High-resolution dynamic inversion imaging with motion-aberrations-free using optical flow learning networks.

Dynamic optical imaging (e.g. time delay integration imaging) is troubled by the motion blur fundamentally arising from mismatching between photo-induced charge transfer and optical image movements. Motion aberrations from the forward dynamic imaging link impede the acquiring of high-quality images. Here, we propose a high-resolution dynamic inversion imaging method based on optical flow neural learning networks. Optical flow is reconstructed via a multilayer neural learning network. The optical flow is able to construct the motion spread function that enables computational reconstruction of captured images with a single digital filter. This works construct the complete dynamic imaging link, involving the backward and forward imaging link, and demonstrates the capability of the back-ward imaging by reducing motion aberrations

STR-903: UNSUPERVISED NOVELTY DETECTION BASED STRUCTURAL DAMAGE DETECTION METHOD

Many structural damage detection methods using machine learning algorithms and clustering methods have been proposed and developed in recent years. Novelty detection is a common method that is based on an unsupervised learning technique to detect structural damage. The detection process involves applying the novelty detection algorithm to recognize abnormal data from the testing data sets. In order to make these algorithms capable of identifying abnormal data, sufficient normal data must first be obtained and used as training data. It is the fact that sufficient normal data is relatively convenient to measure compared to abnormal data for large-scale civil structures. Abnormal data from the testing data sets can be identified by using the well-trained normal model established by the algorithms. In this paper, a machine learning based novelty detection method called the Density Peaks based Fast Clustering Algorithm (DPFCA) is introduced and some improvements to this algorithm are made to increase the precision of detecting and localizing the damage in an experimental structure. Feature extraction is also an important factor in the process of damage detection. Thus, two damage-sensitive features such as crest factor, and transmissibility are extracted from the measured responses in the experiments. Experimental results showed good performance of the innovative method in detecting and locating the structural damage positions in various scenarios

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment

The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn/

Self-assembly of hydrofluorinated Janus graphene monolayer:a versatile route for designing novel Janus nanoscrolls

With remarkably interesting surface activities, two-dimensional Janus materials arouse intensive interests recently in many fields. We demonstrate by molecular dynamic simulations that hydrofluorinated Janus graphene (J-GN) can self-assemble into Janus nanoscroll (J-NS) at room temperature. The van der Waals (vdW) interaction and the coupling of C-H/π/C-F interaction and π/π interaction are proven to offer the continuous driving force of self-assembly of J-GN. The results show that J-GN can self-assemble into various J-NSs structures, including arcs, multi-wall J-NS and arm-chair-like J-NS by manipulating its original geometry (size and aspect ratio). Moreover, we also investigated self-assembly of hydrofluorinated J-GN and Fe nanowires (NWs), suggesting that Fe NW is a good alternative to activate J-GN to form J-NS. Differently, the strong vdW interaction between J-GN and Fe NW provides the main driving force of the self-assembly. Finally, we studied the hydrogen sorption over the formed J-NS with a considerable interlayer spacing, which reaches the US DOE target, indicating that J-NS is a promising candidate for hydrogen storage by controlling the temperature of system. Our theoretical results firstly provide a versatile route for designing novel J-NS from 2D Janus nanomaterials, which has a great potential application in the realm of hydrogen storage/separation

Analysis of gut microbiotal diversity in healthy young adults in Sunan County, Gansu Province, China

ObjectiveTo examine gut microbiotal diversity in the Han Chinese and Yugur populations of Sunan County, Gansu Province, living in the same environmental conditions, and to analyze possible causes of differences in diversity.MethodsWe selected 28 people, ages 18–45 years old, all of whom were third-generation pure Yugur or Han Chinese from Sunan County. Fresh fecal samples were collected, and total bacterial deoxyribonucleic acid (DNA) was extracted. We performed 16S ribosomal ribonucleic acid (16S rRNA) high-throughput sequencing (HTS) and bioinformatics to study the relationships among between gut microbiota structure, genetics, and dietary habits in Yugur and Han Chinese subjects.ResultsWe found 350 differential operational taxonomic units (OTUs) in Han Chinese and Yugur gut microbiota, proving that gut microbiota differed between the two populations. That were less abundant among Yugurs than Han Chinese were Prevotella_9 and Alloprevotella. That were more abundant among Yugurs than Han Chinese were Anaerostipes and Christensenellaceae_R-7_group. And they were significantly associated with a high-calorie diet In addition. we found differences in predicted gut microbiota structural functions (The main functions were metabolic and genetic information) between the two populations.ConclusionYugur subjects demonstrated differences in gut microbiotal structure from Han Chinese subjects, and this difference influenced by dietary and may be influenced by genetic influences. This finding will provide a fundamental basis for further study of the relationships among gut microbiota, dietary factors, and disease in Sunan County

One-Step Preparation of High Performance TiO 2 /CNT/CQD Nanocomposites Bactericidal Coating with Ultrasonic Radiation

© 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/).As an environmental semiconductor material, TiO2 has important applications in the fields of environmental protection and water treatment. The preparation of P25 particles into nano-functional material films with a high specific surface area has always been a bottleneck limiting its large-scale application. In this paper, a one-step method of preparing TiO2 nanocomposites by doping carbon nanotube (CNT) and carbon quantum dots (CQD) with tetrabutyltitanate and P25 TiO2 under ultrasonic radiation is proposed to synthesize a novel antifouling material, which both eliminates the bacterium of Escherichia coli and shows good photoelectric properties, indicating a great value for the industrial promotion of TiO2/CNT. This mesoporous composite exhibits a high specific surface area of 78.07 M2/g (BET) and a tested pore width range within 10–120 nm. The surface morphology of this composite is characterized by TEM and the microstructure is characterized through XRD. This preparation method can fabricate P25 particles into a nano-functional material film with a high specific surface area at a very low cost.Peer reviewe

Programming Bacteria With Light—Sensors and Applications in Synthetic Biology

Photo-receptors are widely present in both prokaryotic and eukaryotic cells, which serves as the foundation of tuning cell behaviors with light. While practices in eukaryotic cells have been relatively established, trials in bacterial cells have only been emerging in the past few years. A number of light sensors have been engineered in bacteria cells and most of them fall into the categories of two-component and one-component systems. Such a sensor toolbox has enabled practices in controlling synthetic circuits at the level of transcription and protein activity which is a major topic in synthetic biology, according to the central dogma. Additionally, engineered light sensors and practices of tuning synthetic circuits have served as a foundation for achieving light based real-time feedback control. Here, we review programming bacteria cells with light, introducing engineered light sensors in bacteria and their applications, including tuning synthetic circuits and achieving feedback controls over microbial cell culture