Review of Typical Municipal Solid Waste Disposal Status and Energy Technology

AbstractThe physical and chemical components of urban domestic waste, medical waste and electronic waste are introduced, the advantages and disadvantages of the existing processing methods are pointed out. Considering the higher content of organic and metal of these municipal solid wastes, pyrolysis technology is a suitable method to make them harmless, reduced, reusable and being available energy

Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey

With the rapid advance in camera sensor technology, the acquisition of high-resolution images or videos has become extremely convenient and cost-effective. Computer vision that extracts semantic knowledge directly from digital images or videos, offers a promising solution for non-contact and full-field structural vibration measurement and condition assessment. Unmanned aerial vehicles (UAVs), also known as flying robots or drones, are being actively developed to suit a wide range of applications. Taking advantage of its excellent mobility and flexibility, camera-equipped UAV systems can facilitate the use of computer vision, thus enhancing the capacity of the structural condition assessment. The current article aims to provide a concise survey of the recent progress and applications of UAV-based computer vision in the field of structural dynamics. The different aspects to be discussed include the UAV system design and algorithmic development in computer vision. The main challenges, future trends, and opportunities to advance the technology and close the gap between research and practice will also be stated

RLLTE: Long-Term Evolution Project of Reinforcement Learning

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a complete and luxuriant ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia.Comment: 22 pages, 15 figure

A Multicenter prospective study of poor-grade aneurysmal subarachnoid hemorrhage (AMPAS): observational registry study

BACKGROUND: Poor-grade aneurysmal subarachnoid hemorrhage (aSAH) is associated with very high mortality and morbidity. Our limited knowledge on predictors of long-term outcome in poor-grade patients with aSAH definitively managed comes from retrospective and prospective studies of small case series of patients in single center. The purpose of the AMPAS is to determine the long-term outcomes in poor-grade patients with different managements within different time after aSAH, and identify the independent predictors of the outcome that help guide the decision on definitive management. METHODS/DESIGN: The AMPAS study is a prospective, multicenter, observational registry of consecutive hospitalized patients with poor grade aSAH (WFNS grade IV and V). The aim is to enroll at least 226 poor-grade patients in 11 high-volume medical centers (eg, >150 aSAH cases per year) affiliated to different universities in China. This study will describe poor grade patients and aneurysm characteristics, treatment strategies (modality and time of definitive management), hospitalization complications and outcomes evolve over time. The definitive management is ruptured aneurysm treatment. Outcomes at 3, 6, 12 months after the management were measured using the Glasgow Outcome Scale and the Modified Rankin Scale. DISCUSSION: The AMPAS is the first prospective, multicenter, observational registry of poor grade aSAH with any management. This study will contribute to a better understanding of significant predictors of outcome in poor grade patients and help guide future treatment of the worst patients after aSAH. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR-TNRC-10001041

MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. However, this is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space for fusion, and they can be roughly divided into 1) early fusion of raw points that aims at augmenting the 3D point cloud at the early input stage, and 2) late fusion of BEV (bird-eye view) maps that merges LiDAR and camera BEV features before the detection head. While both have their merits in enhancing the representation power of the combined features, this single-level fusion strategy is a suboptimal solution to the aforementioned challenge. Their major drawbacks are the inability to interact the multi-granularity semantic features from two distinct modalities sufficiently. To this end, we propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features. Our proposed method, abbreviated as MDMSFusion, achieves state-of-the-art results in 3D object detection, with 69.1 mAP and 71.8 NDS on nuScenes validation set, and 70.8 mAP and 73.2 NDS on nuScenes test set, which rank 1st and 2nd respectively among single-model non-ensemble approaches by the time of submission

STDP-based adaptive graph convolutional networks for automatic sleep staging

Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Ternary CdS/Au/3DOM-SrTiO3 composites with synergistic enhancement for hydrogen production from visible-light photocatalytic water splitting

This work was supported by the National High Technology Research and Development Program of China (Grant No. 2012AA063008), the Tianjin Municipal Natural Science Foundation (Grant Nos. 17JCYBJC22600 and 15JCTPJC63500), China Scholarship Council (Grant 201606200096), and the Fundamental Research Funds for the Central Universities.New ternary composites based on three dimensionally ordered macroporous (3DOM) SrTiO3 (CdS/Au/3DOM-SrTiO3) were prepared and used as photocatalysts in visible light (λ > 420 nm) photocatalytic water splitting for hydrogen evolution. Through optimizing the pore size of 3DOM-SrTiO3 materials and the loading amounts of Au and CdS, CdS/Au/3DOM-SrTiO3(300), templated by 300 nm sized poly(methyl methacrylate) colloids, was found to exhibit a remarkably enhanced photocatalytic hydrogen evolution rate (2.74 mmol/h•g), which was 3.2 times as high as that of CdS/Au/C-SrTiO3 catalyst based on commercial SrTiO3. This notably enhanced photocatalytic performance was mainly attributed to the slow photon enhancement effect of 3DOM-SrTiO3(300) material, which significantly promoted the light harvesting efficiency of ternary composite for the slow photon region of 3DOM-SrTiO3(300) was well matched with the optical absorption band of photocatalyst. Further depositing Pt nanoparticles on CdS/Au/3DOM-SrTiO3(300) composite as a co-catalyst, an extraordinarily high hydrogen evolution rate (up to 5.46 mmol/g•h) and apparent quantum efficiency (42.2% at 420 nm) were achieved because of the synergistic effect of efficient carrier separation, Au SPR effect, and slow photon effect. Furthermore, these ternary CdS/Au/3DOM-SrTiO3 composite photocatalysts were very stable and could be easily recycled four times in visible light photocatalytic water splitting experiments without any loss in activity.PostprintPeer reviewe