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Jenny Godley,1 Nicole M Glenn,2 Arya M Sharma,3 John C Spence4 1Department of Sociology, University of Calgary, Calgary, AB, Canada; 2School of Public Health, Université de Montréal, Montreal, QC, Canada; 3Department of Medicine, 4Sedentary Living Laboratory, Faculty of Physical Education and Recreation, University of Alberta, Edmonton, AB, Canada Abstract: Students training in obesity research, prevention, and management face the challenge of developing expertise in their chosen academic field while at the same time recognizing that obesity is a complex issue that requires a multidisciplinary and multisectoral approach. In appreciation of this challenge, the Canadian Obesity Network (CON) has run an interdisciplinary summer training camp for graduate students, new career researchers, and clinicians for the past 8 years. This paper evaluates the effects of attending this training camp on trainees' early careers. We use social network analysis to examine the professional connections developed among trainee Canadian obesity researchers who attended this camp over its first 5 years of operation (2006–2010). We examine four relationships (knowing, contacting, and meeting each other, and working together) among previous trainees. We assess the presence and diversity of these relationships among trainees across different years and disciplines and find that interdisciplinary contact and working relationships established at the training camp have been maintained over time. In addition, we evaluate the qualitative data on trainees' career trajectories and their assessments of the impact that the camp had on their careers. Many trainees report that camp attendance had a positive impact on their career development, particularly in terms of establishing contacts and professional relationships. Both the quantitative and the qualitative results demonstrate the importance of interdisciplinary training and relationships for career development in the health sciences. Keywords: social network analysis, training, research collaboration, interdisciplinary

Jean-Eric Tarride1,2, Mahbubul Haq1, Valerie H Taylor3, Arya M Sharma4, Hamid Reza Nakhai-Pour1, Daria O’Reilly1,2, Feng Xie1,2, Lisa Dolovich2,5,6, Ron Goeree1,21Programs for Assessment of Technology in Health (PATH) Research Institute, St Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada; 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada; 3Department of Psychiatry, University of Toronto, Ontario, Canada; 4Department of Medicine, University of Alberta, Edmonton, Alberta, Canada; 5Centre for Evaluation of Medicines, St Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada; 6Department of Family Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, CanadaBackground: Obesity is today’s principal neglected public health problem, as a rising proportion of adults will succumb to the medical complications of obesity. However, little is known about the burden of obesity in adults living in Ontario.Objectives: To present an overview of the human and economic burden associated with BMI categories in Ontario, Canada, in terms of socio-demographics, comorbidities, health-related quality of life (HRQoL) and costs associated with hospitalization, same day procedures and physician visits.Methods: The records of all Ontarians who participated in the Canadian Community Health Survey (CCHS), cycle 1.1 and provided consent to data linkage were linked to three administrative databases. Socio-demographic variables, medical characteristics, HRQoL, one year hospitalization, day procedure and physician costs were described per BMI category. Regression analyses were conducted to identify predictors of medical characteristics, HRQoL and costs.Results: More than 50% of adult participants were either overweight or obese in 2000/2001. Obese adults, and to a lesser extent overweight adults, were more likely to report physician-diagnosed comorbid conditions, to use medications, and to have a lower HRQoL. After covariate adjustment, the hospitalization and physician costs were respectively 40% and 22% higher among obese and overweight adults than among normal-weight adults. No statistical cost differences were observed between normal and underweight individuals or between normal and overweight individuals. HRQoL was significantly lower in underweight and obese adults when compared to normal-weight individuals.Conclusions: Due to the large human and economic burden associated with under- or excess-weight, policies promoting healthy weight should remain a priority for governments and employers.Keywords: health-related quality of life, costs, body mass index categories, Ontario

The emerging field of molecular scatology enables critical testing of food web theory. The non-invasive application of molecular tools allows for sequencing of prey DNA from predator fecal matter, evaluating diet breadth and foraging guild. While insectivorous bats are obscure foragers compared to most insectivorous birds, more is known about which arthropod species bats consume because molecular techniques have been optimized for mammalian systems, not avian physiology. Our research objective was to use molecular tools to detect arthropod prey in the fecal matter of an insectivorous avian predator. We used Western Bluebird (Sialia mexicana) as a model predator due to its generalist foraging strategy. We compared two fecal DNA extraction kits: (1) Qiagen's DNA stool mini kits, used widely in dietary studies on bats and (2) Zymo's Soil/Fecal DNA MiniPrep kits, not currently cited in the molecular scatology literature. We successfully extracted DNA only with the Zymo kit, amplified mitochondrial cytochrome oxidase c subunit I genes, sequenced, and identified the arthropod prey. A spiked PCR experiment showed evidence of possible inhibitors remaining in the Qiagen kit extractions. Overall, arthropod prey from seven different orders and five different classes were identified. We discuss the ecological implications of these data and suggest areas of future research applying molecular techniques to avian fecal matter. Consistent methodological advancement will enable molecular scatology to identify ecosystem services provided by insectivorous birds, develop ecological theory, and inform predator conservation efforts. © 2013 Springer Science+Business Media Dordrecht.

Background and objectiveIn patients with coronary artery disease (CAD), obesity is paradoxically associated with better survival (the 'obesity paradox'). Our objective was to determine whether this counterintuitive relationship extends to health-related quality of life (HRQOL) outcomes.DesignCross-sectional observational study.SubjectsAll adults undergoing coronary angiography residing in Alberta, Canada between January 2003 and March 2006 in the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) registry.MethodsPatients completed self-reported questionnaires 1 year after their index cardiac catheterization, including the Seattle Angina Questionnaire (SAQ) and the EuroQol 5D (EQ-5D Index). Patients were grouped into six body mass index (BMI) categories (underweight, normal, overweight, mild obesity, moderate obesity and severe obesity). An analysis of covariance was used to create risk-adjusted scores.ResultsA total of 5362 patients were included in the analysis. Obese patients were younger than normal and overweight participants, and had a higher prevalence of depression and cardiovascular risk factors. In the adjusted models, SAQ physical function scores and the EQ Index (representing overall QOL) were significantly reduced in patients with mild, moderate and severe obesity compared with patients with a normal BMI. Patients with severe obesity had both statistically and clinically significant reductions in HRQOL scores. Depressive symptoms accounted for a large proportion in variability of all HRQOL scores.ConclusionsBMI is inversely associated with physical function and overall HRQOL in CAD patients, especially in patients with severe obesity. High body weight is a modifiable risk factor; however, given the apparent obesity paradox in patients with CAD, it is critical that future studies be conducted to fully clarify the relationships between HRQOL and body composition (body fat and lean mass), nutritional state and survival outcomes.

Agriculture encompasses the study, practice, and discipline of plant cultivation. Agriculture has an extensive history dating back thousands of years. Depending on climate and terrain, it began independently in various locations on the planet. In comparison to what could be sustained by foraging and gathering, agriculture has the potential to significantly increase the human population. Throughout the twenty-first century, precision farming (PF) has increased the agricultural output. precision agriculture (PA) is a technology-enabled method of agriculture that assesses, monitors, and evaluates the needs of specific fields and commodities. The primary objective of this farming method, as opposed to conventional farming, is to increase crop yields and profitability through the precise application of inputs. This work describes in depth the development and function of artificial intelligence (AI) and the internet of things (IoT) in contemporary agriculture. Modern day-to-day applications rely extensively on AI and the IoT. Modern agriculture leverages AI and IoT for technological advancement. This improves the accuracy and profitability of modern agriculture. The use of AI and IoT in modern smart precision agricultural applications is highlighted in this work and the method proposed incorporates specific steps in PF and demonstrates superior performance compared to existing classification methods. It achieves a remarkable accuracy of 98.65%, precision of 98.32%, and recall rate of 97.65% while retaining competitive execution time of 0.23 s, when analysing PF using the FAOSTAT benchmark dataset. Additionally, crucial equipment and methods used in PF are described and the vital advantages and real-time tools utilised in PA are covered in detail.

Layered composite materials are widely used across a variety of sectors, including the automotive industry, aerospace engineering, offshore, and various mechanical domains, because of their strong yet lightweight structures. Therefore, various emergent theories are available on the deformation of layered beams. The previous research studies are insufficient as they are based on deformation of layered composite and sandwich arches with simply supported (SS) end conditions. Therefore, it is a good opportunity for researchers to investigate the arches using exponential shear deformation and normal deformation theory. The leading hypothesis mainly adds to the research of bending for sandwich and layered composite arches adopting the exponential theory. The present theory does not require any shear correction factor to satisfy zero transverse shear stress condition at the bottom and top fibers of arches. Governing equations and associated end conditions are derived through principle of virtual work. Navier’s techniques used for sandwich and layered composite arches are SS boundary conditions subjected to uniformly distributed load. The results of the current study showed that the exponential normal and shear deformation theories may be used to evaluate static responses for layered composite and sandwich arches. The obtained results from the present theory are validated through the results available in published literature.

A detailed research on deep learning in renewable energy forecasting shows how sophisticated algorithms may improve prediction accuracy. The research explores deep learning models and finds intriguing aspects that improve predictions. Long Short-Term Memory (LSTM) networks can capture temporal relationships in energy data, making them successful in predicting short-term variations with a prediction accuracy boost of 18.18% over ARIMA. Convolutional Neural Networks (CNNs) capture spatial correlations in huge datasets with up to 13% accuracy. With its capacity to analyze sequential data, Recurrent Neural Networks (RNNs) can capture long-term patterns and improve forecasting accuracy by 29.41% over Support Vector Machines. In addition, LSTM’s better handling of non-linear connections in wind energy data has improved prediction accuracy by 14.29% over feedforward networks. These results demonstrate how deep learning approaches improve renewable energy forecasting with unparalleled precision and dependability. As shown in diverse applications, LSTM, CNN, and RNN models improve renewable energy forecasting efficiency and efficacy, boosting sustainable energy solution innovation.

Background: Distal femoral fractures represent a challenging injury, with many different factors such as the method of fixation, complexity of fracture pattern, and patient co-morbidities affecting the outcome. Lots of surgical treatment options have been described, but recently double construct fixation, using a nail/plate combination, has received lots of attention, a technique that leads to faster weight-bearing, low risk of metalwork failure, and non-union. The purpose of this study was to investigate the effectiveness of the linked nail/plate construct in the management of complex distal femur fractures and to investigate if the above technique leads to faster recovery and earlier radiographic union. Materials and methods: In total 15 cases were included in the study, that underwent a combined nail/plate construct for a distal femur fracture between January 2021 and December 2022. Only cases with a linked nail/plate construct were included, with a minimum follow-up of 1 year. Open femur fractures, single implant fixation cases, and revision procedures were excluded. Results: In this cohort study, 11 cases were periprosthetic distal femur features, and 4 cases were distal femur fractures around a native knee joint. The mean age group was 74 years, 86.6% of the patients had a BMI > 25 and the mean time to fracture union was 24 weeks (range from 20 to 26 weeks). All cases healed uneventfully and the complication rate was 6.6%, including 1 case of superficial infection which resolved completely with oral antibiotics. Conclusion: The increasing age population, the complexity of distal femoral fractures along with the increasing physiological demands of the elderly population, drive the need for double fixation constructs that allow early mobilization and enhance fracture stability. In our study, the linked nail/plate construct seems to provide adequate stability and excellent union rates (100%) with no associated increased risk of complications.

The COVID-19 outbreak has punctured every part of a person's life, along with how people perform, enjoy, study, work out, and interact with one another. The debunking industry has tried to lessen the enormous spread of false information in modern society due to the growth of social media. Current research investigates debunked fallacious content relating to COVID-19 in India, intending to understand the key themes and trends in fabricated content for health literacy. The study is exploratory and uses supervised machine learning for the purpose of classification. The study uses thematic analysis techniques to look at the 506 claims of false information about COVID-19 that Indian fact-checking platforms have refuted. The results discern specific trends and health literacy within the range. The tendency toward fallacious content starts with anger toward China and creates communal hatred against India's minority group. Political propaganda, then false health information, was the main subject of such content. Moreover, many misrepresented and refuted claims came from the mainstream media.

Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level.

To shorten the travel time and improve comfort, the automatic train driving system is considered to replace manual driving. In this article, an automatic control method of computer application data-processing system based on artificial intelligence is proposed. An automatic train operation (ATO) introduced the structure and function of an autopilot system (train), optimized the train running on the target curve, introduced the basic principle of fuzzy generalized predictive control (PC) algorithm, and combined with the characteristics of ATO system design the speed controller based on optimization algorithm, the target curve to make use of the designed controller to track and simulation validation. The experimental outcomes demonstrate that when the train runs to 90 s, the displacement difference reaches about 40 m, which proves that the fuzzy PC has better displacement tracking, punctual arrival, and higher stopping accuracy.

Advancement in engineering provides various improvement in quality life while taking consideration of important factors for safety and environment. The use of mower food maintenance of land it is very common across several parts of the world with some frequent noise generated through its operation. This article is an attempt to study the noise and frequency generated through the vibrations of mower blade. In this study, an integrated design for designing, testing and developing mower blade that generates less noise is presented. For designing efficient blade that produces less noise, we have implemented various engineering approaches such as rapid product design, process of re-engineering and reverse engineering. The simulation of the designed blade is carried out through CAD software where the design prototype is analysed for its performance. The outcomes of the prototype are tested through simulation and its performance is compared for the determination of success of proposed design at different variations in frequency level. It is observed through the experimentation that the noise and vibration differences are generated through load carrying vehicles, mowers with riding capacity and simple mowers. From the analysis, mower with riding capacity is observed as safest among all other types of machines.

The demand to improve image display in TFT-LCD, implementation of design for image processing is important. In order to meet the specific requirements of low-end Thin Film Transistor-Liquid-Crystal-Display (TFT-LCD) image display. This paper adopts a novel algorithm to conduct subsequent processing of the medical image after SCALER scaling, including contrast adjustment, gamma correction and dithering. Dithering algorithm is the focus of our research. After the study of some classical video image processing algorithms, and considering the real-time requirements, an intelligent algorithm is implemented for hardware implementation and improvement. For each part, MATLAB language is firstly used for advanced simulation to verify its feasibility, and then Right-To-Left (RTL) hardware language description is carried out. The characteristics extraction from images is performed implementing RGB standard and grayscale images. The pixel intensity is analyzed for each RGB component and the variance is calculated. When a panel displays a variation of 6% related with their reference values, the panel is rejected. The results obtained from classification shows a 95.24% accuracy rate in the detection of defects. The results of the two simulations show that the design achieves the expected goal, and the processing time is shorter.

A recent advent has been seen in the usage of Internet of things (IoT) for autonomous devices for exchange of data. A large number of transformers are required to distribute the power over a wide area. To ensure the normal operation of transformer, live detection and fault diagnosis methods of power transformers are studied. This article presents an IoT-based approach for condition monitoring and controlling a large number of distribution transformers utilized in a power distribution network. In this article, the vibration analysis method is used to carry out the research. The results show that the accuracy of the improved diagnosis algorithm is 99.01, 100, and 100% for normal, aging, and fault transformers. The system designed in this article can effectively monitor the healthy operation of power transformers in remote and real-time. The safety, stability, and reliability of transformer operation are improved.

In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

The current industrial environment is seeing a significant change towards sustainability and the integration of modern technology. Remanufacturing is developing as a crucial solution that intersects both developments. This manuscript presents a synopsis of the upcoming comprehensive research report that examines the ongoing paradigm shift observed in several businesses around the globe. The practise of remanufacturing, which involves the restoration of used products to a condition comparable to that of newly manufactured items, is increasingly becoming recognised as an environmentally viable alternative to conventional manufacturing methods. This study investigates the role of remanufacturing in facilitating resource conservation, waste reduction, and the establishment of a circular economy. This study examines the environmental advantages, economic feasibility, and regulatory structures that facilitate the adoption of remanufacturing. In conjunction with the principle of sustainability, advanced technological solutions are significantly transforming the field of remanufacturing practises. State-of-the-art technologies, including artificial intelligence, robots, additive manufacturing, and the Internet of Things (IoT), are being utilised to optimise and augment the remanufacturing process. The present study work aims to conduct an analysis of the integration of various technologies, with a particular focus on evaluating their impact on efficiency, quality, and cost-effectiveness. This paper will examine how industries from diverse sectors are adopting remanufacturing as a central approach to achieve a more environmentally sustainable and technologically advanced future. The analysis will be supported by case studies and real-world examples.

The understanding of sustainable remanufacturing as an innovative method has come about as a solution to the ecological difficulties posed by industrial manufacturing. The present study investigates the concept of industrial reinvention via a review of novel practices in the field of remanufacturing. Remanufacturing is an organizational strategy that seeks to increase the operational lifecycle of items, hence reducing the production of waste and maximizing resource use. The remanufacturing process includes a thorough set of phases, involving removal, repair, and enhancement, with the goal of rebuilding already utilized products to their former functionality as well as performance standards. This study examines the current state of procedures, methods, and strategies that contribute to the evolution of the remanufacturing operation in an environmentally friendly form. The abstract underlines the urgent requirement for sustainable solutions in industrial production as a response to problems with the environment. The idea of remanufacturing has been suggested as an effective way to solve these issues. This recent discussion presents an easy-to-understand representation of the remanufacturing process, emphasizing its essential relevance in increasing the lifespan of goods and decreasing the production of waste. The subsequent section of the abstract describes the primary objective of the research, which is the investigation of novel methods in the field of green remanufacturing. The paper aims to investigate multiple methods, tools, and strategies that are currently impacting the emergence of remanufacturing companies.