Aircraft Ground Taxiing Deduction and Conflict Early Warning Method Based on Control Command Information

Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation. In order to solve the problem, The aircraft taxiing deduction and conflict early warning method based on control order information is proposed. This method does not need additional equipment and operating costs, and is completely based on his-torical data and control command information. When the aircraft taxiing command is given, the future route information will be deduced, and the probability of conflict with other taxiing aircraft will be calculated to achieve conflict detection and early warning of different levels. The method is validated by the aircraft taxi data from real airports. The results show that the method can effectively predict the aircraft taxiing process, and can provide early warning of possible conflicts. Due to the advantages of low cost and high accuracy, this method has the potential to be applied to airport operation decision support system

Synthesis, Crystal Structures and Urease Inhibition of Mononuclear Copper(II) and Nickel(II) Complexes with Schiff Base Ligands

Three mononuclear copper(II) and nickel(II) complexes, [Cu(L1)(NCS)(CH3OH)] (1), [Cu(L2)(NCS)] (2) and [Ni(L2)(N3)] (3), where L1 and L2 are the monoanionic forms of the Schiff bases N'-(pyridin-2-ylmethylene)picolinohydrazide (HL1) and 4-methyl-2-(((pyridin-2-ylmethyl)imino)methyl)phenol (HL2), have been prepared and characterized by elemental analysis, IR and UV-Vis spectroscopy, as well as single crystal X-ray diffraction studies. The Cu atom in complex 1 is in a square pyramidal coordination, with the three N atoms of the ligand L and the N atom of the thiocyanate ligand in the basal plane, and with the methanol O atom at the apical position. The Cu and Ni atoms in complexes 2 and 3 are in square planar coordination, with the three donor atoms of the Schiff base ligands and the terminal N atoms of thiocyanate and azide ligands. Complexes 1 and 2 inhibit the Jack bean urease with IC50 value of 0.33±0.12 and 0.39±0.10 μmol L–1, respectively. Molecular docking study was performed to investigate the interaction between the complexes and the enzyme

EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding

Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few efforts were devoted to structurally interpreting how features are encoded in the learned embedding space. This work proposes EmbeddingTree, a hierarchical embedding exploration algorithm that relates the semantics of entity features with the less-interpretable embedding vectors. An interactive visualization tool is also developed based on EmbeddingTree to explore high-dimensional embeddings. The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities. We demonstrate the efficacy of EmbeddingTree and our visualization tool through embeddings generated for industry-scale merchant data and the public 30Music listening/playlists dataset.Comment: 5 pages, 3 figures, accepted by PacificVis 202

Multitask Learning for Time Series Data with 2D Convolution

Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally surpasses its non-MTL counterparts in terms of generalizability. Although MTL has been extensively researched in various domains such as computer vision, natural language processing, and recommendation systems, its application to time series data has received limited attention. In this paper, we investigate the application of MTL to the time series classification (TSC) problem. However, when we integrate the state-of-the-art 1D convolution-based TSC model with MTL, the performance of the TSC model actually deteriorates. By comparing the 1D convolution-based models with the Dynamic Time Warping (DTW) distance function, it appears that the underwhelming results stem from the limited expressive power of the 1D convolutional layers. To overcome this challenge, we propose a novel design for a 2D convolution-based model that enhances the model's expressiveness. Leveraging this advantage, our proposed method outperforms competing approaches on both the UCR Archive and an industrial transaction TSC dataset

Toward a Foundation Model for Time Series Data

A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks. However, current research on time series pre-training has mostly focused on models pre-trained solely on data from a single domain, resulting in a lack of knowledge about other types of time series. However, current research on time series pre-training has predominantly focused on models trained exclusively on data from a single domain. As a result, these models possess domain-specific knowledge that may not be easily transferable to time series from other domains. In this paper, we aim to develop an effective time series foundation model by leveraging unlabeled samples from multiple domains. To achieve this, we repurposed the publicly available UCR Archive and evaluated four existing self-supervised learning-based pre-training methods, along with a novel method, on the datasets. We tested these methods using four popular neural network architectures for time series to understand how the pre-training methods interact with different network designs. Our experimental results show that pre-training improves downstream classification tasks by enhancing the convergence of the fine-tuning process. Furthermore, we found that the proposed pre-training method, when combined with the Transformer model, outperforms the alternatives

Increased expression of pigment epithelium-derived factor in aged mesenchymal stem cells impairs their therapeutic efficacy for attenuating myocardial infarction injury.

AIMS: Mesenchymal stem cells (MSCs) can ameliorate myocardial infarction (MI) injury. However, older-donor MSCs seem less efficacious than those from younger donors, and the contributing underlying mechanisms remain unknown. Here, we determine how age-related expression of pigment epithelium-derived factor (PEDF) affects MSC therapeutic efficacy for MI. METHODS AND RESULTS: Reverse transcriptase-polymerized chain reaction and enzyme-linked immunosorbent assay analyses revealed dramatically increased PEDF expression in MSCs from old mice compared to young mice. Morphological and functional experiments demonstrated significantly impaired old MSC therapeutic efficacy compared with young MSCs in treatment of mice subjected to MI. Immunofluorescent staining demonstrated that administration of old MSCs compared with young MSCs resulted in an infarct region containing fewer endothelial cells, vascular smooth muscle cells, and macrophages, but more fibroblasts. Pigment epithelium-derived factor overexpression in young MSCs impaired the beneficial effects against MI injury, and induced cellular profile changes in the infarct region similar to administration of old MSCs. Knocking down PEDF expression in old MSCs improved MSC therapeutic efficacy, and induced a cellular profile similar to young MSCs administration. Studies in vitro showed that PEDF secreted by MSCs regulated the proliferation and migration of cardiac fibroblasts. CONCLUSIONS: This is the first evidence that paracrine factor PEDF plays critical role in the regulatory effects of MSCs against MI injury. Furthermore, the impaired therapeutic ability of aged MSCs is predominantly caused by increased PEDF secretion. These findings indicate PEDF as a promising novel genetic modification target for improving aged MSC therapeutic efficacy

Extraordinary Thermoelectric Properties of Topological Surface States in Quantum-Confined Cd3As2 Thin Films

Topological insulators and semimetals have been shown to possess intriguing thermoelectric properties promising for energy harvesting and cooling applications. However, thermoelectric transport associated with the Fermi arc topological surface states on topological Dirac semimetals remains less explored. In this work, we systematically examine thermoelectric transport in a series of topological Dirac semimetal Cd3As2 thin films grown by molecular beam epitaxy. Surprisingly, we find significantly enhanced Seebeck effect and anomalous Nernst effect at cryogenic temperatures when the Cd3As2 layer is thin. Combining angle-dependent quantum oscillation analysis, magnetothermoelectric measurement, transport modelling and first-principles simulation, we isolate the contributions from bulk and surface conducting channels and attribute the unusual thermoeletric properties to the topological surface states. Our analysis showcases the rich thermoelectric transport physics in quantum-confined topological Dirac semimetal thin films and suggests new routes to achieving high thermoelectric performance at cryogenic temperatures

An Efficient Content-based Time Series Retrieval System

A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, healthcare, and manufacturing. For example, users seeking to learn more about the source of a time series can submit the time series as a query to the CTSR system and retrieve a list of relevant time series with associated metadata. By analyzing the retrieved metadata, users can gather more information about the source of the time series. Because the CTSR system is required to work with time series data from diverse domains, it needs a high-capacity model to effectively measure the similarity between different time series. On top of that, the model within the CTSR system has to compute the similarity scores in an efficient manner as the users interact with the system in real-time. In this paper, we propose an effective and efficient CTSR model that outperforms alternative models, while still providing reasonable inference runtimes. To demonstrate the capability of the proposed method in solving business problems, we compare it against alternative models using our in-house transaction data. Our findings reveal that the proposed model is the most suitable solution compared to others for our transaction data problem