In this research, we combined two distinct, structured light methods, the single-shot pseudo-random sequence-based approach and the time-multiplexing stripe indexing method. As a result, the measurement resolution of the single-shot, spatially encoded, pseudo-random sequence-based method improved significantly. Since the time-multiplexed stripe-indexed-based techniques have a higher measurement resolution, we used varying stripes to enhance the measurement resolution of the pseudo-random sequence-based approaches. We suggested a multi-resolution 3D measurement system that consisted of horizontal and vertical stripes with pixel sizes ranging from 8 × 8 to 16 × 16. We used robust pseudo-random sequences (M-arrays) to controllably distribute various stripes in a pattern. Since single-shape primitive characters only contribute one feature point to the projection pattern, we used multiple stripes instead of single-shape primitive symbols. However, numerous stripes will contribute multiple feature points. The single character-based design transforms into an increased featured size pattern when several stripes are employed. Hence, the projection pattern contains a much higher number of feature points. So, we obtained a high-resolution measurement. Each stripe in the captured image is located using adaptive grid adjustment and stripe indexing techniques. The triangulation principle is used to measure 3D.

In order to investigate the health risks of NO3- in rural drinking groundwater in Suihua, China and provide a basis for healthy drinking water, 40 sets of groundwater samples were collected in the Suihua area, and the average concentration of nitrate in the study area was 71.66 mg/L, statistical analysis software (SPSS19), Hydrogeochemical Analysis Software (AqQA) and groundwater pollution analysis software were used. Through water sample collection, chemical analysis and construction of human health risk model (HHRA), a qualitative and quantitative assessment of NO3- health risk was carried out for people of different ages and sexes, and it was concluded that there was NO3- pollution health risk in rural drinking groundwater in Suihua. Health risk level: infants > children > adult females > adult males. The evaluation provides a scientific basis for the prevention and control of NO3- pollution in groundwater and new ideas for preventing human health risks.

In this work, a novel object-level building-matching method using cross-dimensional data, including 2D images and 3D point clouds, is proposed. The core of this method is a newly proposed plug-and-play Joint Descriptor Extraction Module (JDEM) that is used to extract descriptors containing buildings’ three-dimensional shape information from object-level remote sensing data of different dimensions for matching. The descriptor is named Signed Distance Descriptor (SDD). Due to differences in the inherent properties of different dimensional data, it is challenging to match buildings’ 2D images and 3D point clouds on the object level. In addition, features extracted from the same building in images taken at different angles are usually not exactly identical, which will also affect the accuracy of cross-dimensional matching. Therefore, the question of how to extract accurate, effective, and robust joint descriptors is key to cross-dimensional matching. Our JDEM maps different dimensions of data to the same 3D descriptor SDD space through the 3D geometric invariance of buildings. In addition, Multi-View Adaptive Loss (MAL), proposed in this paper, aims to improve the adaptability of the image encoder module to images with different angles and enhance the robustness of the joint descriptors. Moreover, a cross-dimensional object-level data set was created to verify the effectiveness of our method. The data set contains multi-angle optical images, point clouds, and the corresponding 3D models of more than 400 buildings. A large number of experimental results show that our object-level cross-dimensional matching method achieves state-of-the-art outcomes.

In this paper, genetic algorithm and commutation switch based three-phase imbalanced distribution network are studied. A genetic algorithm-based commutation switch three-phase imbalance management strategy was developed, and the three-phase current distribution station area was established. A mathematical model was developed for the multi-objective optimal commutation based on the minimum imbalance degree and the minimum switching times of the low-voltage load online automatic commutation device during the commutation process. Additionally, the real-time online governance control strategies were balanced. Finally, the effectiveness of the proposed method was verified using simulation studies.

Ecological characteristics are very important for the Earth’s environment and human lives. Recently, more attention has been paid to the ecological problems in the world. The soil and its parent materials/rocks serve as the supporting materials for the ecological system on the Earth’s surface. The ecological characteristics in one region are associated with or even dependent on the soil-forming process. Thus, the study of the weathering process of parent materials/rocks is important for our understanding of the geological genesis of ecological problems. In this study, three typical ecological problems in China are introduced: land salinization in the north, the desertification of land in the northwest, and Karst rocky desertification in the south. We chose 23 typical profiles for observation and sampling. The soil-forming processes in these areas were investigated by geochemical research, and eco-geology models were subsequently established to explain the profound relationship between vegetation cover and the soil-forming process. Our work first focuses on the geochemical methods used to explore these ecological issues, emphasizing the contribution of the geological genesis to the ecological characteristics. Using geochemical methods, such as the chemical index of alteration (CIA), major element and trace element distribution characteristics, the formation processes, and characteristics of bedrock-weathering soils under diverse epigenetic geological settings were determined. Eco-geology models were then developed by evaluating the vertical structure and material composition of soil, the characteristics of elemental migration during soil formation, and the enrichment and loss of elements in the supporting layers and their repercussions.

Smart agriculture is the integration and development of modern science and technology in agriculture related fields. Traditional agriculture is gradually transitioning to automatic and intelligent agriculture. Agricultural Internet of things can not only increase crop output, but also liberate productivity and improve labor return. Therefore, this paper studies the intelligent agrometeorological information monitoring system, which can wirelessly monitor the value and range of temperature and humidity, light, soil humidity, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> concentration, and save manpower and material resources for people to monitor agrometeorological information at all time. The intelligent agricultural meteorological information monitoring system uses ZigBee technology conforming to IEEE 802.15.4 standard to deploy wireless networks and each node in the network communicates with each other, The terminal sensor sends the collected temperature and humidity, illumination, soil humidity and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> concentration signals to the nearest routing node. The routing node wirelessly sends various signals to the coordinator, which then uploads them to the PC through the serial port. The PC displays all information and gives an alarm according to the designed limit value. This paper focuses on the analysis of the main framework and key technologies of the intelligent agriculture remote monitoring system, and the system research, design and implementation.

Omnichannel catering firms need to pay attention to the consumer behavior of customers in both online and offline channels to make the corresponding capacity decisions. However, customers’ consumption behavior is not only affected by base utility, but also by reference utility, which indicates that customers have reference dependence when making consumption decisions. In order to better help omnichannel catering firms to make capacity decisions more in line with customer consumption behavior in their actual operations, this paper adopts the queuing theory to construct an optimal capacity decision-making model for omnichannel catering firms. We also analyzed the impact of channel information availability on customer reference behavior and the subsequent impact on the firm’s optimal decision-making. The findings were as follows: when the channel information is unavailable, customers in each channel take their expectation of waiting time as the reference point. With the improvement of the sensitivity of customers in different channels to the reference point, firms should improve their safety capacity to meet customers’ needs. Moreover, at a higher reference point, the increase in customer sensitivity can make the firm obtain higher profits. When channel information is available, customers take the waiting time of customers in different channels as the reference point, and the increased sensitivity of online customers to the reference point will prompt firms to reduce safety capacity. Comparing the two scenarios, we also found that omnichannel catering firms could develop lower safety capacities and obtain higher profits more easily when channel information is available. This not only contributes to the development of omnichannel catering firms, but also expands the application scope of reference theory.