Use Of Different Weights Research Articles

In Spiderman we trust: The influence of familiar media characters on the decision-making of primary school children

Children are increasingly active consumers in the media world and are thus confronted with a wide range of information. Making good decisions in such an environment is a major challenge. Weighting valid information in decision-making is an important skill that children must learn and apply. Yet, how do children weight information and which weights do they use? We developed a measuring instrument for children's weighting of advices by cue validity – the so-called Space Treasure Hunt. It is an easy to understand, child-friendly decision game. By anchoring it in the reality of children's media lives, we are able to assess the influence of familiarity and affect on children's decision making. During a treasure hunt in game situated in outer space, children are confronted with various characters who provide contradictory advice regarding where to find treasures. One of the advice-givers was an expert (valid cue), whereas the other advice-giver varied in terms of its media familiarity and affect. In our study (N = 372), we found that media familiar characters had a strong influence on children's decision-making. Media idols showed an assimilation effect, meaning that children followed the expert's advice the least often when it contradicted their media idol. Conversely, media familiar but less liked characters showed a contrast effect, meaning that children followed the expert's advice most often when it contradicted the media familiar, less liked character. Moreover, we found differences between children in terms of a three-way split: One third of the children, respectively, systematically followed the expert's advice, the media idol, or made their decisions unsystematically. We discuss how our results indicate the use of different weights in children's decision making.

Detecção automática de epilepsia generalizada através da análise espectral singular multivariada

The collection and interpretation of electroencephalogram (EEG) signals are laborious and time-consuming activities, requiring a trained specialist to perform them. Automatic detection of epilepsy may be a solution. However, research on the subject has focused on detecting specific, non-generalized epilepsies in a larger patient population. Decomposition of signals, through singular spectrum analysis, of records of patients with epilepsy for subsequent verification of the energy limit. These records were available in a publicly accessible signal bank. The use of different weights to calculate means and standard deviations of the energy series and different sample sizes contributed to improve the diagnosis.

Allocating aid for COVID-19 support: A constrained optimisation approach.

The objective of this paper is to develop a prescriptive model for the inter-country allocation of aid for COVID-19 support. Aid allocations are increasing functions of three dimensions of need: (i) hazard and exposure to COVID-19; (ii) vulnerability to COVID-19 impacts; and (iii) the capacity to cope, subject to an aid budget constraint. COVID-19 support from official donors to all ODA eligible countries is modeled. Aid allocations for COVID-19 support prescribed by the model are compared to actual allocations in 2020. Findings from the main model suggest that low-income countries are over-represented among the countries that should have received a greater share of the aid for COVID-19 support. Middle-income countries tended to receive more donor support for COVID-19 than amounts suggested by the model. The choice of weights assigned to the dimensions of need is arbitrary but findings are broadly consistent with respect to the use of different weights, alternative measures of support and changes to other parameters of the model. It is recommended that the donor community commit to a transparent needs-based funding formula when addressing a global pandemic.

Physical Bases Employed in the MRI Based on the Morphology of Tropical Fruits study: An Experience Report

Magnetic Resonance Imaging (MRI) is an exam that portrays morphological structures through high-definition images using a magnetic field. Understanding the physical basis of MRI represents a challenge for students in diagnostic imaging, as it involves advanced and complex technology. Here, we discuss image acquisition parameters, demonstrating advantages and disadvantages in using different values for each variable addressed and their impact on the quality and resolution of images obtained from the morphological study of tropical fruits. Based on the analysis of images of fruits submitted to MR scanning, we investigated the advantages and disadvantages of using different values for the field of view, slice thickness, matrix, and the use of different weights for scanning, measuring the resolution and quality of the images obtained. It was found that the use of fruit scanning represents an excellent tool for the development of students in radiodiagnosis, aiming at understanding the parameters used in the protocols for acquiring MR equipment and optimizing the quality of images.

Physical Bases Employed in the MRI Based on the Morphology of Tropical Fruits study: An Experience Report

Magnetic Resonance Imaging (MRI) is an exam that portrays morphological structures through high-definition images using a magnetic field. Understanding the physical basis of MRI represents a challenge for students in diagnostic imaging, as it involves advanced and complex technology. Here, we discuss image acquisition parameters, demonstrating advantages and disadvantages in using different values for each variable addressed and their impact on the quality and resolution of images obtained from the morphological study of tropical fruits. Based on the analysis of images of fruits submitted to MR scanning, we investigated the advantages and disadvantages of using different values for the field of view, slice thickness, matrix, and the use of different weights for scanning, measuring the resolution and quality of the images obtained. It was found that the use of fruit scanning represents an excellent tool for the development of students in radiodiagnosis, aiming at understanding the parameters used in the protocols for acquiring MR equipment and optimizing the quality of images.

Reconfigurable Hardware Design Approach for Economic Neural Network

Hardware-based neural networks are becoming attractive because of their superior performance. One of the research challenges is to design such hardware using less area to minimize the cost of on-chip implementation. This brief proposes an area-efficient implementation of an Artificial Neural Network (ANN). The proposed method reduces the number of layers in the ANN by nearly half through a novel, dual use of some layers denoted as hidden layers. The hidden layers are non-traditional layers proposed in this brief. They are adaptable, and each such layer performs two separate functions through judicious use of different weights. Thus, each hidden or flexible layer does the work of two traditional ANN layers. The other type of layers used in the proposed design is the fixed layers that are used traditionally. The fixed layers are not flexible and serve the functionality of a single layer. The proposed design keeps the number of the fixed layers as low as possible. One or more fixed layers may still be needed for some applications besides the proposed flexible layers. The proposed method is implemented in Verilog HDL on Altera Arria 10 GX FPGA. Its area usage is only 41% of the state-of-the-art method, while its power consumption and speed overheads are small.

Modeling the effect of sensor failure on the location of counting sensors for origin-destination (OD) estimation

The network sensor location problem (NSLP) for origin–destination (OD) estimation identifies the optimal locations for sensors to estimate the vehicular flow of OD pairs in a road network. Like other measurement apparatuses, these sensors are subject to failure, which can affect the reliability of the OD estimations. In this paper, we propose a novel model that allows us to solve the NSLP for OD demand estimation by identifying the most reliable locations to install sets of sensors with consideration for a nonhomogeneous Poisson process to account for time-dependent sensor failure. The proposed model does not rely on the assumption that true OD demand information is known. We introduce two separate objective functions to minimize the maximum possible information loss (MPIL) associated with OD demand on sensor-equipped links and OD pairs during the lifetimes of the sensors. Both objective functions are formulated to incorporate the possibility of sensor failure into the calculated OD demands. We use stochastic user equilibrium (SUE) to address the stochasticity of traffic route selection. We then employ the weighted sums method (WSM) and an ε-constraint to incorporate the objective functions into an integrated formulation. Two sensor types with different time-dependent failure rates are considered to identify the optimal locations for sets of sensors for OD demand estimation purposes while addressing the available budget constraints. We also address the problem of scheduled/routine maintenance of existing sensors by introducing an additional sensor deployment phase that focuses on maintaining the reliability of information by repairing or replacing failed sensors, installing additional sensors or a combination of both. The numerical results from the proposed model demonstrate how the deployment of more advanced sensors with lower failure rates can effectively improve the reliability of the information obtained from sensors. We also evaluate the use of different weights for the WSM’s objective functions to explore alternative combinations of sensor configurations. The introduction of additional sensors to a network shows that the decision between repairing failed sensors and installing new sensors is highly dependent on the available budget and the failed sensors’ locations.

The Use of Different Weights and Resistances and their Effect on Bending the Muscles of the Lower Limbs and the Achievement of the Long Jump Contestants for Al Diwaniyah Clubs in Athletics

The research included several topics, where the two researchers touched on the introduction and importance of the research in the first section, as all sources and research confirm the importance of using weights in training programs because of their importance in developing working muscles, especially in activities of jumping and jumping and thus reflected positively on achievement, as for the problem of research lies inferior The level of achievement for the effectiveness of the long jump, especially in the local championships, due to the lack of use of modern methods and means in the training curricula, especially the weighted ones, which negatively affected the achievement. The third was the methodology of the research, where the researchers used the experimental method to suit the nature of the problem. As for the research sample, they played the Diwaniyah clubs with the strong games of the long jump for the applicants and the participants in the Iraqi championships. As for the statistical methods, the researchers used the appropriate statistical means that are compatible with the research paragraphs. The results are according to a solid scientific method to reach the most important treatments and solutions, as for the mass The fifth urge was to include the most important conclusions and recommendations, as it became clear that weight training exercises an effective role on the angles of the body, the movement path, and the achievement.

Improving deep learning sorghum head detection through test time augmentation

The continuous growth of the world’s population requires immediate action to ensure food security. Sorghum is among the five most-produced cereals and is a dietary staple in many developing countries. Therefore, it is of great importance to obtain precise information for improving cereal productivity. An indicator for estimating sorghum yields is the number of crop heads in different branching arrangements. Approaches based on image processing and artificial intelligence have proved useful for automatically and efficiently obtaining this type of information for different crops. However, their application to sorghum crops presents some additional challenges owing to differences in the shape and color of sorghum heads. In this study, a methodology to detect sorghum heads in unmanned aerial vehicle imagery was investigated, and its performance was evaluated using a standard quality index in object detection problems (mean average precision). Specifically, test-time-augmentation (TTA) techniques have been implemented using a set of geometrical and color transformations selected according to the sorghum plant imagery requiring analysis, as well as four different ensemble learning methods. Because these methods are weighted, two different approaches for calculating these weights to improve sorghum head detection have been proposed. The results show that in sorghum head detection, TTA strategies outperform detection based only on individual transformed testing sets. Moreover, these results were improved by the use of different weights during the ensemble of TTA results.

The Saudi National Mental Health Survey: Sample design and weight development

ObjectivesTo describe the sample design and weighting procedures used in the Saudi National Mental Health Survey (SNMHS).MethodsA multistage clustered area probability design was used to select the SNMHS sample with one male and one female KSA citizen ages 15–65 surveyed in each sample household.ResultsA design representative of the household population was developed and modified iteratively to adjust for unanticipated field complications. These modifications, along with variation in within‐household probabilities of selection and geographic–demographic variation in response rates were accounted for through survey weights. Design‐based estimation methods were used to adjust for the effects of these weights and of geographic clustering. Design effects were estimated and simulations were carried out on bias‐variancetrade‐offs in weight trimming to evaluate the implication of design features for precision of estimates.ConclusionsThe multiple purposes of the survey will require the use of different weights for different types of analyses, including household and person weights as well as weights for proxy reports about household members whose disabilities prevented them from participating in the survey. It will be important to use these different weights appropriately in the diverse analyses that will be undertaken with the SNMHS data.

The Effect of Exercise Technique Altering with Use Heavyweight Rackets in the Development of Concentration (Sodium, Potassium) in the Blood and Smash Skill for Player's Badminton

The skill of beating is one of the most important offensive skills in the game of badminton, through which the player can decide the point in his favor, and given the diversity of the use of skills at one point and the speed of the large brush requires us as researchers to find the best technique to develop this skill, so researchers used The method of exercise altering dimensions and trends and different altitudes in addition to the use of different weights and weights of some of them are heavy and some of them are lighter, and researchers try to know this method on the concentration of functional alterings in blood and whether there was a burden and effort on the muscle.

Adaptive linear discriminant regression classification for face recognition

Linear discriminant regression classification (LDRC) was presented recently in order to boost the effectiveness of linear regression classification (LRC). LDRC aims to find a subspace for LRC where LRC can achieve a high discrimination for classification. As a discriminant analysis algorithm, however, LDRC considers an equal importance of each training sample and ignores the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, in this paper, we propose an adaptive linear discriminant regression classification (ALDRC) algorithm by taking special consideration of different contributions of the training samples. Specifically, ALDRC makes use of different weights to characterize the different contributions of the training samples and utilizes such weighting information to calculate the between-class and the within-class reconstruction errors, and then ALDRC seeks to find an optimal projection matrix that can maximize the ratio of the between-class reconstruction error over the within-class reconstruction error. Extensive experiments carried out on the AR, FERET and ORL face databases demonstrate the effectiveness of the proposed method.

A Novel Weighted Median Switching Filter for Denoising Corrupted Images

This paper presents a new weighted switching median filter is proposed to restore images corrupted by noise. The neighborhood of a pixel is classified into low corrupted, medium corrupted and highly corrupted regions. Based on this classification the weights associated with the median filter employed for denosing is determined. In addition, switching mechanism allows the use of different weights for different regions based on localized statistics. The proposed algorithm can be applicable to digital images with most of the fine details of the image are mostly preserved. The simulation analysis would shows that the proposed method is good for noise reduction and image restoration. General Terms Denoising, Security, Median Filter, Image Processing

Comparison of scoring methods for ACE-27: Simpler is better

OBJECTIVE: To examine the prognostic value of different comorbidity coding schemes for predicting survival of newly diagnosed elderly cancer patients. MATERIALS AND METHODS: We analyzed data from 8,867 patients aged 65 years of age or older, newly diagnosed with cancer. Comorbidities present at the time of diagnosis were collected using the Adult Comorbidity Evaluation-27 index (ACE-27). We examined multiple scoring schemes based on the individual comorbidity ailments, and their severity rating. Harrell's c index and Akaike Information Criterion (AIC) were used to evaluate the performance of the different comorbidity models. RESULTS: Comorbidity led to an increase in c index from 0.771 for the base model to 0.782 for a model that included indicator variables for every ailment. The prognostic value was however much higher for prostate and breast cancer patients. A simple model which considered linear scores from 0 to 3 per ailment, controlling for cancer type, was optimal according to AIC. CONCLUSION: The presence of comorbidity impacts on the survival of elderly cancer patients, especially for less lethal cancers, such as prostate and breast cancers. Different ailments have different impacts on survival, necessitating the use of different weights per ailment in a simple summary score of the ACE-27.

Correlation Between Skin Biopsy With Quantification of Intraepidermal Nerve Fiber and the Severity of Sciatic Nerve Traction Injury in Rats

To predict the severity of nerve traction injury by skin biopsy with quantification of intraepidermal nerve fibers density (IENFD). Traction injuries to the right sciatic nerve of Sprague-Dawley rats with the use of different weights (0, 25, 50, 100, 200, 400, and 600 g) were performed. Sham operations were performed on the left sciatic nerve. After a period of 7 days, IENFD derived from quantification of protein gene product 9.5-immunoreactive intraepidermal nerve fibers of the hindpaw skin were calculated to compare with numbers of intact teased fibers of tibia nerve. There were 100% +/- 0%, 94% +/- 5%, 52% +/- 8%, 48% +/- 6%, 42% +/- 7%, 9% +/- 3%, and 0% +/- 0% of teased nerve fibers remained intact after traction injury with 0 g, 25 g, 50 g, 100 g, 200 g, 400 g, and 600 g weights, respectively. When compared with IENFD of the control rats (ipsilateral paw: 21.6 +/- 0.6; contralateral paw: 20.1 +/- 0.8 fibers/mm), there were loss of nearly half of the small fibers after 50 g, 100 g, and 200 g weight traction injury in ipsilateral paw (10.1 +/- 1.2, 7.4 +/- 1.5, and 8.3 +/- 2.2 fibers/mm, respectively) and contralateral paw (11.4 +/- 0.9, 9.6 +/- 0.6, and 10.0 +/- 1.5 fibers/mm, respectively). After 400 g and 600 g weight traction injury, there was severe (0.2 +/- 0.2 fibers/mm) to complete destruction of the small fibers in ipsilateral paw and marked reduction in contralateral paw (5.5 +/- 0.7 and 4.3 +/- 0.7 fibers/mm, respectively). With the ability to classify the nerve traction injury into a mild, moderate, or severe injury, correlations can be made between skin biopsy with IENFD and the severity of nerve traction injury. However, the extent of nerve injury cannot be differentiated within group of moderate injury with the use of three different traction weights.

IMPROVING AN ALGORITHM FOR TOTAL ELECTRON CONTENT ESTIMATION: EXPERIMENTS FROM ESFAHAN PROVINCE

It is possible to use single frequency GPS receivers to estimate the Total Electron Content (TEC). In this research, we improved an algorithm presented by Giffard [2], that is based on a least squares solution. We investigated the effect of the use of different weights (elevation of satellites, signal to noise ratio, combination of elevation and signal to noise ratio) and different block sizes on TEC estimates. We found that these parameters had a significant impact on TEC estimates based on this algorithm. Our research is based on observations at the GPS site of the Esfahan University made with single frequency 12-channel Leica System 500 receivers.

Experimental model for evaluation of the cutaneous sensitivity of the abdomen

The purpose of this study is to report an experimental model to evaluate several modalities of sensibility and to map the areas of the abdominal wall with decreased sensibility after abdominoplasty. Patients were divided in two groups: in the control group, patients had no previous abdominal incisions and patients of the experimental group had been undergone abdominoplasty. The sensibility evaluation of patients from the experimental group was made from 12 to 60 months after the operation. The abdominal skin was divided into twelve areas; nine of them were above the abdominoplasty incision and three below it. Sensibility to superficial touch, superficial pain, hot and cold temperature, vibration was tested and recorded as positive if the patient mentioned that he felt the stimulation or negative. Sensibility to pressure was tested with the use of different weights; therefore, a value was obtained when the patient felt the pressure. A specific test was used to evaluate each modality of sensibility. The experimental model designed to test skin sensibility after abdominoplasty showed to be feasible in patients.

Ordering symmetric sparse matrices for small profile and wavefront

The ordering of large sparse symmetric matrices for small profile and wavefront or for small bandwidth is important for the efficiency of frontal and variable-band solvers. In this paper, we look at the computation of pseudoperipheral nodes and compare the effectiveness of using an algorithm based on level-set structures with using the spectral method as the basis of the Reverse Cuthill–McKee algorithm for bandwidth reduction. We also consider a number of ways of improving the performance and efficiency of Sloan's algorithm for profile and wavefront reduction, including the use of different weights, the use of supervariables, and implementing the priority queue as a binary heap. We also examine the use of the spectral ordering in combination with Sloan's algorithm. The design of software to implement the reverse Cuthill–McKee algorithm and a modified Sloan's algorithm is discussed. Extensive numerical experiments that justify our choice of algorithm are reported on. Copyright © 1999 John Wiley & Sons, Ltd.

Controllability Along a Trajectory: A Variational Approach

This paper unifies and improves most sufficient conditions of local controllability both along a trajectory and at a point. This is accomplished by defining high-order variations that can be continuously summed. The peculiar property of these variations is that they may be generated by thin conditions as relations in the Lie algebra associated with a control system. This property leads to a variational interpretation of Sussmann-type sufficient conditions of local controllability (neutralization of obstructions), and it allows the use of different weights for neutralization.

Beta weights, unit weights, and other weights.

A simple example of the use of different weights with just two predictors was presented. The replacement of the beta weights by unit weights resulted in a very slight loss of predictive validity, and even the arbitrary reversal of the beta weights resulted in a smaller loss than might have been expected. A relationship demonstrated by Green was used to help explain these results. In many situations, it appears worthwhile to follow the approach taken here to determine how great a loss of predictive validity would result from the use of unit weights in place of beta weights.