The Application of Object-Oriented Views to an Engineering Environment.

With the increasing popularity of object-oriented technology, object-oriented database systems are being used in design environments as central repositories. In this thesis, we investigate the role of versioning and the characteristics of design databases in design environments. In an effort to improve the configuration management scheme in a design environment, we also investigate the use of database views as a possible configuration tool. We propose a unified version management scheme that facilitates cooperative team work and show that the use of database views provides a powerful configuration management scheme for a design environment

Region-Object Relation-Aware Dense Captioning via Transformer

Dense captioning provides detailed captions of complex visual scenes. While a number of successes have been achieved in recent years, there are still two broad limitations: 1) most existing methods adopt an encoder-decoder framework, where the contextual information is sequentially encoded using long short-term memory (LSTM). However, the forget gate mechanism of LSTM makes it vulnerable when dealing with a long sequence and 2) the vast majority of prior arts consider regions of interests (RoIs) equally important, thus failing to focus on more informative regions. The consequence is that the generated captions cannot highlight important contents of the image, which does not seem natural. To overcome these limitations, in this article, we propose a novel end-to-end transformer-based dense image captioning architecture, termed the transformer-based dense captioner (TDC). TDC learns the mapping between images and their dense captions via a transformer, prioritizing more informative regions. To this end, we present a novel unit, named region-object correlation score unit (ROCSU), to measure the importance of each region, where the relationships between detected objects and the region, alongside the confidence scores of detected objects within the region, are taken into account. Extensive experimental results and ablation studies on the standard dense-captioning datasets demonstrate the superiority of the proposed method to the state-of-the-art methods

Research Trends in Non Point Source during 1975-2010

AbstractAccording to the samples of 2924 articles about non point source of SCI and SSCI databases from 1975 to 2010, this study analysed the articles in the growth trend of article outputs, subject categories and journals, international collaborations, geographic distribution and scientific research issues by using bibliometric analysis. The results showed that non point source research steadily increased over the past 35 years and the annual number of articles published in 2010 was 79 times of that in 1975. Non point source was involved into 67 kinds of subjects and appeared in 451 journals. The main study area was concentrated in North America and Europe, following by East Asia. There were 79 countries/territories participated in non point source research, and USA was the largest contributor in non point source research and had a central position in collaboration networks. A keyword analysis indicated that water quality, non point pollutions, and watershed were the hottest issues of non point source research; “GIS, “watershed management”, “modeling”, “simulation”, “monitoring”, and “remote sensing” were the most popular research methods; and “agriculture”, “land use”, “runoff”, and “pollution” were the leading causes of non point pollution

Liquid Crystal-Solid Interface Structure at the Antiferroelectric-Ferroelectric Phase Transition

Total Internal Reflection (TIR) is used to probe the molecular organization at the surface of a tilted chiral smectic liquid crystal at temperatures in the vicinity of the bulk antiferroelectric-ferroelectric phase transition. Data are interpreted using an exact analytical solution of a real model for ferroelectric order at the surface. In the mixture T3, ferroelectric surface order is expelled with the bulk ferroelectric-antiferroelectric transition. The conditions for ferroelectric order at the surface of an antiferroelectric bulk are presented

Optimization of isolation and purification of total flavonoids from Ardisia mamillata Hance roots using macroporous resins, and determination of their antioxidant activity

Purpose: To isolate, purify and determine the antioxidant property of total flavonoids from the roots of Ardisia mamillata, so as to provide a  theoretical basis for development of natural antioxidants.Methods: Macroporous resin was used to optimize the isolation and  purification of total flavonoids, taking adsorption rate and resolution rate as evaluation indices. The antioxidant property of the purified total flavonoids was determined using 1,1-diphenyl-2-picrylhydrazyl radical 2,2-diphenyl-1-(2,4,6- trinitrophenyl)hydrazyl (DPPH) radical scavenging activity.Results: The best conditions for separation and purification of total  flavonoids from Ardisia mamillata roots were: use of ADS-7 resin, loading total flavonoid concentration of 0.8896 mg/mL, loading buffer flow rate of 1.5 mL/min, loading buffer pH of 4.48, elution ethanol concentration of 60 %, and flow rate of 2.5 mL/min. Under these conditions, the degree of purification of total flavonoids of Ardisia mamillata root was 76.43 ± 0.36 %, adsorption rate was 96.52 ± 0.19 %, while resolution rate was 99.31 ± 0.27 %. When the concentration of the purified total flavonoids was 4.0 mg/mL, its DPPH radical scavenging activity was stronger than that of the standard, butylated hydroxytoluene (BHT), but lower than that of vitamin C.Conclusion: ADS-7 resin is the best macroporous resin for the purification of total flavonoids from the radix of Ardisia mamillata Hance, under the  optimized conditions. The purified total flavonoids of Ardisia mamillata root have stronger DPPH radical scavenging ability than the standard, BHT.Keywords: Szechwan raspberry root, Flavonoids, Macroporous adsorption resin, ADS-7 resin, Purification, Antioxidan

Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models

Pretrained vision-language models, such as CLIP, have demonstrated strong generalization capabilities, making them promising tools in the realm of zero-shot visual recognition. Visual relation detection (VRD) is a typical task that identifies relationship (or interaction) types between object pairs within an image. However, naively utilizing CLIP with prevalent class-based prompts for zero-shot VRD has several weaknesses, e.g., it struggles to distinguish between different fine-grained relation types and it neglects essential spatial information of two objects. To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. Specifically, RECODE first decomposes each predicate category into subject, object, and spatial components. Then, it leverages large language models (LLMs) to generate description-based prompts (or visual cues) for each component. Different visual cues enhance the discriminability of similar relation categories from different perspectives, which significantly boosts performance in VRD. To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues. Extensive experiments on four VRD benchmarks have demonstrated the effectiveness and interpretability of RECODE

ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation

Pervasive mobile AI applications primarily employ one of the two learning paradigms: cloud-based learning (with powerful large models) or on-device learning (with lightweight small models). Despite their own advantages, neither paradigm can effectively handle dynamic edge environments with frequent data distribution shifts and on-device resource fluctuations, inevitably suffering from performance degradation. In this paper, we propose ECLM, an edge-cloud collaborative learning framework for rapid model adaptation for dynamic edge environments. We first propose a novel block-level model decomposition design to decompose the original large cloud model into multiple combinable modules. By flexibly combining a subset of the modules, this design enables the derivation of compact, task-specific sub-models for heterogeneous edge devices from the large cloud model, and the seamless integration of new knowledge learned on these devices into the cloud model periodically. As such, ECLM ensures that the cloud model always provides up-to-date sub-models for edge devices. We further propose an end-to-end learning framework that incorporates the modular model design into an efficient model adaptation pipeline including an offline on-cloud model prototyping and training stage, and an online edge-cloud collaborative adaptation stage. Extensive experiments over various datasets demonstrate that ECLM significantly improves model performance (e.g., 18.89% accuracy increase) and resource efficiency (e.g., 7.12x communication cost reduction) in adapting models to dynamic edge environments by efficiently collaborating the edge and the cloud models

Meson Mixing in Pion Superfluid

We investigate meson mixing and meson coupling constants in pion superfluid in the framework of two flavor NJL model at finite isospin density. The mixing strength develops fast with increasing isospin chemical potential, and the coupling constants in normal phase and in the pion superfluid phase behave very differently.Comment: 6 pages, 4 figures. Updates from version 2: 1, Correct Some language mistakes and Some errors in the cited references. 2, Rewrite the last sentence in the summary to indicate a possible way to measure the isospin-asymmetry related meson propertie

Investigation of the Free-Fall Dynamic Behavior of a Rectangular Wing with Variable Center of Mass Location and Variable Moment of Inertia

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).In recent years, the air-drop launch technology of near-space UAVs has attracted much attention. Between downfall from the carrier and the flight control system’s initiation, the UAV presents free-fall movement. This free-fall process is very important for the control effect of the flight control system and is also crucial for the safety of the UAV and the carrier. Focus is required on two important dynamic parameters of the UAV: the moment of inertia and the center of mass position. In this paper, we used a quasi-steady model proposed by predecessors to address the flat-plate falling problem with modifications to describe the freely falling motion of the wing. Computational fluid dynamics (CFD) were used to simulate the free-fall movement of the wing with various parameters, and the wing release behavior was analyzed to check the quasi-steady model. Research shows that the movement characteristics of the falling wing are mostly reflected in the longitudinal plane, and the developed quasi-steady analytical model can more accurately describe the dynamic behavior of free-fall to some extent. By using CFD methods, we further investigated the aerodynamic performance of the free-fall wing. The results show that the wing mainly presents tumbling and fluttering motion. Changing the moment of inertia around the tumbling axis changes the tumbling frequency and the time point as the wing enters tumbling. In contrast, changing the position of the center of mass significantly changes the form of falling and makes the free-fall motion more complex. Therefore, it is necessary to carefully configure the center of mass in the UAV design process.Peer reviewe

Early and late rice identification from Tiangong- 2 wide band images based on CNN

The wide band images acquired from the Tiangong-2 space laboratory covers many spectral bands such as visible light, shortwave infrared and thermal infrared. These high-quality images can be used for space science experiments such as earth observation. In this paper, we use CNN (convolutional neural networks) to extract the spectral features of different landcover from the wide band images, then identify the early rice and the late rice accurately in Huarong County, Hunan Province, China. With advanced techniques such as deep learning, the spatial distribution information of crops can be effectively obtained from the wide band images which can provide data services for agricultural production management