A Proposal on the Novel Method to Estimate Optimal Sleep Duration Based on Self-Reported Survey Data

Background: Many adolescents have a sleep debt. Individuals sleeping for their optimal sleep duration are expected to experience no sleepiness. Then, it is important to recognize one’s optimal sleep duration to reduce sleep debt. However, there is no simple method to determine this value. Since body mass index and sleep duration exhibit a U-shaped association, it is expected that a person taking optimal sleep duration would show no marked deviation from the mean body mass index value for the population evaluated. By using self-reported sleepiness and standardized body mass index, this study aimed to estimate individual optimal sleep duration. Methods: Data from 2540 grade 5 - 11 students were used. Students who declared no sleepiness during class and also had a gender- and grade-standardized body mass index of ±1.5 were termed ideal students. The average sleep durations of ideal students were compared with those of non-ideal students. The differences of sleep duration between ideal and no-ideal students were added to habitual sleep duration of each non-ideal student to obtain assumed optimal sleep duration. A multiple regression line to predict assumed optimal sleep duration was calculated using the least squares method. Results: The mean sleep duration of 666 ideal students exceeded the lower limit of daily sleep duration proposed as “may be appropriate” for children aged 6 - 17 years by National Sleep Foundation of the USA, being longer than those of non-ideal students. Significant regression formula for assumed optimal sleep duration was obtained (adjusted R2 = 0.996, p Conclusions: No contradiction was identified in the sleep duration obtained from ideal students as with optimal sleep duration. Although further studies to confirm the current estimation are needed, a simple formula to estimate individual optimal sleep duration through easily obtainable parameters was proposed.

Indoor smart farming by inducing artificial climate for high value-added crops in optimal duration

The global population is increasing rapidly as compared to food production;approximately three times more food would be required in 2050.Climate change affects crop production by causing sudden changes in weather conditions,including rain,storms,heat waves,doughiness,and water shortages.Farming with smart technology provides a productive solution.Smart farming is a productive solution that provides a great resource of income and improves the countries’economy by exporting consumable goods and preventing food security problems.Smart agriculture provides a combination of flexibility,remote access,and automation through the use of intelligent control technologies.Many countries are working towards smart and intelligent agriculture farming that analyzes crop,soil fertility,pests and weeds,and other problems caused by mismanagement and incompetence.However,smart agricultural farming is less widely adopted in agriculture as a result of high costs and little understanding of technology.In this study,An artificial climate control chamber(ACCC)was designed for cultivating plants by controlling the optimal parameters,especially the light spectrum.In ACCC,influential plant factors such as light,moisture,humidity,and fertilizer concentration have been controlled intelligently.Light spectrum was controlled by time periods in the previous system,while in the system proposed in this study,the light was controlled by image processing.In an artificial control chamber,the plant growth stages have been determined through image processing techniques.Datasets of image images have been used to organize specific intensities of the light spectrum.This intelligent system provides aid in the speed breeding procedure through variant spectrums of light and fertilizers combinations.In the research study,the yield and quality of intelligent farming are enhanced.

Optimization of Injection Timing and Injection Duration of a Diesel Engine Running on Pure Biodiesel SME (Soya Methyl Ester)

This study was carried out to predict the impact of injection timing and injection duration on engine brake power and Nitrogen Oxides emissions in a diesel engine using biofuel Soya Methyl Ester (SME). Predictions were accomplished at three different injection timings 10°, 5° Crank Angle (CA) before Top Dead Center (bTDC) and 0° CA at Top Dead Center (TDC) and four injection durations 20°, 25°, 30°, 35° CA. The study was conducted using a simulation software (Diesel-RK). The predicted results showed that the powers produced by all the setups of the different injection timings are almost equal, but they differ in injection durations, e.g. the power at setup (10° CA-bTDC) duration 20° CA and 2500 rpm equal to 52 kW, at setup (5° CA-bTDC) duration 25° CA and same engine speed the power is equal to 51 kW, and at setup (0° CA-TDC) durations 30° the power is equal to 51 kW. The power in all setups are decreased as the injection duration increased, e.g. at setup 0° CA TDC durations 25°, 35°, and 40° CA and at 4000 rpm, the brake powers are equal 71, 65, and 59 kW respectively, thus the reduction percentages are 9% and 17% when compared to the 25° injection duration. The nitrogen oxides emissions decreased as the injection duration is increased, e.g. the emissions at setup (10° CA-bTDC) durations 25°, 30°, and 40° CA and at 2500 rpm are equal 852, 589, 293 ppm respectively, the reduction percentages are 30% and 72%. The variations of injection timing and injection duration have taken a weighty influence on engine performance and emissions. The results are considered as a novelty in the field of using pure biofuel Soya Methyl Ester in diesel engine according to our information.

Perimeter traffic control strategy based on macroscopic fundamental diagrams

A perimeter traffic signal control strategy is proposed based on the macroscopic fundamental diagram theory(MFD)to solve the signal control problem in oversaturated states.First,the MFD of a specific regional network can be derived using VSS IM simulation software.Secondly,the maximum number of cumulative vehicles that the network can accommodate is determined based on the MFD.Then,through monitoring the influx flow,the number of vehicles existing in and exiting from the network,a perimeter traffic control model is proposed to optimize the signal timing of the boundary intersections.Finally,a virtual network simulation model is established and three different kinds o f traffic demand are loaded into the network.Simulation results show that a fer the strategy implementation,the number o f vehicles accumulating in the network can be kept near the optimal value,while the number o f both entering and exiting vehicles increases significantly and the road network can be maintained at a large capacity.Simultaneously,the queue length at the approach of the border intersections is reasonably controlled and vehicles entering and exiting the network can maintain a more efficient and stable speed.The network performance indices such as the average traffic delay and average number of stops can be improved to a certain degree,thus verifying the effectiveness and feasibility of the perimeter control strategy.