Development of a duck weight estimation system using Vision Transformer and weighted random sampling

The safe and effective administration of fluids and medications during the management of medical emergencies in children depends on an appropriately determined dose, based on body weight. Weight can often not be measured in these circumstances and a convenient, quick and accurate method of weight estimation is required. Most methods in current use are not accurate enough, but the newer length-based, habitus-modified (two-dimensional) systems have shown significantly higher accuracy. This meta-analysis evaluated the accuracy of weight estimation systems in children. Articles were screened for inclusion into two study arms: to determine an appropriate accuracy target for weight estimation systems; and to evaluate the accuracy of existing systems using standard meta-analysis techniques. There was no evidence found to support any specific goal of accuracy. Based on the findings of this study, a proposed minimum accuracy of 70% of estimations within 10% of actual weight (PW10 > 70%), and 95% within 20% of actual weight (PW20 > 95%) should be demonstrated by a weight estimation system before being considered to be accurate. In the meta-analysis, the two-dimensional systems performed best. The Mercy method (PW10 70.9%, PW20 95.3%), the PAWPER tape (PW10 78.0%, PW20 96.6%) and parental estimates (PW10 69.8%, PW20 87.1%) were the most accurate systems investigated, with the Broselow tape (PW10 55.6%, PW20 81.2%) achieving a lesser accuracy. Age-based estimates achieved a very low accuracy. Age- and length-based systems had a substantial difference in over- and underestimation of weight in high-income and low- and middle-income populations. A benchmark for minimum accuracy is recommended for weight estimation studies and a PW10 > 70% with PW20 > 95% is suggested. The Mercy method, the PAWPER tape and parental estimates were the most accurate weight estimation systems followed by length-based and age-based systems. The use of age-based formulas should be abandoned because of their poor accuracy.

Paediatric weight estimation practices of advanced life support providers in Johannesburg, South Africa.

One of the most important parameters that must be obtained when resuscitating a critically ill or injured paediatric patient is their weight. The best known paediatric weight estimation system is arguably the Broselow Tape, but the tape has been shown to be very inaccurate. The aim of this study was to determine and compare the accuracy of the Broselow tape, a modified Broselow tape system, the PAWPER XL tape and the hanging leg weight technique for potential utilisation in the paediatric resuscitation setting. A convenience sample of 200 children between the ages of 1 month and 16 years were enrolled. All the children's weights were estimated using the Broselow tape, a habitus-modified Broselow tape system, the PAWPER XL tape and the hanging leg weight technique. Overall accuracy was evaluated using the percentage of weight estimations falling within 10% of actual weight (PW10). The PAWPER XL tape performed the best, whilst the hanging leg weight technique performed the poorest with PW10s of 74% and 19.5%, respectively. The Broselow tape with and without habitus-modification only showed modest accuracy, with PW10s of 61.7% and 59.1% respectively. The PAWPER XL tape performed significantly better than other weight estimation systems and is most appropriate for use in South African paediatric emergencies. The habitus-modified Broselow system produced only modest improvement in overall weight estimation accuracy of the Broselow tape.

The accuracy of paediatric weight estimation during simulated emergencies: The effects of patient position, patient cooperation, and human errors.

The system is based on pig study, in order to facilitate the measurement of pig body weight, coupled with development of information technology. it uses a non-contact measurement means. Detection of pigs with body size and weight estimate system requirements, to B / S architecture based system software design and development. Java programming language using image processing pig, Using MySQL database technology to achieve results in pigs weighing scale data storage and other related, in the MyEclipse platform to achieve detection of body size and weight of pig forecast system testing. Pig by digital image processing and analysis, to achieve the automatic identification of pigs feet and detection. The system of agricultural development in China is significant.

Development and validation of a length- and habitus-based method of total body weight estimation in adults.

IntroductionDuring medical emergencies in children, accurate and appropriate weight estimations may ultimately influence the outcome by facilitating the delivery of safe and effective doses of medications. Children at the extremes of habitus, especially obese children, are more at risk of an inaccurate weight estimation and therefore may be more at risk of medication errors. The objective was therefore to develop an algorithm to guide accurate emergency weight estimation in obese children.MethodsRelevant medical evidence was reviewed regarding weight estimation and its role and timing in the resuscitation of obese children. This was used as the basis for a weight-estimation algorithm.ResultsThere was limited evidence regarding the way the weight-estimation systems should be used in obese children other than that the dual length- and habitus-based systems were the most accurate. The methods included in the algorithm were the Broselow tape, the Mercy method, parental estimates, the paediatric advanced weight prediction in the emergency room/ eXtra Length-eXtra Large (PAWPER XL) tape and the Traub-Johnson formula. The algorithm recognised several ways in which weight estimation could be tailored to the clinical scenario to estimate both ideal and total body weight.ConclusionWeight-estimation in obese children must be conducted appropriately to avoid medication errors. This algorithm provides a framework to achieve this.

ImportanceThe PAWPER tape system is one of the three most accurate paediatric weight estimation systems in the world. The latest version of the tape, which does not rely on a subjective assessment of habitus, is the PAWPER XL-MAC method which uses length and mid-arm circumference (MAC) to estimate weight. It was derived and validated in a population in the USA and has not yet been fully validated in a population from a resource-limited setting.ObjectiveThe objective of this study was to evaluate the performance of the PAWPER XL-MAC tape weight estimation system in a large dataset sample of children from resource-limited settings.MethodsThis was a “virtual” study in which weight estimates were generated using the PAWPER XL-MAC tape and Broselow tape 2007B and 2011A editions in a very large open access dataset. The dataset contained anthropometric information of children aged 6 to 59 months from standardised nutritional surveys in 51 low- and middle-income countries. The performance of PAWPER XL-MAC method was compared with the Broselow tape and a new length- and habitus-based tape, the Ralston method.Main outcomes and measuresThe bias of the weight estimation methods was assessed using the mean percentage error (MPE) and precision using the 95% limits of agreement (LOA) of the MPE. The overall accuracy was denoted by the percentage of weight estimates falling within 10% and 20% of actual weight (abbreviated as p10 and p20 respectively).ResultsThe MPE (LOA) for the PAWPER XL-MAC tape, the Broselow 2007B and 2011A and Ralston method were 1.9 (-15.3, 19.2), 5.4 (-15.9, 26.7), 7.7 (-13.3, 30.5) and -0.7 (-20.2, 19.3) respectively. The p10 and p20 for each method were 79.3% and 96.9% for the PAWPER XL-MAC tape, 64.3% and 91.0% for the Broselow tape 2007B, 55.5% and 85.9% for the Broselow tape 2011A and 67.4 and 94.0% for the Ralston method respectively. The PAWPER XL-MAC system was statistically significantly more accurate than the Broselow tape 2011A, the Broselow tape 2007B and the Ralston method. The relative difference in accuracy (p10) was 43% (odds ratio 4.4 (4.4, 4.5), p<0.001), 23% (odds ratio 2.9 (2.8, 2.9), p<0.001) and 18% (odds ratio 1.8 (1.8, 1.8), p<0.001) compared to each method, respectively.Conclusions and relevanceThe PAWPER XL-MAC tape performed well in this study and was statistically significantly more accurate than both the Broselow tape editions and the Ralston method. This difference was substantial and clinically important. The tape did not perform as well at extremes of habitus-type, however, and might benefit from recalibration.

Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system’s ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment.

Smart Cattle is a suite of technologies for boosting the quality and quantity of production in dairy farms. Measuring cattle’s live weight is essential as the captured data could be used to monitor cattle’s health and nutrition management. However, weighting cattle and consistently monitoring its weights of is time consuming and require intensive labour. Profitability of beef production system depends highly on the annual live weight gain, stocking rate, feed quality, feed quantity, and the balance between these factors. The lighter cattle causes farmers to lose profit. In feedlot, where the feed can be systematically rationed, this problem can be mitigated with feeding correction. The limitation of this approach is to monitor the live-weight and growth rate of the cattle, as the farmers need to weigh the animals at consistent interval such as once in a month during the production period. This task is laborious, time consuming and costly. Hence, the aim of this work is to develop a live weight estimation system that will be able to perform a physical weight estimation of a cattle using a deep learning approach. The objectives of this work are to design a deep learning model, specifically a Convolutional Neural Network (CNN) for cattle weighting estimation system based on features extracted from the images, to implement an optimized deep learning model for cattle physical weight estimation based on high resolution images and finally to evaluate the performance of the proposed cattle weight estimation system using a deep learning model. This approach can be used to replace traditional methods of weighing using electronic scale and direct observation and measurement can be made based on the cattle images. In this work, the impact of dropout rates on the performance of convolutional neural networks will also be studied for image classification. Based on the results obtained, the proposed CNN model with dropout regularization produced higher performance accuracy of 54.0%.

This paper presents a parametric classification methodology to identify common indoor and outdoor furniture objects present in the 3D Cartesian point cloud of the surveyed environment. For this purpose, a low cost custom made trolley based scanning and surveying system has developed using orthogonal integration of two popular Hokuyo-30LX 2D laser scanners. The developed system has been successfully used to generate 3D point cloud of the environment using Simultaneous Localization and Mapping (SLAM) technique. The instrumentation system of the trolley has been interfaced through Robot Operating System (ROS) for online processing and recording of all sensorial data. While classification of the furniture present in point cloud has been done in offline mode using Random Sampling and Consensus (RANSAC) based parametric segmentation technique. The innovative furniture detection has applied on each scan in order to reduce the region of interest in the developed point cloud. In addition, the validation of the classified furniture objects has been performed using Fuzzy Logic. Multiple indoor and outdoor vicinities have been scanned and modelling results have been found accurate nearer to ground truth. In comparison to available surveying solutions present in the local market, the developed system has been found faster and precise to produce more enhanced structural results with minute details.

As large-scale and intensive fattening pig farming has become mainstream, the increase in farm size has led to more severe issues related to the hierarchy within pig groups. Due to genetic differences among individual fattening pigs, those that grow faster enjoy a higher social rank. Larger pigs with greater aggression continuously acquire more resources, further restricting the survival space of weaker pigs. Therefore, fattening pigs must be grouped rationally, and the management of weaker pigs must be enhanced. This study, considering current fattening pig farming needs and actual production environments, designed and implemented an intelligent sorting system based on weight estimation. The main hardware structure of the partitioning equipment includes a collection channel, partitioning channel, and gantry-style collection equipment. Experimental data were collected, and the original scene point cloud was preprocessed to extract the back point cloud of fattening pigs. Based on the morphological characteristics of the fattening pigs, the back point cloud segmentation method was used to automatically extract key features such as hip width, hip height, shoulder width, shoulder height, and body length. The segmentation algorithm first calculates the centroid of the point cloud and the eigenvectors of the covariance matrix to reconstruct the point cloud coordinate system. Then, based on the variation characteristics and geometric shape of the consecutive horizontal slices of the point cloud, hip width and shoulder width slices are extracted, and the related features are calculated. Weight estimation was performed using Random Forest, Multilayer perceptron (MLP), linear regression based on the least squares method, and ridge regression models, with parameter tuning using Bayesian optimization. The mean squared error, mean absolute error, and mean relative error were used as evaluation metrics to assess the model’s performance. Finally, the classification capability was evaluated using the median and average weights of the fattening pigs as partitioning standards. The experimental results show that the system’s average relative error in weight estimation is approximately 2.90%, and the total time for the partitioning process is less than 15 s, which meets the needs of practical production.

The title of this research is “The Excecutives Participation and System Complexity Concerning The Development of Information System at the construction Company in Mataram City” which is intended for knowing the simultaneously and partially influence between excecutive’s participation and system complexity concerning the development of information system. &#x0D; The sample determined which is used in this research is the stratified random sampling method with 56 people samples. The hypothetic test which used is the double linier regression. The F test is used for knowingthe influence of independent variable concerning the dependent variable concerning the dependent variable partially. &#x0D; Based on the analysis which have done before, known that the excecutive’s participation and system complexity variable is influenced simultaneously concerning the development of information system. The result of second hypothetic analysis showed that the eexecutive’s participation had influenced partially concerning the development of information system, while system complexity had it.&#x0D; Based on the research result that have gotten, the next researcheris hoped to develop more sample or to add the other variable which is predicted that it has influence ininformation system development, like the communication of user developerand user affect and take a sample industry building on process (continues product).

In this paper, we extend the development of a weigh- in- motion (WIM) system for use on in-service highway bridges. We consider the problem of using a bridge's elastic response due to a passing truck to estimate the unknown truck parameters axle spacing, speed, axle weights, and gross weight by minimizing the difference between measured deflection profiles and approximated ones. For this problem, the bridge is modeled as a static Euler beam, and the truck is modeled as two quarter cars which include a linear representation of the suspension dynamics. A general model of the truck is discussed which is a superposition of the solutions of the truck's equations of motion with parameters such as weight, natural frequencies, dampings, and initial conditions unknown. Three deflection measurements are taken along the beam over time, and an optimization routine is employed to estimate the values of the unknown truck parameters. A variety of different truck configurations are considered. With zero measurement noise in the data, the errors in the weight estimates were 0.3%. Measurement noise of different magnitudes up to 10 microns was then added to the data and the estimates of all truck parameters were within 1.5%.

Background: Writing, implementing and evaluating a literacy programme is challenging particularly when the intervention is multi-faceted. In relation to literacy content, certain features have been shown to improve reading attainment (e.g. a systematic phonics programme); however, advice on how to integrate these strands into a whole is less clear. Furthermore, the success of an intervention is arguably as dependent on teacher quality as the literacy components. Evaluation of multi-faceted interventions can fail to explore the relative effects of different elements, whereas single strand interventions may be easier to measure but they can be atypical. It was with these thoughts in mind that the authors developed a literacy programme in North Lanarkshire, Scotland. Purpose: The paper evaluates the impact of a two-year literacy programme on attainment in reading and spelling. Programme description: The literacy programme ‘Think About It’ incorporated three strands: phonemic awareness and phonics instruction (strand 1), the development of semantic cueing systems (strand 2) and the use of metacognitive strategies to improve decoding and comprehension (metacomprehension, strand 3). The intervention was supported by continuing professional development, consultation and curriculum materials, parental involvement and by the deployment of early years' workers. Sample: The study took place in a socio-economically deprived local authority in Central Scotland. Sixteen mainstream primary schools in rural and urban areas took part in the intervention, which targeted children in their first two years of school (ages five and six). A random sample of children from each intervention class in the 16 pilot schools was assessed yearly. Additionally a random comparison sample of 10 children came from each class in the year previous to the intervention. Over the period of the study, approximately 480 intervention children were assessed. Design and methods: The study used a cross-sectional design over a period of four years, with standardised measures in each year. Each of the three intervention groups started in ensuing years. The longest established of these received the intervention for two years and were followed up for the next two years. The others followed this pattern as far as they were able. Because of the longitudinal nature of the study, the measures were not the same across all the years – they were changed to match the development of the children's reading skills. Questionnaires were used with staff to assess their views of the intervention. Results: Children's attainments in word reading, spelling and reading comprehension were significantly improved as result of the intervention. This was true not only at the end of the intervention, but at follow-up one and two years later. Conclusion: The intervention (of phonemic awareness and phonic instruction, the development of semantic and syntactical cueing systems, and the development of metacognitive strategies) was evidence based and did appear to work. The intervention was multi-faceted, but the relative efficacy of these different resource components is unknown. Recommendations for practice and future research are made.