Linear Integration Model Research Articles

Metastable zone width and nucleation kinetics of vanillyl alcohol crystallization in various solvents

In this study, linear cooling batch crystallization of vanillyl alcohol using a laser power system was carried out to experimentally measure the metastable zone widths of vanillyl alcohol in selected solvents (ethanol, 2-propanol, and methyl cyanide) at various saturated temperatures (40, 50, and 60 °C), cooling rates (0.5, 1, 1.5, and 2 °C/min), and agitation speeds (300, 400 rpm). Besides, the sonocrystallization of vanillyl alcohol in ethanol was conducted for measuring MSZW at a various ultrasonic amplitudes (0 %, 25 %, 50 %, and 100 %) at a fixed temperature of 40 °C, cooling rate of 1 °C/min, and agitation speed of 300 rpm. For all three solvents, the MSZW decreases with saturation temperature, while it increases with cooling rate, and this trend doesn’t change for different agitation speeds. Three models—the self-consistent Nývlt-like model, the classical 3D nucleation theory model, and the simplified linear integral model based on classical nucleation theory (CNT)—were employed to estimate the nucleation kinetic parameters for vanillyl alcohol in three solvents. The goodness fit of the models were checked by the coefficient of determination (R-squared). The R-squared values reflected a very good fit between the experimental and predicted values and implied that the models are reliable to estimate nucleation kinetic parameters. Additionally, the interfacial energy between vanillyl alcohol and the solvents was observed to decrease with increasing temperature. Overall, the results indicate that a low nucleation order and low interfacial energy suggest weak solute–solvent interactions, making nucleation easier in the following order: methyl cyanide > ethanol > 2-propanol.

Broadband high-resolution direction of arrival estimation using the generalized weighted Radon transform

Traditional direction of arrival (DOA) estimation algorithms typically have poor spatial resolution and robustness. In this paper, we propose a broadband high-resolution DOA estimation method based on the generalized weighted Radon transform (GWRT). The array signal can be converted into the frequency-wavenumber (f-k) domain using the conditional wavenumber spectrum function (CWSF). Then, a linear integral mathematical model for high-resolution DOA estimation is derived by transforming the f-k domain into the azimuth-energy domain using the GWRT. Computer simulation and sea trials were conducted to validate the feasibility and performance of the proposed method. The results obtained indicate that the proposed method yields a lower sidelobe level and can more effectively suppress the output energy in the non-target direction when compared to the conventional beamforming (CBF), steered minimum variance (STMV), and deconvolution (DCV) methods. Further, the proposed method provides improved spatial resolution and robustness in a multi-target environment.

Monochromatic image reconstruction via machine learning

X-ray computed tomography (CT) is a nondestructive imaging technique to reconstruct cross-sectional images of an object using x-ray measurements taken from different view angles for medical diagnosis, therapeutic planning, security screening, and other applications. In clinical practice, the x-ray tube emits polychromatic x-rays, and the x-ray detector array operates in the energy-integrating mode to acquire energy intensity. This physical process of x-ray imaging is accurately described by an energy-dependent non-linear integral equation on the basis of the Beer–Lambert law. However, the non-linear model is not invertible using a computationally efficient solution and is often approximated as a linear integral model in the form of the Radon transform, which basically loses energy-dependent information. This approximate model produces an inaccurate quantification of attenuation images, suffering from beam-hardening effects. In this paper, a machine learning-based approach is proposed to correct the model mismatch to achieve quantitative CT imaging. Specifically, a one-dimensional network model is proposed to learn a non-linear transform from a training dataset to map a polychromatic CT image to its monochromatic sinogram at a pre-specified energy level, realizing virtual monochromatic (VM) imaging effectively and efficiently. Our results show that the proposed method recovers high-quality monochromatic projections with an average relative error of less than 2%. The resultant x-ray VM imaging can be applied for beam-hardening correction, material differentiation and tissue characterization, and proton therapy treatment planning.

Recognition and Simulation of Exercise Mode Based on Energy Consumption Model

Sports energy consumption is a quantitative reflection of physical exercise effect. Combined with different sports modes and students’ physical characteristics, the calculation model of sports energy consumption is put forward. Firstly, the relationship between students’ age, height, weight, gender, and energy consumption is analyzed by using multiple linear regression method, and a linear acceleration model is proposed by combining different exercise methods. The relationship between the integral value of acceleration and energy consumption is analyzed, and a linear integral model based on different motion modes is proposed. Based on the kinetic energy theorem, the student movement energy expenditure is estimated. This paper proposes a human movement recognition method based on hybrid features, which mostly can represent the curve of the second generation wavelet transform edge thinning, and from the edge and texture features of the optimal said human posture, the statistical characteristic of the second generation wavelet transform is subtly trained as image characteristics, learning and recognition of human movement. Then, the motion recognition algorithm is tested, which can effectively identify the common movement patterns of primary and middle school students. Finally, the linear relationship between the estimation results of the model and the calculation results of Meijer is analyzed. The analysis results show that the linear acceleration model proposed in this paper can estimate the energy consumption of primary and middle school students’ motion relatively accurately.

Uneven state distribution of homicides in Brazil and their effect on life expectancy, 2000–2015: a cross-sectional mortality study

ObjectiveTo determine cause-specific and age-specific contributions to life expectancy changes between 2000 and 2015, separately by state and sex in Brazil, with a focus on homicides.DesignRetrospective cross-sectional demographic analysis of...

RAINFALL TREND AND IT IMPACT IN KEFFI NASARAWA STATE

Rainfall is the vital ingredient and factor of soil nourishment on which crops are grown. The basic agricultural practices all over the world depend on rainfed cultivation for their sustainable development. This study focus on the rainfall trend between 2010 -2018 as it affects crop production in Keffi Nasarawa State, Nigeria. The time series analysis was conducted by using appropriate techniques of data collection and analysis. The linear integration model, quadratic trend model, growth curve model and regression analysis was applied to show the correlation between the rainfall and crop production in the study area. It was discovered that the period of rainy months have high correction and significantly fitted the model which indication high crop yields per harvest.

Recovering the electrical conductivity of the soil via a linear integral model

Electromagnetic induction techniques are often used to ascertain the presence of conductive substances in the subsoil. The device we are interested in generates an electromagnetic field and measures the secondary field produced by eddy currents induced by the primary field. We analyze a linear model which reproduces the device readings, consisting of a system of two Fredholm linear integral equations of the first kind. After studying the continuous problem in various function spaces, we describe three collocation methods to transform the integral equations into a system of linear equations, which is solved by a regularization technique. Numerical experiments show the performance of the different discretizations, and of a particular approach to deal with the integral truncation error. We also verify the influence on the solution of regularization matrices based on the discretization of the first and second derivative.

Evaluating the Linear Integration Method of Estimating Cumulative Loading Using an Eccentric Exercise

The ability to quantify cumulative exposure is critically in understanding dose-response relationship in the development of work-related musculoskeletal disorders. Different integration methods have been used in estimating cumulative loading (force or torque). The general objective of cumulative loading integration methods has been to sum the loading exposure for each individual task, calculate by multiplying the magnitude of the task loading times the task duration, and develop an “area under the curve”. An assumption of this linear integration model is that short time exposure to high forces will result in a similar level of damage as relatively long-time exposure to low forces. In this study, three loading groups of eccentric exercise with the same “area under the curve” were performed by thirty participants (ten in each group). Relaxed elbow angle and maximum isometric voluntary contractions (MIVC) were collected before, immediately after, and 2, 4, 8 days after the exercise. The relaxed elbow angle and the changes in MIVC were significantly impacted by the loading group. This result suggests that the linear integration method of estimating cumulative loading may underestimate the impact of high force loading in terms of cumulative muscle damage.

Towards Transformative Regional Integration and Measuring Integration Progress

The state-driven and trade-centred regional integration approach needs to be replaced by a participatory, transformative and inclusive regional integration approach. In this contribution arguments about the dynamics and the performance of regional integration processes in Africa are presented, and some policy considerations follow. It is intended to look at the limits of the “linear regional integration model” and then to present as an alternative the “transformative regional integration mode...

English

The objectives of this study are to establish damage functions for the Ramularia areola pathosystem and characterize the damage in cultivars susceptible and resistant to it. The experiment was conducted in the city of Tangará da Serra in the 2012/2013 harvest season. The experimental design was randomized blocks with four replications in a factorial arrangement of four cultivars × four gradients. The plots were divided into two subplots (twin plots). The division I received fungicide applications at intervals of 15, 30 and 45 days and no applications to establish the gradients of the disease, and the division II received weekly applications of fungicide in order to be kept without the disease or at minimum levels. FiberMax 993, FMT 701, FMT 707 RX and IAC 25 RMD cultivars were used with different reactions to ramularia spots. Disease severity was weekly determined and used to calculate the area below the disease progress curve (ABDPC). In addition to area below the severity progress curve (ABSPC), the variables related to the following production components were obtained: seed cotton productivity, damage, percentage of damage to productivity, average number of bolls per plant, weight of one hundred seeds, percentage of fiber, and components related to fiber technological characteristics. Analyses of variance were conducted and data were adjusted by the linear integral model. The ABSPC variable showed a high relation with damage and percentage of damage to productivity. Estimates reached levels of up to 975.42 kg ha-1, or 36.83%, of damage to productivity.   Key words: Damage, functions, Ramularia areola, Gossypium hirsutum, fiber technological characteristics (HVI).

A surrogate model for acid functional group conversion in alkyd reactor

Due to lack of detailed kinetic model, the alkyd resin polymerization reactor problem has been a subject of extensive theoretical, simulation and experimental studies, given the demand of processes with better compromises between safety, productivity and quality, and the development of control methods. In this study, a novel linear integral kinetic model for the conversion of acid functional group have been developed for predicting the progress of the reaction in the alkyd batch reactor on the basis of empirical third-order rate model, wave propagation theory and discrete delayed measurements. The new approach when tested, using an alkyd reactor calibration problem was found to be much more reliable. The model was able to approximate the motion and the trajectory of the polyesterification reaction process such that the desired properties were kept within specification.

Sparse Linear Integration of Content and Context Modalities for Semantic Concept Retrieval

The semantic gap between low-level visual features and high-level semantics is a well-known challenge in content-based multimedia information retrieval. With the rapid popularization of social media, which allows users to assign tags to describe images and videos, attention is naturally drawn to take advantage of these metadata in order to bridge the semantic gap. This paper proposes a sparse linear integration (SLI) model that focuses on integrating visual content and its associated metadata, which are referred to as the content and the context modalities, respectively, for semantic concept retrieval. An optimization problem is formulated to approximate an instance using a sparse linear combination of other instances and minimize the difference between them. The prediction score of a concept for a test instance measures how well it can be reconstructed by the positive instances of that concept. Two benchmark image data sets and their associated tags are used to evaluate the SLI model. Experimental results show promising performance by comparing with the approaches based on a single modality and approaches based on popular fusion methods.

Modeling the Roles of Category and Feature Information in Inference

The goal of the current research is to use the experimental methods and mathematical models of the information integration framework to precisely determine how category and feature information are combined when making an inference. In three experiments, participants were trained on a probabilistic relationship between a category label and the presence of a property and, separately, the relationship between a visual feature and the presence of the property. Participants were then shown the category label alone, the feature alone, or both in combination, and asked to infer the presence or absence of the property. Two information integration models, the fuzzy logical model of perception and the linear integration model, were fit to the data. The modeling results show that participants were non-Bayesian in their combination of the two sources of information, showed diversity in the relative weight placed on category information, and consistently used each source of information to the extent to which it was known.

The contributions of image content and behavioral relevancy to overt attention.

During free-viewing of natural scenes, eye movements are guided by bottom-up factors inherent to the stimulus, as well as top-down factors inherent to the observer. The question of how these two different sources of information interact and contribute to fixation behavior has recently received a lot of attention. Here, a battery of 15 visual stimulus features was used to quantify the contribution of stimulus properties during free-viewing of 4 different categories of images (Natural, Urban, Fractal and Pink Noise). Behaviorally relevant information was estimated in the form of topographical interestingness maps by asking an independent set of subjects to click at image regions that they subjectively found most interesting. Using a Bayesian scheme, we computed saliency functions that described the probability of a given feature to be fixated. In the case of stimulus features, the precise shape of the saliency functions was strongly dependent upon image category and overall the saliency associated with these features was generally weak. When testing multiple features jointly, a linear additive integration model of individual saliencies performed satisfactorily. We found that the saliency associated with interesting locations was much higher than any low-level image feature and any pair-wise combination thereof. Furthermore, the low-level image features were found to be maximally salient at those locations that had already high interestingness ratings. Temporal analysis showed that regions with high interestingness ratings were fixated as early as the third fixation following stimulus onset. Paralleling these findings, fixation durations were found to be dependent mainly on interestingness ratings and to a lesser extent on the low-level image features. Our results suggest that both low- and high-level sources of information play a significant role during exploration of complex scenes with behaviorally relevant information being more effective compared to stimulus features.

Thermal decomposition kinetics of sweet sorghum bagasse analysed by model free methods

The iterative linear integral model free method proposed by Cai and Chen (J. Cai and S. Chen: J. Comput. Chem., 2009, 30, 1986–1991.) and the non‐linear method proposed by Vyazovkin and Dollimore (S. Vyazovkin and D. Dollimore: J. Chem. Inf. Comput. Sci., 1996, 36, 42–45.) have been used for the estimation of the activation energy values of the thermal decomposition of sweet sorghum bagasse. Applied to the non‐isothermal thermogravimetric analysis data on the thermal decomposition of sweet sorghum bagasse, the model free methods yield the resultant activation energy values that depend on the degree of conversion. The effective activation energy values vary from 150 to 220 kJ mol−1 in the conversion range of 0·15–0·85 for the thermal decomposition of sweet sorghum bagasse.

Integration of disparity and velocity information for haptic and perceptual judgments of object depth

Do reach-to-grasp (prehension) movements require a metric representation of three-dimensional (3D) layouts and objects? We propose a model relying only on direct sensory information to account for the planning and execution of prehension movements in the absence of haptic feedback and when the hand is not visible. In the present investigation, we isolate relative motion and binocular disparity information from other depth cues and we study their efficacy for reach-to-grasp movements and visual judgments. We show that (i) the amplitude of the grasp increases when relative motion is added to binocular disparity information, even if depth from disparity information is already veridical, and (ii) similar distortions of derived depth are found for haptic tasks and perceptual judgments. With a quantitative test, we demonstrate that our results are consistent with the Intrinsic Constraint model and do not require 3D metric inferences (Domini, Caudek, & Tassinari, 2006). By contrast, the linear cue integration model (Landy, Maloney, Johnston, & Young, 1995) cannot explain the present results, even if the flatness cues are taken into account.

SYNCHRONIZATION OF CHAOTIC OSCILLATORS BY MEANS OF GENERALIZED PROPORTIONAL INTEGRAL OBSERVERS

The problem of synchronization of chaotic oscillators, using state observers, is handled through the use of a simplified perturbed linear integration model of the chaotic system output dynamics. The linear simplified model of the chaotic system does not entitle approximate linearizations, nor state coordinate transformations, but, simply, a pure linear integration model with additive unknown but bounded perturbation inputs lumping all the output dynamics nonlinearities. An extended linear state observer (here addressed as Generalized Proportional Integral (GPI) observer) is proposed for the accurate estimation of the phase variables and the perturbation input of the nonlinear output dynamics. The effectiveness of the approach is tested in the synchronization of two study cases: The Genesio–Tesi chaotic system and the Rossler oscillator. As an application of the estimation process, a coding–decoding process involving encrypted messages, in transmitted phase variables, is implemented using a Rossler chaotic system.

Model evaluation using grouped or individual data

Analyzing the data of individuals has several advantages over analyzing the data combined across the individuals (the latter we term group analysis): Grouping can distort the form of data, and different individuals might perform the task using different processes and parameters. These factors notwithstanding, we demonstrate conditions in which group analysis outperforms individual analysis. Such conditions include those in which there are relatively few trials per subject per condition, a situation that sometimes introduces distortions and biases when models are fit and parameters are estimated. We employed a simulation technique in which data were generated from each of two known models, each with parameter variation across simulated individuals. We examined how well the generating model and its competitor each fared in fitting (both sets of) the data, using both individual and group analysis. We examined the accuracy of model selection (the probability that the correct model would be selected by the analysis method). Trials per condition and individuals per experiment were varied systematically. Three pairs of cognitive models were compared: exponential versus power models of forgetting, generalized context versus prototype models of categorization, and the fuzzy logical model of perception versus the linear integration model of information integration. We show that there are situations in which small numbers of trials per condition cause group analysis to outperform individual analysis. Additional tables and figures may be downloaded from the Psychonomic Society Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.

Optimization of harvesting age in an integral age-dependent model of population dynamics

The paper deals with optimal control in a linear integral age-dependent model of population dynamics. A problem for maximizing the harvesting return on a finite time horizon is formulated and analyzed. The optimal controls are the harvesting age and the rate of population removal by harvesting. The gradient and necessary condition for an extremum are derived. A qualitative analysis of the problem is provided. The model shows the presence of a zero-investment period. A preliminary asymptotic analysis indicates possible turnpike properties of the optimal harvesting age. Biological interpretation of all results is provided.

The two membrane segments of leader peptidase partition one by one into the lipid bilayer via a Sec/YidC interface.

We have addressed the mechanism of insertion of both transmembrane segments (TMs) of leader peptidase, a double-spanning protein, into the Escherichia coli inner membrane. Using photo-crosslinking, the first TM (H1) was shown to insert at a Sec-translocon/YidC interface in a fixed orientation. H1 lost its contacts with the Sec-translocon and gained access to lipids near YidC soon after complete exposure outside the ribosome. Following lipid integration, it moved away from the Sec/YidC insertion site. The second TM (H2) inserted and interacted with SecY and YidC in a similar transient fashion. The data are consistent with a linear integration model in which the TMs of polytopic inner membrane proteins move one by one from a Sec/YidC insertion site into the lipid bilayer. We propose that YidC assists the lipid partitioning of single TMs.