training with advanced features. This study aims to develop a new approach using ICT Newhouse indicators with reasoning techniques capable of handling both explicit and tacit knowledge using a fuzzy rule-based approach in which linguistic (semantic) terms are used to express rules and preferences. The fuzzy rules are implemented using forward and backward chaining techniques. Evaluation of our proposed approach has utilised testing using 12 courses at Hanoi University of Science and Technology with experts feedback using the three reasoning rule levels in the knowledge base to realise precise definition(s) of performance and quality of outcomes. Experimental results derived in the third layer demonstrate that the proposed approach achieved is the highest being in the range of 84% to 90% compared to expert's responses in respect of the correct answers, combined events, and results of other rules in the knowledge base. As compared to alternative conventional approaches which use forward and backward chaining, the performance of the proposed approach shows improvements measured by responses in real-time when applied to the web-based training with automated reasoning dialogue consultants.

Formal Specification of the Assurance Point Web Service Composition Model

This paper presents a formal specification of the Assurance Point (AP) web service composition model. The AP model provides a flexible way of checking constraints and responding to execution errors in service composition. An AP is a combined logical and physical checkpoint, providing an execution milestone that stores critical data and interacts with integration rules (IRs) to alter program flow and to invoke different forms of recovery depending on the execution status. In this paper, the execution and recovery semantics of assurance points have been fully defined in the context of if-else, parallel, and loop control structures. The activities and complex control structures of the AP model have been formalized and tested in the Yet Another Workflow Language (YAWL) engine. By doing so, the correctness of the execution and recovery semantics in the AP model has been verified, demonstrating that YAWL nets of the AP model satisfy the soundness property. Different from existing web service composition models, the AP model presented by this research provides multiple levels of protection against service execution failure with a combination of forward and backward recovery techniques.