Response of Annual Herbaceous Plant Leaching and Decomposition to Periodic Submergence in Mega-Reservoirs: Changes in Litter Nutrients and Soil Properties for Restoration

Litter decomposition is an important soil nutrient source that promotes vegetation in deteriorated riparian zones worldwide. The periodic submergence and sediment burial effects on two prominent annual herbaceous plants (Echinochloa crusgali and Bidens tripartite) are little known in mega-reservoir settings. Our study focuses on the mass and carbon loss and nutrient release from E. crusgali and B. tripartitle litter and changes in soil properties, which are important for riparian zone rehabilitation in the Three Gorges Dam Reservoir, China. This study adopted the litter bag method to explore the nutrient change characteristics and changes in soil properties at different sediment burial depths under flooding scenarios. Three burial depths (0 cm, 5 cm, and 10 cm) were used for these two plants, and the experiment lasted for 180 days. The results revealed that the litter decay rate was high at first in the incubation experiment, and the nutrient loss rate followed the pattern of K > P > N > C. The relationship between % C remaining and % mass remaining was nearly 1:1, and the total amount of P exhibited a leaching–enrichment–release state in the decomposition process. Nutrients were changed significantly in the soil and overlying water at the first decomposition stage. Still, the total soil nutrient change was insignificant at the end, except for the 10 cm burial of B. tripartitle. Moreover, oxidation–reduction potential was the main factor in the litter decomposition process at different burial depths. This study indicated that sediment deposition reduced litter mass loss, slowed down the release of N and P, and retained more C, but promoted the release of K. Conclusively, in litter decomposition under waterlogging, the total soil nutrient content changed little. However, litter does more to the soil than that. Therefore, it is necessary to study the residual soil litter’s continuous output after the water level declines for restoration purposes.

Cycling performance prediction based on cadence analysis by using multiple regression

This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000. Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days’ worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable’s mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.

Methacholine Challenges: comparison of different tidal breathing challenge methods

Tidal breathing methacholine challenges are now recommended by guidelines, to avoid the bronchoprotective effects of deep inhalation. This study compared different tidal breathing methacholine challenge methods, assessed the agreement between tidal dosimetric and continuous output challenges, and challenge repeatability with different methods. 15 asthma patients performed dosimetric challenges and a continuous output breath actuated challenge, all at least 3 days apart. All subjects had a pre-bronchodilator forced expired volume in 1 s (FEV1) ≥65% predicted, and PD20 <1.2 mg. Of the dosimetric challenges, one method increased methacholine concentration (standard dosimetric challenge), and one adjusted nebuliser output time to increase dose (adjusted dosimetric challenge). The adjusted dosimetric and continuous output challenges were performed twice on separate days to assess for repeatability. All challenges were matched for dose at each dose step. The mean PD20 ratio of the standard dosimetric challenge to the adjusted dosimetric challenge was 0.90 (CI: 0.66–1.23; p=0.49), and intraclass correlation coefficient (ICC)=0.82. Repeated adjusted dosimetric challenges had an ICC=0.62 for PD20. Repeated continuous output challenges had an ICC =0.74 for PD20. The adjusted dosimetric and continuous output challenges correlated (r=0.69, p=0.0043; ICC: 0.65), but PD20 was higher for the adjusted dosimetric challenge (mean PD20 ratio=2.31; CI: 1.57–3.40; p=0.0004). Tidal dosimetric methacholine challenge using adjustment of nebuliser output produces results with good repeatability. The results of this adjusted dosimetric method differed from the continuous output method, underscoring that the results of different methacholine challenge methodologies may not be directly comparable.

An Influence Analytical Model of Dedicated Bus Lane on Network Traffic by Macroscopic Fundamental Diagram

To study the influence mechanism of dedicated bus lanes on the urban road network, this paper proposes a novel analytical model of macroscopic fundamental diagram (MFD) and passenger macroscopic fundamental diagram (p-MFD) and the corresponding indicators based on MFD and p-MFD to evaluate the operation of the network. Taking the grid network as an example, this paper collects traffic flow to calibrate the developed MFD and p-MFD and evaluates the network performance under different proportions of dedicated bus lanes. The simulation results show that the larger the proportion of dedicated bus lanes, the greater the impact on the rising section and the stable section of MFD and the descending section and post-stable section of p-MFD. Further analysis for the sensitivity of simulation experiments found that the strategy of setting dedicated bus lanes will improve the efficiency of vehicle and passenger transport when the road network is in a smooth state and ensure the continuous output of passengers when the network is in a congested state.

Joint Channel Tracking and Data Detection for Massive MIMO Uplink Over Time-Varying Channel

In this letter, we propose a novel joint channel estimation and data detection scheme for massive multiple-input multiple-output (MIMO) uplink, which operates in a time-varying fading channel. More specifically, at the receiver of the proposed scheme, a data frame is divided into multiple blocks, and in each block, a demodulated data block is used for updating channel state information (CSI) in an iterative manner. Furthermore, the initial CSI in the block of interest is given by the estimated CSI in the previous block, hence allowing accurate tracking of CSI in a time-varying channel without imposing additional pilot insertion inside the data frame. Since the length of the divided blocks affects both the achievable channel tracking and data detection performances, it is optimized so as to maximize the discrete-input continuous-output memoryless channel's (DCMC) capacity derived in this letter. It is demonstrated that the DCMC capacity of the proposed scheme is capable of nearly achieving those of the perfect CSI counterpart without imposing any substantial pilot overhead.

Joint Channel Tracking and Data Detection for Massive MIMO Uplink Over Time-Varying Channel

In this letter, we propose a novel joint channel estimation and data detection scheme for massive multiple-input multiple-output (MIMO) uplink, which operates in a time-varying fading channel. More specifically, at the receiver of the proposed scheme, a data frame is divided into multiple blocks, and in each block, a demodulated data block is used for updating channel state information (CSI) in an iterative manner. Furthermore, the initial CSI in the block of interest is given by the estimated CSI in the previous block, hence allowing accurate tracking of CSI in a time-varying channel without imposing additional pilot insertion inside the data frame. Since the length of the divided blocks affects both the achievable channel tracking and data detection performances, it is optimized so as to maximize the discrete-input continuous-output memoryless channel's (DCMC) capacity derived in this letter. It is demonstrated that the DCMC capacity of the proposed scheme is capable of nearly achieving those of the perfect CSI counterpart without imposing any substantial pilot overhead.