Research on Homogeneous Charge Compression Ignition Combustion of Intake Port Exhaust Gas Recirculation Based on Cam Drive Hydraulic Variable Valve Actuation Mechanism

The thermal efficiency of an efficient gasoline engine is only about 40% and it will produce a large number of harmful products. Curbing harmful emissions and enhancing thermal efficiency have always been the goals pursued and emission regulations are also being tightened gradually. As one of the main consumers of fossil fuels, automobile engines must further reduce fuel consumption and emissions to comply with the concept of low-carbon development, which will also help them compete with electric vehicles. Homogeneous charge compression ignition (HCCI) combustion combined with variable valve actuation (VVA) technology is one of the important ways to improve engine emissions and economy. HCCI combustion based on VVA can only be realized at small and medium loads. The actual application on the entire vehicle needs to be combined with spark ignition (SI) combustion to achieve full working condition coverage. Therefore, HCCI combustion needs fast valve response characteristics; however, the valve lift and timing of the existing VVA mechanisms are mostly controlled separately, resulting in poor valve response. In order to solve this problem, the cam driven hydraulic variable valve actuation (CDH-VVA) mechanism was designed. The valve lift and timing can be adjusted at the same time and the switching of valve lift and timing can be completed in 1~2 cycles. A set of combustion mode switching data is selected to show the response characteristics of the CDH-VVA mechanism. When switching from spark ignition (SI) to HCCI, it switches to HCCI combustion after only one combustion cycle and it switches to stable HCCI combustion after two combustion cycles, which proves the fast response characteristics of the CDH-VVA mechanism. At the same time, the CDH-VVA mechanism can form the intake port exhaust gas recirculation (EGR), as one type of internal EGR. This paper studies the HCCI combustion characteristics of the CDH-VVA mechanism in order to optimize it in the future and enable it to realize more forms of HCCI combustion. At 1000 rpm, if the maximum lift of the exhaust valve (MLEV) is higher than 5.0 mm or lower than 1.5 mm, HCCI combustion cannot operate stably, the range of excess air coefficient (λ) is largest when the MLEV is 4.5 mm, ranging from 1.0~1.5. Then, as the MLEV decreases, the range of λ becomes smaller. When the MLEV drops to 1.5 mm, the range of λ shortens to 1.0~1.3. The maximum value of the MLEV remains the same at the three engine speeds (1000 rpm, 1200 rpm and 1400 rpm), which is 5.0 mm. The minimum value of the MLEV gradually climbs as the engine speed increase, 1000 rpm: 1.5 mm, 1200 rpm: 2.0 mm, 1400 rpm: 3.0 mm. With the increase of engine speed, the range of indicated mean effective pressure (IMEP) gradually declines, 3.53~6.31 bar (1000 rpm), 4.11~6.75 bar (1200 rpm), 5.02~6.09 bar (1400 rpm), which proves that the HCCI combustion loads of the intake port EGR are high and cannot be extended to low loads. The cyclic variation of HCCI combustion basically climbs with the decrease of the MLEV and slightly jumps with the increase of the engine speed. At 1000 rpm, when the MLEV is 5.0 mm, the cyclic variation range is 0.94%~1.5%. As the MLEV drops to 1.5 mm, the cyclic variation range rises to 3.5%~4.5%. Taking the maximum value of the MLEV as an example, the cyclic variation range of 1000 rpm is 0.94%~1.5%, 1200 rpm becomes 1.5%~2.3% and 1400 rpm rises to 2.0%~2.5%

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard

Identification of Key Areas for Ecosystem Restoration Based on Ecological Security Pattern

Ecosystem degradation and conversion are leading to a widespread reduction in the provision of ecosystem services. It is crucial for the governance of regional land spaces to rapidly identify key areas for ecosystem restoration. Herein, we combined the InVEST Habitat Quality Model with the granularity inverse method to identify ecological sources in Jiashi county, China, based on the “source-corridor” ecological security pattern paradigm. The minimum cumulative resistance model and circuit theory were adopted to diagnose the ecological “pinch points”, barrier points, break points, and key restoration areas for land space. Our results show that: (1) the area of the ecological source and the total length of the ecological corridor were identified as 1331.13 km2 and 316.30 km, respectively; (2) there were 164 key ecological “pinch points” and 69 key ecological barrier points in Jiashi county, with areas of 15.13 km2 and 14.57 km2, respectively. Based on the above ecological security pattern, recovery strategies are put forward to improve regional ecosystem health. This study describes the best practices which can be used to guide the planning and implementation of ecosystem restoration at the local landscape scale

Identification of Key Areas for Ecosystem Restoration Based on Ecological Security Pattern

Ecosystem degradation and conversion are leading to a widespread reduction in the provision of ecosystem services. It is crucial for the governance of regional land spaces to rapidly identify key areas for ecosystem restoration. Herein, we combined the InVEST Habitat Quality Model with the granularity inverse method to identify ecological sources in Jiashi county, China, based on the “source-corridor” ecological security pattern paradigm. The minimum cumulative resistance model and circuit theory were adopted to diagnose the ecological “pinch points”, barrier points, break points, and key restoration areas for land space. Our results show that: (1) the area of the ecological source and the total length of the ecological corridor were identified as 1331.13 km2 and 316.30 km, respectively; (2) there were 164 key ecological “pinch points” and 69 key ecological barrier points in Jiashi county, with areas of 15.13 km2 and 14.57 km2, respectively. Based on the above ecological security pattern, recovery strategies are put forward to improve regional ecosystem health. This study describes the best practices which can be used to guide the planning and implementation of ecosystem restoration at the local landscape scale

Temperature-induced variations in photocatalyst properties and photocatalytic hydrogen evolution: Differences in UV, visible, and infrared radiation

In this work, solar-heating-induced temperature-based photocatalytic hydrogen evolution reaction (PC-HER) of different photocatalysts (TiO2 P25, g-C3N4, and their loaded Pt) was comprehensively studied and analyzed with the assistance of a series of temperature-based in situ characterizations. It was found that pristine TiO2 P25 and g-C3N4 displayed enhanced PC-HER performances with increasing temperature (25-65 °C), while their loaded Pt nanoparticles (NPs) demonstrated a different behavior under ultraviolet (UV) or visible irradiation, presenting the highest hydrogen evolution rate at 35 °C. More interestingly, Pt NPs-g-C3N4 showed increasing PC-HER performances from 25 to 65 °C under visible light irradiation. Characterizations suggested that lowered electrical impedance, reduced band gap, increased light absorption, and elongated photoelectron lifetime with increased temperature are beneficial for improved PC-HER. However, agglomeration of Pt NPs significantly deteriorated the PC-HER performance at higher temperature and UV light can aggravate the thermal agglomeration of Pt NPs

Characteristics, Sources and Health Risk of Heavy Metals in Road Dust in the Typical County Town, Central China

In this study, to investigate the contamination characteristics and potential health implications of heavy metals in road dust of the typical county in central China, heavy metals (Cd, Co, Cr, Cu, Mn, Ni, V, Pb, Zn) in typical road dust with large traffic flow, in different functional areas of Yangxin County, were determined. The results of the geo-accumulation index (Igeo) showed that Co, Mn, Ni, and V were not polluted, while other heavy metals caused different degrees of pollution. According to principal component analysis (PCA), there were three main sources of heavy metals. The result of statistical analysis showed that heavy metal pollution in road dust mainly comes from traffic activities, industrial production activities, building pollution, and the natural environment. The carcinogenic and non-carcinogenic risks of children and adults were within the safe range, and hand–oral contact was the main exposure route of non-carcinogenic risks. The non-carcinogenic risk and carcinogenic effects of heavy metals in urban road dust were acceptable to children and adults. However, we should still pay attention to the impact of heavy metals on the ecological environment and human health

Characteristics, Sources and Health Risk of Heavy Metals in Road Dust in the Typical County Town, Central China

In this study, to investigate the contamination characteristics and potential health implications of heavy metals in road dust of the typical county in central China, heavy metals (Cd, Co, Cr, Cu, Mn, Ni, V, Pb, Zn) in typical road dust with large traffic flow, in different functional areas of Yangxin County, were determined. The results of the geo-accumulation index (Igeo) showed that Co, Mn, Ni, and V were not polluted, while other heavy metals caused different degrees of pollution. According to principal component analysis (PCA), there were three main sources of heavy metals. The result of statistical analysis showed that heavy metal pollution in road dust mainly comes from traffic activities, industrial production activities, building pollution, and the natural environment. The carcinogenic and non-carcinogenic risks of children and adults were within the safe range, and hand–oral contact was the main exposure route of non-carcinogenic risks. The non-carcinogenic risk and carcinogenic effects of heavy metals in urban road dust were acceptable to children and adults. However, we should still pay attention to the impact of heavy metals on the ecological environment and human health