Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

OPTOELECTRONIC HARDWARE-SOFTWARE MONOMODULAR SENSOR OF METEOROLOGICAL RANGE OF VISIBILITY AND ATMOSPHERE TRANSPARENCY WITH AUTOMATIC CORRECTION OF EXTERNAL POLLUTION EFFECT OF OPTICAL SYSTEMS

A model sample of a monomodular hardware-software optoelectronic sensor of k-factor, meteorological range of visibility and atmosphere transparency with high technical and operational characteristics of small -sized execution is developed and created. The combination in the algorithm of functioning of the developed hardware-software methods of sensor creation provided increase of accuracy and invariance of results of measurement concerning external background illumination and self-testing with automatic correction of pollution effect of optical windows of the sensor. Continuous monitoring of the degree of contamination of optical windows provides an opportunity in case of detection of exceeding the permissible level of pollution to provide the operator with information about the need for preventive work. The proposed method of automatic correction of contamination of optical windows either completely eliminates or significantly increases the time interval of their mechanical cleaning operations, which reduces labour costs for maintenance of the sensor. The analysis of technical capabilities of the nodes of the model sample of the sensor allowed to estimate its basic meteorological parameters: the k-factor – (0.17÷1.11∙10-4) m-1, the meteorological visibility – (18÷27∙103 ) m and the atmosphere transparency per 1 km of the layer – (0÷0.895). The developed meteorological sensor can be used as a basic remote-controlled instrument for measuring meteorological parameters of the state of the atmosphere at the hydrometeorological stations of the country, mobile meteorological stations, stationary meteorological stations of road and air services to ensure guaranteed safety of workers.

Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “K,” which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC.

Cropping pattern and schedule using K-Factor for optimizing irrigation networks operation in Krueng Jreu Irrigation Area, Aceh, Indonesia

Determining the cropping pattern and schedule according to the availability and requirement of irrigation water is important in an irrigation command area. Supplying irrigation water in the Krueng Jreu Irrigation Area is still less effective in the dry season, so it is necessary to review the existing cropping pattern and schedule in the irrigation area by considering the K factor. To achieve optimal irrigation networks operation, simulation of cropping patterns and schedules based on the K factor was conducted. Optimal cropping patterns and schedules were determined by the highest frequency of the half-month K factor greater than 0.75. The best cropping pattern and schedule for Krueng Jreu Irrigation Area was rice-rice-soybean cropping pattern with first planting season started in mid-July, second planting season in mid-November and third planting season in mid-March. The results achieved the best frequency of K factor > 0.75 as many as 15 times, K factor in the range of 0.50 - 0.75 as many as 3 times, and K factor < 0.25 as many as 4 times. The application of the selected cropping pattern and schedule was done by allocating water into three groups in case of the K factor < 0.75, namely Group I (Menara and Krueng Aceh Extension secondary canal), Group II (Krueng Jreu Kiri secondary canal) and Group III (Kayee, Lamkrah, and Inong secondary canal).

Calibration and Optimization of an Insertion Electromagnetic Flowmeter with the Aid of Numerical Analysis

Accurate measurement of the volumetric flow rate of working liquids is essential for process control, as well as energy consumption evaluation. Electromagnetic flowmeters have gained popularity in applications where low-pressure drop and low maintenance are required. Dwyer Instruments, Inc. recently developed an adjustable insertion electromagnetic flowmeter (IEF) featuring accurate and reliable measurement. However, unexpected and non-repeatable behavior in the K-factor was observed during the calibration process. The K-factor is the coefficient used to convert the measured electric potential to the flow velocity in pipes, and the non-repeatable behavior imposes challenges for precise measurement. A one-way coupled magnetohydrodynamics model was developed to reduce the effort and time of on-site troubleshooting and optimization. By modeling the measurement process, the transition of flow regimes and the regeneration of the boundary layer on the electrode surface were identified as the causes of the non-repeatable issue. Then, a series of parametric studies were performed to provide reliable solutions. A new design with further embedded electrodes to allow the smooth transition between boundary layers was recommended. The field test showed excellent repeatability by using the new design, and the non-repeatable issue was entirely resolved. The improvement in the IEF design was implemented in production in less than one week, and it reduced the calibration time by 50%.

Some degree conditions for P≥k-factor covered graphs

A spanning subgraph of a graph $G$ is called a path-factor of $G$ if its each component is a path. A path-factor is called a $\mathcal{P}_{\geq k}$-factor of $G$ if its each component admits at least $k$ vertices, where $k\geq2$. Zhang and Zhou [\emph{Discrete Mathematics}, \textbf{309}, 2067-2076 (2009)] defined the concept of $\mathcal{P}_{\geq k}$-factor covered graphs, i.e., $G$ is called a $\mathcal{P}_{\geq k}$-factor covered graph if it has a $\mathcal{P}_{\geq k}$-factor covering $e$ for any $e\in E(G)$. In this paper, we firstly obtain a minimum degree condition for a planar graph being a $\mathcal{P}_{\geq 2}$-factor and $\mathcal{P}_{\geq 3}$-factor covered graph, respectively. Secondly, we investigate the relationship between the maximum degree of any pairs of non-adjacent vertices and $\mathcal{P}_{\geq k}$-factor covered graphs, and obtain a sufficient condition for the existence of $\mathcal{P}_{\geq2}$-factor and $\mathcal{P}_{\geq 3}$-factor covered graphs, respectively.