Objectives:Cycling economy is associated with muscle strength in athletes.However,the relationship between strength capacity(i.e.maximal and explosive strength)and cycling economy in previously untrained but healthy i...Objectives:Cycling economy is associated with muscle strength in athletes.However,the relationship between strength capacity(i.e.maximal and explosive strength)and cycling economy in previously untrained but healthy individuals remains unclear.Therefore,this study aimed to assess the associations between cycling economy and strength performance in a population of recreationally active but untrained healthy individuals.Methods:A total of 155 recreationally active individuals(95 males and 60 females)were included.Strength capacity was assessed through an incremental one-repetition maximum test,from which the one-repetition maximum,mean propulsive velocity,and mean propulsive power were derived as strength indices.Cycling economy was assessed using a step protocol on a cycle ergometer and gross oxygen cost and caloric unit cost were determined at submaximal intensities.Results:Marginal R^(2) ranged between 0.013 and 0.062 for the gross oxygen cost and between 0.022 and 0.103 for the gross caloric unit cost,respectively.Greater cycling economy is related to higher strength levels.However,the relationship is relatively weak,explaining only 1.3–6.2%of the variance in gross oxygen cost and 2.2–10.3%of the variance in gross caloric unit cost.Conclusions:Greater cycling economy in recreationally active males and females is related to higher strength levels(i.e.one-repetition maximum,mean propulsive velocity,mean propulsive power).展开更多
Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consum...Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly det展开更多
文摘Objectives:Cycling economy is associated with muscle strength in athletes.However,the relationship between strength capacity(i.e.maximal and explosive strength)and cycling economy in previously untrained but healthy individuals remains unclear.Therefore,this study aimed to assess the associations between cycling economy and strength performance in a population of recreationally active but untrained healthy individuals.Methods:A total of 155 recreationally active individuals(95 males and 60 females)were included.Strength capacity was assessed through an incremental one-repetition maximum test,from which the one-repetition maximum,mean propulsive velocity,and mean propulsive power were derived as strength indices.Cycling economy was assessed using a step protocol on a cycle ergometer and gross oxygen cost and caloric unit cost were determined at submaximal intensities.Results:Marginal R^(2) ranged between 0.013 and 0.062 for the gross oxygen cost and between 0.022 and 0.103 for the gross caloric unit cost,respectively.Greater cycling economy is related to higher strength levels.However,the relationship is relatively weak,explaining only 1.3–6.2%of the variance in gross oxygen cost and 2.2–10.3%of the variance in gross caloric unit cost.Conclusions:Greater cycling economy in recreationally active males and females is related to higher strength levels(i.e.one-repetition maximum,mean propulsive velocity,mean propulsive power).
文摘Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly det
