Depth Super-Resolution from Explicit and Implicit High-Frequency Features

We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The former are extracted by an efficient transformer processing both local and global contexts, while the latter are obtained by projecting color images into the frequency domain. Both are combined together with depth features by means of a fusion strategy within a multi-stage and multi-scale framework. Experiments on the main benchmarks, such as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms existing methods by a large margin (~20% on NYUv2 and DIML against the contemporary work DADA, with 16x upsampling), establishing a new state-of-the-art in the guided depth super-resolution task

Pyrolytic Characteristics and Kinetics of Phragmites australis

The pyrolytic kinetics of Phragmites australis was investigated using thermogravimetric analysis (TGA) method with linear temperature programming process under an inert atmosphere. Kinetic expressions for the degradation rate in devolatilization and combustion steps have been obtained for P. australis with Dollimore method. The values of apparent activation energy, the most probable mechanism functions, and the corresponding preexponential factor were determined. The results show that the model agrees well with the experimental data and provide useful information for the design of pyrolytic processing system using P. australis as feedstock to produce biofuel

Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level

Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction in severe cases. In this paper, we proposed the A. gossypii infestation monitoring method, which identifies the infestation level of A. gossypii at the cotton seedling stage, and can improve the efficiency of early warning and forecasting of A. gossypii, and achieve precise prevention and cure according to the predicted infestation level. We used smartphones to collect A. gossypii infestation images and compiled an infestation image data set. And then constructed, trained, and tested three different A. gossypii infestation recognition models based on Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once (YOLO)v5 and single-shot detector (SSD) models. The results showed that the YOLOv5 model had the highest mean average precision (mAP) value (95.7%) and frames per second (FPS) value (61.73) for the same conditions. In studying the influence of different image resolutions on the performance of the YOLOv5 model, we found that YOLOv5s performed better than YOLOv5x in terms of overall performance, with the best performance at an image resolution of 640×640 (mAP of 96.8%, FPS of 71.43). And the comparison with the latest YOLOv8s showed that the YOLOv5s performed better than the YOLOv8s. Finally, the trained model was deployed to the Android mobile, and the results showed that mobile-side detection was the best when the image resolution was 256×256, with an accuracy of 81.0% and FPS of 6.98. The real-time recognition system established in this study can provide technical support for infestation forecasting and precise prevention of A. gossypii

DT-driven memory cutting control method using VR instruction of boom-type roadheader

Aiming at the problems of low intelligence of current tunneling equipment, difficulty in describing over-excavation, under-excavation and abnormal collision in tunneling process, and difficulty in adapting traditional automatic cutting and memory cutting technology to complex geological conditions, a digital twin-driven virtual teaching memory cutting control method for cantilever roadheader is proposed. By analyzing the research situation of digital twin technology in the field of intelligent coal mining, the overall scheme of memory cutting control system of cantilever roadheader driven by digital twin is designed, and the key technology of memory cutting of cantilever roadheader under complex working conditions is studied. Firstly, the characteristics of digital twin and virtual reality technology are fully utilized to study the virtual teaching strategy under complex working conditions. Based on the Unity3D platform, the virtual twin model of the working face and equipment with the same size of the corresponding entity, the kinematics model of the cutting unit and the virtual collision detection model are established. The virtual model movement is controlled through the intelligent interactive interface at the virtual end, and the teaching trajectory is designed and optimized according to the worker’s experience, so that it can be used as the target expected trajectory of trajectory tracking to make up for the excessive dependence on the worker’s experience caused by the traditional underground manual teaching due to the harsh working conditions. Secondly, in order to improve the quality of section forming, the control method of teaching trajectory tracking and reproduction in memory automatic cutting stage is studied. The dynamic model of cutting part is established by Lagrange method, and the tracking control accuracy of end effector to teaching trajectory is improved by combining iterative learning with sliding mode control. Finally, the simulation control platform of the memory cutting of the cantilever roadheader is built. Through the real-time data transmission and interaction between the virtual space and the physical space and between the modules, the three-dimensional visual simulation of the memory cutting virtual teaching and trajectory tracking control process is completed in the virtual space, and then the memory automatic cutting trajectory tracking control command is generated and sent to the end effector of the physical entity of the cantilever roadheader to drive it to carry out the section forming cutting according to the teaching trajectory. At the same time, the physical sensor collects the pose data of the cantilever roadheader fuselage and the cutting arm, and reversely drives the virtual model to move synchronously. The closed-loop control of robot virtual model and physical entity is realized. On this basis, the virtual and real synchronization of the system, the motion consistency between the virtual prototype and the physical prototype, and the trajectory tracking and reproduction control accuracy are verified. The experimental results show that the system data transmission delay is low, which can ensure the virtual and real consistency and synchronization, and the trajectory tracking control accuracy meets the actual use requirements. This method provides a new idea for memory cutting and intelligent control of tunneling equipment

Mutation-induced remodeling of the BfmRS two-component system in Pseudomonas aeruginosa clinical isolates

Genetic mutations are a primary driving force behind the adaptive evolution of bacterial pathogens. Multiple clinical isolates of Pseudomonas aeruginosa, an important human pathogen, have naturally evolved one or more missense mutations in bfmS, which encodes the sensor histidine kinase of the BfmRS two-component system (TCS). A mutant BfmS protein containing both the L181P and E376Q substitutions increased the phosphorylation and thus the transcriptional regulatory activity of its cognate downstream response regulator, BfmR. This reduced acute virulence and enhanced biofilm formation, both of which are phenotypic changes associated with a chronic infection state. The increased phosphorylation of BfmR was due, at least in part, to the cross-phosphorylation of BfmR by GtrS, a noncognate sensor kinase. Other spontaneous missense mutations in bfmS, such as A42E/G347D, T242R, and R393H, also caused a similar remodeling of the BfmRS TCS in P. aeruginosa. This study highlights the plasticity of TCSs mediated by spontaneous mutations and suggests that mutation-induced activation of BfmRS may contribute to host adaptation by P. aeruginosa during chronic infections

Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences

The accurate evaluation and fair comparison of wind farms power generation performance is of great significance to the technical transformation and operation and maintenance management of wind farms. However, problems exist in the evaluation indicator systems such as confusion, coupling and broadness, and the influence of wind energy resource differences not being able to be effectively eliminated, which makes it difficult to achieve the fair comparison of power generation performance among different wind farms. Thus, the evaluation indicator system and comprehensive evaluation method of wind farm power generation performance, including the influence of wind energy resource differences, are proposed in this paper to address the problems above, to which some new concepts such as resource conditions, ideal performance, reachable performance, actual performance, and performance loss are introduced in the proposed indicator system; the combination of statistical and comparative indicators are adopted to realize the quantitative evaluation, indicator decoupling, fair comparison, and loss attribution of wind farm power generation performance. The proposed comprehensive evaluation method is based on improved CRITIC (Criteria Importance though Intercrieria Correlation) weighting method, in which the uneven situation of different evaluation indicators and the comprehensive comparison of power generation performance among different wind farms shall be overcome and realized. Several sets of data from Chinese wind farms in service are used to validate the effectiveness and applicability of the proposed method by taking the comprehensive evaluation models based on CRITIC weighting method and entropy weighting method as the benchmarks. The results demonstrated that the proposed evaluation indicator system works in the quantitative evaluation and fair comparison of wind farm design, operation, and maintenance and traces the source of power generation performance loss. In addition, the results of the proposed comprehensive evaluation model are more in line with the actual power generation performance of wind farms and can be applied to the comprehensive evaluation and comparison of power generation performance of different wind farms

Multi‐component condition monitoring method for wind turbine gearbox based on adaptive noise reduction

Abstract Wind turbine gearbox condition monitoring (C&M) is a key technology to promote wind farm maintenance cost reduction and power generation improvement. Existing gearbox C&M methods usually adopt the component‐by‐component modelling approach. Firstly, this approach is inefficient in modelling; secondly, due to the thermal conduction effect, abnormalities in one gearbox component usually affect other components, making it difficult to identify the source of faults. To solve these problems, a normal behaviour model (NBM) combining data adaptive noise reduction and an improved variational auto‐encoder (VAE) is proposed, which can monitor the operational condition of multiple components of one gearbox simultaneously and takes into account the correlation between components when warning of the specific abnormal component. As verified by practical cases, the method balances the modelling accuracy and efficiency of the multi‐component NBM and achieves effective early warning and accurate localization of gearbox abnormalities. The proposed model has lower false alarm and missed alarm rates compared to other single‐component and multi‐component NBMs

Amine-Functionalized Sugarcane Bagasse: A Renewable Catalyst for Efficient Continuous Flow Knoevenagel Condensation Reaction at Room Temperature

A biomass-based catalyst with amine groups (–NH2), viz., amine-functionalized sugarcane bagasse (SCB-NH2), was prepared through the amination of sugarcane bagasse (SCB) in a two-step process. The physicochemical properties of the catalyst were characterized through FT-IR, elemental analysis, XRD, TG, and SEM-EDX techniques, which confirmed the –NH2 group was grafted onto SCB successfully. The catalytic performance of SCB-NH2 in Knoevenagel condensation reaction was tested in the batch and continuous flow reactions. Significantly, it was found that the catalytic performance of SCB-NH2 is better in flow system than that in batch system. Moreover, the SCB-NH2 presented an excellent catalytic activity and stability at the high flow rate. When the flow rate is at the 1.5 mL/min, no obvious deactivation was observed and the product yield and selectivity are more than 97% and 99% after 80 h of continuous reaction time, respectively. After the recovery of solvent from the resulting solution, a white solid was obtained as a target product. As a result, the SCB-NH2 is a promising catalyst for the synthesis of fine chemicals by Knoevenagel condensation reaction in large scale, and the modification of the renewable SCB with –NH2 group is a potential avenue for the preparation of amine-functionalized catalytic materials in industry

Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes

Nowadays, the commercial potential of live e-commerce is being continuously explored, and machine vision algorithms are gradually attracting the attention of marketers and researchers. During live streaming, the visuals can be effectively captured by algorithms, thereby providing additional data support. This paper aims to consider the diversity of live streaming devices and proposes an extremely lightweight and high-precision model to meet different requirements in live streaming scenarios. Building upon yolov5s, we incorporate the MobileNetV3 module and the CA attention mechanism to optimize the model. Furthermore, we construct a multi-object dataset specific to live streaming scenarios, including anchor facial expressions and commodities. A series of experiments have demonstrated that our model realized a 0.4% improvement in accuracy compared to the original model, while reducing its weight to 10.52%

Spatial Distribution of Global Cultivated Land and Its Variation between 2000 and 2010, from Both Agro-Ecological and Geopolitical Perspectives

Food security requires a thorough understanding of the spatial characteristics of cultivated land changes on a global scale. In particular, the spatial heterogeneity of global cultivated land changes needs to be evaluated with high spatial resolution data. This study aims to analyse the spatial distribution of global cultivated land and the characteristics of its variation, by using GlobeLand30 data for 2000 and 2010 with a 30-m spatial resolution. The cultivated land percentage and rate of cultivated land use change are calculated based on 18 agro-ecological zones (AEZs), 32 geopolitical and socioeconomic regions, and 283 world regions. The results show that (1) more cultivated land is located in regions under a temperate climate and moderate moisture conditions; (2) the percentage of cultivated land is related to the gross domestic product (GDP) and population, while increases and decreases in cultivated land are related to the rural population, policy encouragement, urbanization, and economic development; and (3) the percentage of cultivated land and rate of land use change within an AEZ vary greatly due to the different socioeconomic conditions, and the values within a geopolitical area also vary, due to different natural conditions