Research and Application Progress of Straw

Straw is a general term for the stem and leaf parts of mature crops, and is a multi-purpose renewable biomass energy resource in the agricultural ecosystem. The prospect of comprehensive utilization of straw has become broad with the development of agricultural production, the advancement of science and technology, and the improvement of the level of agricultural mechanization. The comprehensive utilization of straw plays an important role in enhancing the sustainable development ability of agricultural economy and improving the current situation of comprehensive utilization of agricultural resources in my country. This paper briefly combs the development history of straw and the prospect and current situation of comprehensive utilization, and expounds the separation technology of straw components, straw man-made panels, straw concrete, straw returning technology and oyster mushroom cultivation. It focuses on the description of the component separation technology of straw and the manufacturing process of straw-based panels. The different separation methods and separation effects of cellulose, hemicellulose and lignin were introduced in detail, and the static yield strength (MOR), internal bonding strength (IB) and water absorption thickness of several common straw-based panels were compared and studied (TS). Finally, it summarizes the benefit analysis of the comprehensive utilization of straw by scholars from the perspective of economics, and summarizes the corresponding measures based on their own views

A comparative study on polyp classification using convolutional neural networks

This work is licensed under a Creative Commons Attribution 4.0 International License.Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called ‘polyp’. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.University of Kansas grant (2228901

On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system

Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations

Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification

Platforms for Parallel Processing of Task on GPU

Import 05/08/2014Tato bakalářská práce se zabývá zpracováním úloh na grafické kartě. Konkrétním typem úloh jsou paralelní třídící algoritmy. V první části práce se vyskytuje popis technologií CUDA a OpenCL, ve kterých je později třídící algoritmus implementován. Dále je rozebrán princip daného algoritmu a jeho implementace. Následuje profilování a optimalizace třídícího algoritmu. V poslední částí je testování algoritmů na různých grafických kartách a porovnání obou technologií.This thesis deals with the processing tasks to the graphics card. Specific types of tasks are selected sorting algorithms. The first part includes description CUDA and OpenCL technology in which sorting algorithm is implemented. Next it is described the principle of the algorithm and its implementation. Next step is profiling and optimization of sorting algorithm. The last part includes testing these algorithms on different graphics cards and a comparison of both technologies.460 - Katedra informatikydobř

Mechanical Properties of Thin-Ply Composites Based on Acoustic Emission Technology

Compared with standard-ply composites, thin-ply composites exhibit a superior mechanical performance under various operating conditions due to their positive size effects. Thin-ply laminate failure modes, including matrix initial damage (MID), matrix failure (MF), and fiber failure (FF), have been distinguished through a systematic acoustic emission (AE) signals analysis combined with scanning electron microscopy (SEM). First, the characteristic frequencies of various failure modes are identified based on unidirectional laminates ([90] 68 and [0] 68). Then, according to the identified frequencies corresponding to distinctive damage modes, four lay-up sequences (02[[90m/0m]ns]02, m = 1, 2, 4, 8, n × m = 16) with a constant total thickness are designed, and the effects of the number of identical plies in the laminate thickness on the damage evolution characteristics and the damage process under uniaxial tension loads are dynamically monitored. The obtained results indicate that the characteristic frequency ranges for MID, MF, and FF are identified as 0–85 kHz, 165–260 kHz, and 261–304 kHz, respectively. The thickness of identical plies has a significant effect on onset damage. With the decrease of the number of identical plies (i.e., m in the stacking sequences), the thin-ply laminates exhibit the initiation of damage suppression effects and crack propagation resistance

Investigation on the Deterioration Mechanism of Recycled Plaster

The deterioration mechanism of recycled plaster (R-P) was studied. The large specific surface area (SSA), improper preparation temperature, increased water requirement of R-P, and microstructure of its hardened body were analyzed by particle size distribution (PSD), Blaine method, differential scanning calorimetry (DSC), scanning electron microscopy (SEM), and nitrogen adsorption porosimetry. The results indicated that the properties of R-P were deteriorated, but its strength decreases from 50% at the same manufacturing process to 30%–40% at similar specific surface area. The analysis shows that the large SSA, poor morphology, narrow PSD, and increased internal detects give rise to increase of water requirement. In addition, the deterioration properties are caused by unsuitable temperature of preparation, loose structure, and large average pore diameter in hardened R-P as well