A Smart Switch Configuration and Reliability Assessment Method for Large-Scale Offshore Wind Farm Electrical Collector System

With the development of offshore wind farms (OWFs) in far-offshore and deep-sea areas, each OWF could contain more and more wind turbines and cables, making it imperative to study high-reliability electrical collector system (ECS) for OWF. Enlightened by active distribution network, for OWF, we propose an ECS switch configuration that enables post-fault network recovery, along with a reliability assessment (RA) method based on optimization models. It can also determine the optimal normal state and network reconfiguration strategies to maximize ECS reliability. Case studies on several OWFs demonstrate that the proposed RA method is more computationally efficient and accurate than the traditional sequential Monte-Carlo simulation method. Moreover, the proposed switch configuration, in conjunction with the network reconfiguration strategy and proper topology, provides significant benefits to ECS reliability.Comment: 10 page

Towards Verifiable Text Generation with Evolving Memory and Self-Reflection

Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also known as hallucination. A promising solution to this issue is verifiable text generation, which prompts LLMs to generate content with citations for accuracy verification. However, verifiable text generation is non-trivial due to the focus-shifting phenomenon, the intricate reasoning needed to align the claim with correct citations, and the dilemma between the precision and breadth of retrieved documents. In this paper, we present VTG, an innovative framework for Verifiable Text Generation with evolving memory and self-reflection. VTG introduces evolving long short-term memory to retain both valuable documents and recent documents. A two-tier verifier equipped with an evidence finder is proposed to rethink and reflect on the relationship between the claim and citations. Furthermore, active retrieval and diverse query generation are utilized to enhance both the precision and breadth of the retrieved documents. We conduct extensive experiments on five datasets across three knowledge-intensive tasks and the results reveal that VTG significantly outperforms baselines

Rational Design of a Two-Photon Fluorescent Probe for Human Cytochrome P450 3A and the Visualization of Mechanism-Based Inactivation

Mechanism-based inactivation (MBI) can mediate adverse reactions and hepatotoxicity from drugs, which is a result of their conversion into highly reactive metabolites catalyzed by enzymes such as cytochrome P450 3A (CYP3A). In the present research, we optimized the key interaction domain of the fluorophore with the target protein to develop a two-photon fluorescent probe for CYP3A that is involved in the metabolism of more than half of all clinical drugs. The developed BN-1 probe exhibited appropriate selectivity and sensitivity for the semi-quantitative detection and imaging of endogenous CYP3A activity in various living systems, thereby providing a high-throughput screening system enabling evaluation of MBI-associated hepatotoxicity by CYP3A. Using BN-1 as a fluorescent molecular tool facilitates the efficient discovery and characterization of CYP3A-induced MBI in natural systems.</p

DSRC versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance

Dedicated short-range communication (DSRC) and 4G-LTE are two widely used candidate schemes for Connected Vehicle (CV) applications. It is thus of great necessity to compare these two most viable communication standards and clarify which one can meet the requirements of most V2X scenarios with respect to road safety, traffic efficiency, and infotainment. To the best of our knowledge, almost all the existing studies on comparing the feasibility of DRSC or LTE in V2X applications use software-based simulations, which may not represent realistic constraints. In this paper, a Connected Vehicle test-bed is established, which integrates the DSRC roadside units, 4G-LTE cellular communication stations, and vehicular on-board terminals. Three Connected Vehicle application scenarios are set as Collision Avoidance, Traffic Text Message Broadcast, and Multimedia File Download, respectively. A software tool is developed to record GPS positions/velocities of the test vehicles and record certain wireless communication performance indicators. The experiments have been carried out under different conditions. According to our results, 4G-LTE is more preferred for the nonsafety applications, such as traffic information transmission, file download, or Internet accessing, which does not necessarily require the high-speed real-time communication, while for the safety applications, such as Collision Avoidance or electronic traffic sign, DSRC outperforms the 4G-LTE. Document type: Articl

Visual identification of gut bacteria and determination of natural inhibitors using a fluorescent probe selective for PGP-1

PGP-1 is a bacterial hydrolase that can hydrolyze the amide bond of the L-pyroglutamate (L-pGlu) residue at the amino terminus of proteins and peptides. Guided by the biological function of PGP-1, an off-on NIR fluorescent probe DDPA was developed for the visual sensing of PGP-1 by conjugating pyroglutamic acid (recognition group) and DDAN (fluorophore). Using intestinal bacteria cultivation, eight bacteria strains with active PGP-1 were identified and cultivated efficiently using DDPA. In addition, three natural inhibitors against PGP-1 were isolated from the medical herb Psoralea corylifolia, which could be used to interfere with bacterial metabolism in the gut. As such, the fluorescent probe DDPA provides an efficient method and potential tool for the investigation of intestinal microbiota.</p

Target Enzyme-Activated Two-Photon Fluorescent Probes:A Case Study of CYP3A4 Using a Two-Dimensional Design Strategy

The rapid development of fluorescent probes for monitoring target enzymes is still a great challenge owing to the lack of efficient ways to optimize a specific fluorophore. Herein, a practical two-dimensional strategy was designed for the development of an isoform-specific probe for CYP3A4, a key cytochrome P450 isoform responsible for the oxidation of most clinical drugs. In first dimension of the design strategy, a potential two-photon fluorescent substrate (NN) for CYP3A4 was effectively selected using ensemble-based virtual screening. In the second dimension, various substituent groups were introduced into NN to optimize the isoform-selectivity and reactivity. Finally, with ideal selectivity and sensitivity, NEN was successfully applied to the real-time detection of CYP3A4 in living cells and zebrafish. These findings suggested that our strategy is practical for developing an isoform-specific probe for a target enzyme.</p

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error

Elucidation of spatial disparities of factors that affect air pollutant concentrations in industrial regions at a continental level

Industrial regions and relevant infrastructures are known to contribute to air pollutant emissions; thus, a detailed investigation of the air pollutant concentrations of a region based on specific land uses, with spatial reasoning, can support smart regional planning. However, the current knowledge about the spatial patterns that indicate the relationship between the anthropological or environmental features and the air pollutant concentrations in industrial regions is limited. Thus, in this study, we aimed to identify the factors that affect air-pollutant concentrations due to local spatial impacts in industrial regions across Australia. Considering the large spatial scale, the impact of a global factor can be overwhelmed by another factor due to local spatial impacts, and the phenomenon is a kind of spatial disparity. We developed a novel set of methods, including a point-of-interests-based spatial identification method and geographically weighted regression (with standardised coefficients), to: (i) identify the industrial regions in the study area, (ii) collect the remote sensing factors, and (iii) identify the factors that affect the spatial disparity of air-pollutant concentrations in industrial regions. The results indicated a significant spatial disparity in the air pollutant concentrations in the industrial region, at a continental scale. Anthropogenic factors significantly affected the spatial patterns of air pollutant concentrations in the industrial regions that were remote to cities, whereas meteorological and topographical factors had significant impacts on the air pollutant distributions in urban industrial regions. Furthermore, within the nationwide industrial lands, drives of the relatively high concentrations of ozone and sulphur dioxide, the drivers of the air pollutant concentrations were environmental factors; high concentrations of nitrogen dioxide were more associated with the topographical features of the region. The methods proposed in this study can serve as a reliable framework for analysing the air quality of industrial regions and can also, supplement future studies on emissions reduction in industrial parks

Estimating the Precursor Frequency of Naive Antigen-specific CD8 T Cells

The constraint of fitting a diverse repertoire of antigen specificities in a limited total population of lymphocytes results in the frequency of naive cells specific for any given antigen (defined as the precursor frequency) being below the limit of detection by direct measurement. We have estimated this precursor frequency by titrating a known quantity of antigen-specific cells into naive recipients. Adoptive transfer of naive antigen-specific T cell receptor transgenic cells into syngeneic nontransgenic recipients, followed by stimulation with specific antigen, results in activation and expansion of both donor and endogenous antigen-specific cells in a dose-dependent manner. The precursor frequency is equal to the number of transferred cells when the transgenic and endogenous responses are of equal magnitude. Using this method we have estimated the precursor frequency of naive CD8 T cells specific for the H-2Db–restricted GP33–41 epitope of LCMV to be 1 in 2 × 105. Thus, in an uninfected mouse containing ∼2-4 × 107 naive CD8 T cells we estimate there to be 100–200 epitope-specific cells. After LCMV infection these 100–200 GP33-specific naive CD8 T cells divide >14 times in 1 wk to reach a total of ∼107 cells. Approximately 5% of these activated GP33-specific effector CD8 T cells survive to generate a memory pool consisting of ∼5 × 105 cells. Thus, an acute LCMV infection results in a >1,000-fold increase in precursor frequency of DbGP33-specific CD8 T cells from 2 × 102 naive cells in uninfected mice to 5 × 105 memory cells in immunized mice

Molecular Design Strategy to Construct the Near-Infrared Fluorescent Probe for Selectively Sensing Human Cytochrome P450 2J2

Cytochrome P450 2J2 (CYP2J2), a key enzyme responsible for oxidative metabolism of various xenobiotics and endogenous compounds, participates in a diverse array of physiological and pathological processes in humans. Its biological role in tumorigenesis and cancer diagnosis remains poorly understood, owing to the lack of molecular tools suitable for real-time monitoring CYP2J2 in complex biological systems. Using molecular design principles we were able to modify the distance between the catalytic unit and metabolic recognition moiety, allowing us to develop a CYP2J2 selective fluorescent probe using a near-infrared fluorophore (E)-2-(2-(6-hydroxy-2, 3-dihydro-1H-xanthen-4-yl)vinyl)-3,3- dimethyl-1-propyl-3H-indol-1-ium iodide (HXPI). To improve the reactivity and isoform specificity, a self-immolative linker was introduced to the HXPI derivatives in order to better fit the narrow substrate channel of CYP2J2, the modification effectively shortened the spatial distance between the metabolic moiety (O-alkyl group) and catalytic center of CYP2J2. After screening a panel of O-alkylated HXPI derivatives, BnXPI displayed the best combination of specificity, sensitivity and applicability for detecting CYP2J2 in vitro and in vivo. Upon O-demethylation by CYP2J2, a self-immolative reaction occurred spontaneously via 1,6-elimination of p-hydroxybenzyl resulting in the release of HXPI. Allowing BnXPI to be successfully used to monitor CYP2J2 activity in real-time for various living systems including cells, tumor tissues, and tumor-bearing animals. In summary, our practical strategy could help the development of a highly specific and broadly applicable tool for monitoring CYP2J2, which offers great promise for exploring the biological functions of CYP2J2 in tumorigenesis.</p