Investigation of a proposed long span timber floor for non-residential applications

University of Technology, Sydney. Faculty of Engineering and Information Technology.Design of floor systems for commercial and multi-residential buildings in many parts of the world is currently dominated by the use of structural materials other than timber, such as reinforced concrete systems. Recent research in Australia has shown that the major barriers to using timber in non-residential buildings are the fire performance and the lack of designer confidence in commercial and industrial timber-based constructions. In this regard, significant research initiatives have commenced in Australia and New Zealand with the aim of developing timber and timber hybrid systems for large span commercial and industrial applications. This PhD research provides a detailed procedure for designing and investigating the short term static behaviour of a proposed long span timber floor system for non-residential applications that meets serviceability and ultimate limit design criteria, with the use of timber as the only structural load bearing part of the system. The specimen’s responses to long-term loading, in-plane loading, dynamic excitation, cyclic loading and loading history are outside the scope of this PhD research. Moreover, other aspects of performance such as assessment of acoustic performance, dynamic performance and the possible interconnection systems alongside floor modules are not covered in the scope of this research project. In this study the behaviour of two types of LVL are investigated through a number of experimental and analytical tests. As a result of the tension and compression tests, a suitable constitutive law is developed which can accurately capture the stress-strain relationship and the failure behaviour of LVL, and it can also be incorporated into FE analysis of any LVL beam with similar structural features to the tested specimens. Further, the results of the full scale four point bending tests on LVL sections are used to identify the behaviour of LVL up to the failure point and to develop a finite element model to capture the behaviour and failure of LVL. Moreover, after investigating the long span timber floors, one system is proposed to be fabricated for the extensive experimental and numerical investigation. The experimental investigation involved subjecting the full scale proposed floor modules (6m and 8m clear span LVL modules) to both serviceability and ultimate limit state static loading to assess the strength and serviceability performance of the proposed system. A continuum-based finite element model is also developed to capture the behaviour and failure of the long span LVL modules and to adequately predict the serviceability and ultimate limit performance of the proposed floor system. To evaluate the partially-composite strength and serviceable performance of LVL floor system, a series of push-out tests are conducted on the fabricated timber connections using normal screws as the shear connectors, and the stiffness of the connections are assessed at serviceability and ultimate limit state. A number of LVL beams (3.5m “T” shaped beams) were also fabricated using only normal screws as the load bearing shear connectors at the interfaces, and are tested under serviceability and ultimate limit state loads with different screw spacing. Furthermore, a closed-form prediction analysis is conducted to calculate the partially-composite ultimate load of the beams. A comparison between the experimental results and the closed-from predicted results is undertaken, and the results are used for predicting the partially-composite behaviour of long span 6m and 8m LVL modules. The results of the full scale experimental tests together with the numerical investigation provide a robust model for predicting the performance of any timber beams with similar structural features to the proposed system while the dimensions and spans can be varied according to special requirements such as dynamic performance or fire resistance requirements

Ultimate performance of timber connection with normal screws

This paper presents the results of experimental push-out tests on two different types of timber composite connections using only normal screws as the shear connecter. The push out tests were conducted based on Eurocode 5 recommendations and the load-slip responses obtained from lab tests are used to determine the stiffness of the connections at serviceability, ultimate and near collapse levels, and the performance of the connections are assessed at ultimate load. Moreover, an analytical model is derived for each type of connection based on the experimental results and using a non linear regression, which can be implemented into non-linear FE analysis of timber beams with normal screws. © 2013 Taylor & Francis Group

Langerhans cells and cDC1s play redundant roles in mRNA-LNP induced protective anti-influenza and anti-SARS-CoV-2 immune responses

Nucleoside modified mRNA combined with Acuitas Therapeutics\u27 lipid nanoparticles (LNPs) has been shown to support robust humoral immune responses in many preclinical animal vaccine studies and later in humans with the SARS-CoV-2 vaccination. We recently showed that this platform is highly inflammatory due to the LNPs\u27 ionizable lipid component. The inflammatory property is key to support the development of potent humoral immune responses. However, the mechanism by which this platform drives T follicular helper (Tfh) cells and humoral immune responses remains unknown. Here we show that lack of Langerhans cells or cDC1s neither significantly affected the induction of PR8 HA and SARS-CoV-2 RBD-specific Tfh cells and humoral immune responses, nor susceptibility towards the lethal challenge of influenza and SARS-CoV-2. However, the combined deletion of these two DC subsets led to a significant decrease in the induction of PR8 HA and SARS-CoV-2 RBD-specific Tfh cell and humoral immune responses. Despite these observed defects, these mice remained protected from lethal influenza and SARS-CoV-2 challenges. We further found that IL-6, unlike neutrophils, was required to generate normal Tfh cells and antibody responses, but not for protection from influenza challenge. In summary, here we bring evidence that the mRNA-LNP platform can support the induction of protective immune responses in the absence of certain innate immune cells and cytokines

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

Geostatistical Models for the Prediction of Water Supply Network Failures in Bogotá, Integrating Machine Learning Algorithms

[EN] Currently new strategies of spatial referencing, data analysis, and machine learning methods are integrated with Geographical Information Systems (GISs) to understand specific characteristics and water supply dynamics. This work explores the variables that can cause spacial failures and potential risk areas with application to a zone in the Bogotá water supply network. Machine learning algorithms are proposed to generate prediction models and potential failure maps. A sensitivity analysis was held to identify the model with the best fit for the estimation. This study will allow water supply decisions makers to focalize their efforts in the field.[ES] Actualmente se buscan nuevas estrategias y/o metodologías basadas en la integración de los Sistemas de Información Geográfica (SIGs) como forma de georeferenciacion espacial y visualización de las variables analizadas, junto con métodos de aprendizaje automático (Machine Learning) que permitan entender características puntuales, variables influyentes y dinámicas de los sistemas de abastecimiento de agua potable.En este trabajo se hace la identificación espacial de los fallos y zonas potenciales de riesgo que se presentan en una zona de la red de abastecimiento de Bogotá, explorando las variables que puedan tener mayor incidencia en los mismos. Se propone el uso de algoritmos de aprendizaje automático para la generación de modelos de predicción y la elaboración de mapas de fallos potenciales, identificando, a través de un análisis de sensibilidad, cuál de estos modelos presenta un mejor ajuste en la estimación. Este estudio permite a los gestores del abastecimiento una localización precisa y eficiente de los fallos en la red, apoyando el proceso de toma de decisiones.Navarrete-López, CF.; Calderón-Rivera, D.; Díaz Arévalo, JL.; Herrera Fernández, AM.; Izquierdo Sebastián, J. (2018). Modelos geoestadísticos para la predicción de fallos de una zona de la red de abastecimiento de agua de Bogotá, integrando algoritmos de Machine Learning. Social Science Research Network. 1-8. https://doi.org/10.2139/ssrn.3113048S1

Measurement of isotopic separation of argon with the prototype of the cryogenic distillation plant Aria for dark matter searches

The Aria cryogenic distillation plant, located in Sardinia, Italy, is a key component of the DarkSide-20k experimental program for WIMP dark matter searches at the INFN Laboratori Nazionali del Gran Sasso, Italy. Aria is designed to purify the argon, extracted from underground wells in Colorado, USA, and used as the DarkSide-20k target material, to detector-grade quality. In this paper, we report the first measurement of argon isotopic separation by distillation with the 26 m tall Aria prototype. We discuss the measurement of the operating parameters of the column and the observation of the simultaneous separation of the three stable argon isotopes: 36Ar , 38Ar , and 40Ar . We also provide a detailed comparison of the experimental results with commercial process simulation software. This measurement of isotopic separation of argon is a significant achievement for the project, building on the success of the initial demonstration of isotopic separation of nitrogen using the same equipment in 2019

Directionality of nuclear recoils in a liquid argon time projection chamber

The direct search for dark matter in the form of weakly interacting massive particles (WIMP) is performed by detecting nuclear recoils (NR) produced in a target material from the WIMP elastic scattering. A promising experimental strategy for direct dark matter search employs argon dual-phase time projection chambers (TPC). One of the advantages of the TPC is the capability to detect both the scintillation and charge signals produced by NRs. Furthermore, the existence of a drift electric field in the TPC breaks the rotational symmetry: the angle between the drift field and the momentum of the recoiling nucleus can potentially affect the charge recombination probability in liquid argon and then the relative balance between the two signal channels. This fact could make the detector sensitive to the directionality of the WIMP-induced signal, enabling unmistakable annual and daily modulation signatures for future searches aiming for discovery. The Recoil Directionality (ReD) experiment was designed to probe for such directional sensitivity. The TPC of ReD was irradiated with neutrons at the INFN Laboratori Nazionali del Sud, and data were taken with 72 keV NRs of known recoil directions. The direction-dependent liquid argon charge recombination model by Cataudella et al. was adopted and a likelihood statistical analysis was performed, which gave no indications of significant dependence of the detector response to the recoil direction. The aspect ratio R of the initial ionization cloud is estimated to be 1.037 +/- 0.027 and the upper limit is R < 1.072 with 90% confidence levelComment: 20 pages, 10 figures, submitted to Eur. Phys. J.

Sensitivity projections for a dual-phase argon TPC optimized for light dark matter searches through the ionization channel

Dark matter lighter than 10 GeV/c$^2$ encompasses a promising range of candidates. A conceptual design for a new detector, DarkSide-LowMass, is presented, based on the DarkSide-50 detector and progress toward DarkSide-20k, optimized for a low-threshold electron-counting measurement. Sensitivity to light dark matter is explored for various potential energy thresholds and background rates. These studies show that DarkSide-LowMass can achieve sensitivity to light dark matter down to the solar neutrino floor for GeV-scale masses and significant sensitivity down to 10 MeV/c$^2$ considering the Migdal effect or interactions with electrons. Requirements for optimizing the detector's sensitivity are explored, as are potential sensitivity gains from modeling and mitigating spurious electron backgrounds that may dominate the signal at the lowest energies