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Intermittent fasting (IF) is an increasingly popular dietary approach used for weight loss and overall health. While there is an increasing body of evidence demonstrating beneficial effects of IF on blood lipids and other health outcomes in the overweight and obese, limited data are available about the effect of IF in athletes. Thus, the present study sought to investigate the effects of a modified IF protocol (i.e. time-restricted feeding) during resistance training in healthy resistance-trained males.
Thirty-four resistance-trained males were randomly assigned to time-restricted feeding (TRF) or normal diet group (ND). TRF subjects consumed 100 % of their energy needs in an 8-h period of time each day, with their caloric intake divided into three meals consumed at 1 p.m., 4 p.m., and 8 p.m. The remaining 16 h per 24-h period made up the fasting period. Subjects in the ND group consumed 100 % of their energy needs divided into three meals consumed at 8 a.m., 1 p.m., and 8 p.m. Groups were matched for kilocalories consumed and macronutrient distribution (TRF 2826 412.3 kcal/day, carbohydrates 53.2 1.4 %, fat 24.7 3.1 %, protein 22.1 2.6 %, ND 3007 444.7 kcal/day, carbohydrates 54.7 2.2 %, fat 23.9 3.5 %, protein 21.4 1.8). Subjects were tested before and after 8 weeks of the assigned diet and standardized resistance training program. Fat mass and fat-free mass were assessed by dual-energy x-ray absorptiometry and muscle area of the thigh and arm were measured using an anthropometric system. Total and free testosterone, insulin-like growth factor 1, blood glucose, insulin, adiponectin, leptin, triiodothyronine, thyroid stimulating hormone, interleukin-6, interleukin-1β, tumor necrosis factor α, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides were measured. Bench press and leg press maximal strength, resting energy expenditure, and respiratory ratio were also tested.
Our results suggest that an intermittent fasting program in which all calories are consumed in an 8-h window each day, in conjunction with resistance training, could improve some health-related biomarkers, decrease fat mass, and maintain muscle mass in resistance-trained males.
ND subjects were instructed to consume the entire breakfast meal between 8 a.m. and 9 a.m., the entire lunch meal between 1 p.m. and 2 p.m., and the entire dinner meal between 8 p.m. and 9 p.m. TRF subjects were instructed to consume the first meal between 1 p.m. and 2 p.m., the second meal between 4 p.m. and 5 p.m., and the third meal between 8 p.m. and 9 p.m. No snacks between the meals were allowed except 20 g of whey proteins 30 min after each training session. Every week, subjects were contacted by a dietician in order to check the adherence to the diet protocol. The dietician performed a structured interview about meal timing and composition to obtain this information.
Training was standardized for both groups, and all subjects had at least 5 years of continuous resistance training experience prior to the study. Training consisted of 3 weekly sessions performed on non-consecutive days for 8 weeks. All participants started the experimental procedures in the months of January or February 2014.
Previous studies have reported mixed results concerning the ability to maintain lean body mass during IF, but the vast majority of these studies imposed calorie restriction and did not utilize exercise interventions [22]. In our study, the nutrient timing related to training session was different between the two groups, and this could affect the anabolic response of the subjects [61] even though these effects are still unclear [62]. However, we did not find any significant differences between groups in fat-free mass, indicating that the influence of nutrient timing may be negligible when the overall content of the diet is similar.
In conclusion, our results suggest that the modified IF employed in this study: TRF with 16 h of fasting and 8 h of feeding, could be beneficial in resistance trained individuals to improve health-related biomarkers, decrease fat mass, and at least maintain muscle mass. This kind of regimen could be adopted by athletes during maintenance phases of training in which the goal is to maintain muscle mass while reducing fat mass. Additional studies are needed to confirm our results and to investigate the long-term effects of IF and periods after IF cessation.
While conventional optimization involves isolated studies of facilities, wells, and reservoirs, integrated asset modeling (IAM) brings together all of the interested parties (reservoir, well inflow, wellbore, pipeline, and compressor models) to create an integrated, optimal solution based on realistic projections consistent with facilities, well, and reservoir constraints. Integrated asset modeling (IAM) software models can create complete production system from reservoir to surface network.
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The prediction for Insurance premium works as follows. You have some features/input (age, gender, smoking, etc.) which go into the training process. These features can determine how high or low is the premium amount. For example, the younger people are less likely to need medical care, so their premiums are generally cheaper, and the premium is higher for smokers. The Label/Output is the Premium/Price that you want to predict, which is the outcome of calling the Machine Learning Model.
In the Program class, add two constants, TRAIN_DATA_FILEPATH and MODEL_FILEPATH. The first contains the path to the dataset; the latter contains the path to where the model will be saved once training is complete.
Create an MLContext instance. MLContext is a beginner class for all ML.NET operations. It provides all required components to load and prepare data, training, evaluation, and model prediction. MLContext has one optional parameter called a seed. By default, the seed value is null, and MLContext environment is nondeterministic, and the output changes across multiple runs. If you provide a fixed number, then the environment becomes deterministic, and the result is consistent across multiple runs.
After the MLContext instance is created, you need to load data using DataOperationsCatalog. This class is used to create components that operate on data, load, save, cache, filter, shuffle, and split input data. But this is not part of the model training pipeline.
You will need to find the best-performing model with the help of AutoML. Below is the code to explore multiple models. More extended training periods allow AutoML to explore more models and give better accuracy for the machine learning model.
After running the experiment, AutoML explored different training models. Below is the console output window that shows the models evaluated and suggesting the best model: LightGbmRegression for the price prediction based on the input data.
Have we made an effort to determine the right level of employee retention Rarely is the right level of retention 100%. Dynamic service organizations require a certain level of turnover. However, in calibrating desired turnover levels, it is important to take into account the full cost of the loss of key service providers, including those of lost sales and productivity and added recruiting, selection, and training.
The Volkswagen emissions scandal, sometimes known as Dieselgate[23][24] or Emissionsgate,[25][24] began in September 2015, when the United States Environmental Protection Agency (EPA) issued a notice of violation of the Clean Air Act to German automaker Volkswagen Group.[26] The agency had found that Volkswagen had intentionally programmed turbocharged direct injection (TDI) diesel engines to activate their emissions controls only during laboratory emissions testing, which caused the vehicles' NOx output to meet US standards during regulatory testing. However, the vehicles emitted up to 40 times more NOx in real-world driving.[27] Volkswagen deployed this software in about 11 million cars worldwide, including 500,000 in the United States, in model years 2009 through 2015.[28][29][30][31]
The scandal raised awareness over the higher levels of pollution emitted by all diesel-powered vehicles from a wide range of car makers, which under real-world driving conditions exceeded legal emission limits. A study conducted by ICCT and ADAC showed the biggest deviations from Volvo, Renault, Jeep, Hyundai, Citroën and Fiat,[46][47][48] resulting in investigations opening into other diesel emissions scandals. A discussion was sparked on the topic of software-controlled machinery being generally prone to cheating, and a way out would be to open source the software for public scrutiny.[49][50][51]
The emissions far exceeded legal limits set by both European and US standards. One of the testers said, \"... we did so much testing that we couldn't repeatedly be doing the same mistake again and again.\"[96][97] John German said the deceit required more effort than merely adding some code to the engine software, as the code would also have to be validated.[96] The US test results confirmed the ICCT's findings in Europe.[93] ICCT also purchased data from two other sources. The new road testing data and the purchased data were generated using Portable Emissions Measurement Systems (PEMS) developed by multiple individuals in the mid-late 1990s and published in May 2014.[87][93][90] The West Virginia scientists did not identify the defeat device, but they reported their findings in a study they presented to the EPA and CARB in May 2014.[98][99] In May 2014 Colorado's RapidScreen real-world emissions test data reinforced the suspected abnormally high emissions levels.[100] After a year-long investigation, an international team of investigators identified the defeat device as a piece of code labelled \"acoustic condition\" which activated emissions-curbing systems when the car's computer identified it was undergoing a test.[101] 153554b96e
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