A STOCHASTIC MODEL FOR BUILDING OCCUPANCY SIMULATION TO DETERMINING RURAL RESIDENTIAL HEATING DEMAND IN NORTHWEST CHINA

Proceedings of the 2021 International Workshop on Modern Science and Technology; September 29, 2021conference pape

Federal Recovery Funds Allocated

Figure S1. Overview of the informatics pipeline used to the identify lincRNAs in goat. (PDF 216 kb

Dynamic increase factor (DIF) for concrete in compression and tension in FE modelling with a local concrete model

The dynamic increase factor (DIF) in the strength of concrete-like materials has been a subject of extensive investigation and debate for many years. It now tends to be generally accepted that the compression DIF as observed from standard sample tests is mainly attributable to the dynamic structural effect, whereas for concrete under tension the DIF is deemed to be governed by different mechanisms, probably more from the material and micro-fracture level. This paper presents a numerical study on the uniaxial compression and tension DIF, with a particular focus on how the DIF, irrespective of its cause, should be included in an appropriate manner in the finite element (FE) modelling with a local concrete model. The inevitable mesh-dependency issue due to numerical localisation and its implications on rate effects are examined in detail. A mesh-objective modification on the standard sample tested tension DIF is proposed with the aim to achieve relatively mesh independent analysis in the FE models where high strain-rate tension is involved. The results demonstrate that the proposed approach is effective, and reliable modelling results can be achieved with the proposed DIF modelling scheme for the local concrete model

Surface-enhanced Raman spectroscopy detection of organic molecules and in situ monitoring of organic reactions by ion-induced silver nanoparticle clusters

AbstractSurface-enhanced Raman spectroscopy (SERS) finds wide applications in the field of organic molecule detection. However, reliable SERS detection of organic molecules and in situ monitoring of organic reactions under natural conditions by metal colloids are still challenging due to the formation of unstable nanoparticle clusters in solution and the low solubility of the organic molecules. Here, we approach the problems by introducing calcium ions to aggregate silver nanoparticles to form stable hot spots and acetone to promote uniform distribution of organic molecules on the nanoparticle surface. Significantly, our method exhibits stable SERS detection of up to 6 types of organic molecules in liquid. With acetone signals as an internal standard, we are able to determine molecule concentrations as well as monitor 3 kinds of organic reactions in situ. Our method shows potential for biomedical analysis, environmental analysis, and organic catalysis research.Abstract Surface-enhanced Raman spectroscopy (SERS) finds wide applications in the field of organic molecule detection. However, reliable SERS detection of organic molecules and in situ monitoring of organic reactions under natural conditions by metal colloids are still challenging due to the formation of unstable nanoparticle clusters in solution and the low solubility of the organic molecules. Here, we approach the problems by introducing calcium ions to aggregate silver nanoparticles to form stable hot spots and acetone to promote uniform distribution of organic molecules on the nanoparticle surface. Significantly, our method exhibits stable SERS detection of up to 6 types of organic molecules in liquid. With acetone signals as an internal standard, we are able to determine molecule concentrations as well as monitor 3 kinds of organic reactions in situ. Our method shows potential for biomedical analysis, environmental analysis, and organic catalysis research

Managed Geo-Distributed Feature Store: Architecture and System Design

Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature stores, different teams across various business groups would maintain the above process independently, which can lead to conflicting and duplicated features in the system. Data scientists find it hard to search for and reuse existing features and it is painful to maintain version control. Furthermore, feature correctness violations related to online (inferencing) - offline (training) skews and data leakage are common. Although the machine learning community has extensively discussed the need for feature stores and their purpose, this paper aims to capture the core architectural components that make up a managed feature store and to share the design learning in building such a system.Comment: All the authors are from the AzureML Feature Store product group and are listed in alphabetical order. Bhala Ranganathan: System architect and tech lead of AzureML Feature Store. Feng Pan, Qianjun Xu: Engineering managers. Sethu Raman: Product Manager of AzureML Feature Store who structured and organized the product vision and specification

Antagonism of rhizosphere Trichoderma brevicompactum DTN19 against the pathogenic fungi causing corm rot in saffron (Crocus sativus L.) in vitro

IntroductionCorm rot in saffron (Crocus sativus L.) significantly impacts yield and quality. Non-toxic fungi, particularly Trichoderma species, are valuable for biological control due to their production of diverse and biologically active secondary metabolites.MethodsThis study aimed to isolate an effective antagonistic fungus against the pathogenic fungi causing corm rot in saffron. Four pathogenic fungi (Fusarium oxysporum, Fusarium solani, Penicillium citreosulfuratum, and Penicillium citrinum) were isolated from diseased saffron bulbs in Chongming. Initial screening through dual culture with these pathogens re-screening from rhizosphere soil samples of C. sativus based on its inhibitory effects through volatile, nonvolatile, and fermentation broth metabolites. The inhibitory effect of biocontrol fungi on pathogenic fungi in vitro was evaluated by morphological observation and molecular biology methods.ResultsAntagonistic fungi were identified as Trichoderma brevicompactum DTN19. F. oxysporum was identified as the most severe pathogen. SEM (scanning electron microscope) and TEM (transmission electron microscope) observations revealed that T. brevicompactum DTN19 significantly inhibited the growth and development of F. oxysporum mycelium, disrupting its physiological structure and spore formation. Additionally, T. brevicompactum DTN19 demonstrated nitrogen fixation and production of cellulase, IAA (Indole acetic acid), and siderophores. Whole-genome sequencing of strain DTN19 revealed genes encoding protease, cellulase, chitinase, β-glucosidase, siderophore, nitrogen cycle, and sulfate transporter-related proteinsDiscussionT. brevicompactum DTN19 may inhibit the propagation of pathogenic fungi by destroying their cell walls or producing antibiotics. It can also produce IAA and iron carriers, which have the potential to promote plant growth. Overall, T. brevicompactum DTN19 showed the development prospect of biological agents

Light-triggered nitric oxide release and structure transformation of peptide for enhanced intratumoral retention and sensitized photodynamic therapy

Tumor-targeted delivery of nanomedicine is of great importance to improve therapeutic efficacy of cancer and minimize systemic side effects. Unfortunately, nowadays the targeting efficiency of nanomedicine toward tumor is still quite limited and far from clinical requirements. In this work, we develop an innovative peptide-based nanoparticle to realize light-triggered nitric oxide (NO) release and structural transformation for enhanced intratumoral retention and simultaneously sensitizing photodynamic therapy (PDT). The designed nanoparticle is self-assembled from a chimeric peptide monomer, TPP-RRRKLVFFK-Ce6, which contains a photosensitive moiety (chlorin e6, Ce6), a β-sheet-forming peptide domain (Lys-Leu-Val-Phe-Phe, KLVFF), an oligoarginine domain (RRR) as NO donor and a triphenylphosphonium (TPP) moiety for targeting mitochondria. When irradiated by light, the constructed nanoparticles undergo rapid structural transformation from nanosphere to nanorod, enabling to achieve a significantly higher intratumoral accumulation by 3.26 times compared to that without light irradiation. More importantly, the conversion of generated NO and reactive oxygen species (ROS) in a light-responsive way to peroxynitrite anions (ONOO-) with higher cytotoxicity enables NO to sensitize PDT in cancer treatment. Both in vitro and in vivo studies demonstrate that NO sensitized PDT based on the well-designed transformable nanoparticles enables to eradicate tumors efficiently. The light-triggered transformable nanoplatform developed in this work provides a new strategy for enhanced intratumoral retention and improved therapeutic outcome