The organization of four units in ninth grade home economics

Thesis (Ed.M.)--Boston Universit

1865-08-26 Attorney James Schouler writes on behalf of Private Charles Stade of Company B regarding bounty payment

https://digitalmaine.com/cw_me_1st_heavy_corr/1435/thumbnail.jp

1865-04-26 James Schouler inquires about the welfare of Patrick Lang

https://digitalmaine.com/cw_me_16th_regiment_corr/1405/thumbnail.jp

Housing situation and healthcare for patients in a psychiatric centre in Berlin, Germany: a cross-sectional patient survey

OBJECTIVE: To determine the housing situation among people seeking psychiatric treatment in relation to morbidity and service utilisation. DESIGN: Cross-sectional patient survey. SETTING: Psychiatric centre with a defined catchment area in Berlin, Germany, March-September 2016. PARTICIPANTS: 540 psychiatric inpatients including day clinics (43.2% of all admitted patients in the study period (n=1251)). MAIN OUTCOME MEASURES: Housing status 30 days prior the interview as well as influencing variables including service use, psychiatric morbidity and sociodemographic variables. RESULTS: In our survey, 327 participants (68.7%) currently rented or owned an own apartment; 62 (13.0%) reported to be homeless (living on the street or in shelters for homeless or refugees); 87 (18.3%) were accommodated in sociotherapeutic facilities. Participants without an own apartment were more likely to be male and younger and to have a lower level of education. Homeless participants were diagnosed with a substance use disorder significantly more often (74.2%). Psychotic disorders were the highest among homeless participants (29.0%). Concerning service use, we did neither find a lower utilisation of ambulatory services nor a higher utilisation of hospital-based care among homeless participants. CONCLUSIONS: Our findings underline the need for effective housing for people with mental illness. Despite many sociotherapeutic facilities, a concerning number of people with mental illness is living in homelessness. Especially early interventions addressing substance use might prevent future homelessness

Training Deep Surrogate Models with Large Scale Online Learning

The spatiotemporal resolution of Partial Differential Equations (PDEs) plays important roles in the mathematical description of the world's physical phenomena. In general, scientists and engineers solve PDEs numerically by the use of computationally demanding solvers. Recently, deep learning algorithms have emerged as a viable alternative for obtaining fast solutions for PDEs. Models are usually trained on synthetic data generated by solvers, stored on disk and read back for training. This paper advocates that relying on a traditional static dataset to train these models does not allow the full benefit of the solver to be used as a data generator. It proposes an open source online training framework for deep surrogate models. The framework implements several levels of parallelism focused on simultaneously generating numerical simulations and training deep neural networks. This approach suppresses the I/O and storage bottleneck associated with disk-loaded datasets, and opens the way to training on significantly larger datasets. Experiments compare the offline and online training of four surrogate models, including state-of-the-art architectures. Results indicate that exposing deep surrogate models to more dataset diversity, up to hundreds of GB, can increase model generalization capabilities. Fully connected neural networks, Fourier Neural Operator (FNO), and Message Passing PDE Solver prediction accuracy is improved by 68%, 16% and 7%, respectively

Effect of Textile Characteristics on the AR-Glass Fabric Efficiency

Alkali-resistant (AR) glass textiles are used as the main reinforcement in several composite applications due to their good performance-to-cost ratio. A huge variety of textiles are already present in the market; they differ on various parameters, such as, for example, the filaments’ diameters, the geometry, the type of weaving, or the nature of the impregnation coating. To orient manufacturers towards the production of efficient textiles, the most important aspect is the balance between cost and performance. In this paper, a series of different fabrics designed for textile-reinforced cementitious composites were considered. Performance was assessed by means of uniaxial tensile tests and the results are presented in terms of load vs. displacement. Then, the selected AR-glass textiles were compared in terms of fabric efficiency, targeting the effect of each parameter on the textile capacity. The research here presented is part of a comprehensive campaign aimed at the optimization of glass-fabric-reinforced cementitious composites for structural retrofitting. To better discuss the different solutions tested, at the end, only considering a small number of the investigated textiles, an efficiency evaluation was carried out at the cementitious composite level

Improving our Understanding of Tropical Cyclone Unusual Motion and Rapid Intensification

Despite steady improvement in their tropical cyclone (TC) track and intensity forecasts over recent decades, operational numerical weather prediction (NWP) models still struggle at times in predicting two TC phenomena: climatologically unusual motion and rapid intensification (RI). Atlantic TCs typically move clockwise along curved tracks skirting the southern, western, and northwestern periphery of the Western Atlantic Ridge. Hurricane Joaquin (2015) followed a particularly unusual hairpin loop-shaped track that was poorly predicted by most operational NWP models, including the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Over recent years, considerable interest has also developed in understanding the cause-and-effect relationship between RI, defined here as a maximum surface wind (VMAX) intensification rate exceeding 15 m s-1 (24 h-1), and outbreaks of inner core deep convection, known as convective bursts (CBs), that have been observed to precede or coincide with RI in some TCs. A deeper physical understanding of the atmospheric processes governing TC unusual motion and RI, together with retrospective case study analyses of model forecast errors, will help us to identify NWP model components – data assimilation and physical parameterizations, for example – that may need further improvement. This research project seeks to (i) identify the atmospheric features that steered Hurricane Joaquin (2015) along the southwestward leg of its looping track and (ii) investigate the thermodynamic and three-dimensional characteristics of CBs as a first step toward developing a more comprehensive understanding of how CBs may facilitate RI. To accomplish (i), we generate a high-resolution Weather Research and Forecasting (WRF) model Control (CTL) simulation of Hurricane Joaquin (2015) that reproduces its looping track and intensification trends. Comparing CTL forecast fields against sensitivity WRF simulations initialized from perturbed analyses and against two representative GFS forecasts, we find that a sufficiently strong mid-to-upper level ridge northwest of Joaquin and a vortex sufficiently deep to interact with northeasterly geostrophic flows surrounding the ridge are both necessary for steering Joaquin southwestward. These results suggest that more accurate track forecasts for TCs developing in vertically sheared environments may be at least partly contingent on improved vortex initialization; for these cases, assimilation of more inner-core observations such as cloudy radiances and airborne radar-derived winds could be particularly beneficial. We address (ii) by comparing parcel traces, thermodynamic variables, and vertical accelerations along trajectories run through CB updraft cores with trajectories representative of the background eyewall ascent in a Hurricane Wilma (2005) WRF simulation. We compute three-dimensional trajectories from WRF-output winds using a model developed for this study that implements an experimental advection correction algorithm designed to reduce time interpolation errors, with the latter confirmed by tests on analytical and numerically-simulated flows. Results show that Wilma’s CBs are characterized by significant thermal buoyancy, particularly in the upper troposphere; this is consistent with their lower environmental air entrainment rates and reduced midlevel hydrometeor loading relative to the background ascent, and with their updrafts being rooted in portions of the boundary layer where ocean surface heat and moisture fluxes are locally higher

Hybridation MEMS/UWB pour la navigation pédestre intra-muros

Facing the expansion of geolocation needs, illustrated by the GALILEO European project, the growth of Location Based Services (LBS) and the need to identify the location of emergency mobile phone calls in Europe (standard E112), the research on localization techniques is booming. This thesis focuses on indoor pedestrian navigation and investigates a localization solution based on micro-electromechanical systems (MEMS) and ultra-wideband waves (UWB). MEMS based localization estimates the current location from a previously determined position using on-board low-cost inertial embedded sensors. Unfortunately, the performances of these autonomous systems are affected by large errors (typical of these sensors). In fact standalone solutions drift rapidly with time. Impulse-Radio UWB (IR-UWB) Times Of Arrival (TOA) are often used for localization purposes. This network based technology uses sensor networks, mainly attached to the infrastructure of the building to estimate the location of the transmitter with decimetre accuracy in ideal scenarii. However the indoor environment is hostile for radio propagation. Full of artificial obstacles, electromagnetic waves are disturbed and radiolocation performances are reduced. Construction materials also affect the magnetic field used to estimate the pedestrian's walking direction. In this context, the hybridization of these two complementary and uncorrelated technologies is promising. The study of the movement pattern of a pedestrian walking indoors induces two main outcomes on localization techniques. Firstly, random pedestrian movements complicate MEMS signal processing. Secondly, when the transmitter is worn by the user, for example around the neck, IR-UWB that propagates through the human body can hardly contribute to the localization. Optimal data fusion filters that hybridize a large set of observations : Angles Of Arrival (AOA), Time Differences Of Arrival (TDOA), accelerations, angular velocities and magnetic field measurements are presented. The coupling of UWB and MEMS data relies on an Extended Kalman Filter (EKF) complemented with specific procedures. Loose integration and tight integration are considered. Outlier detection processes within the radio data enrich the EKF. The most remarkable process is based on the RANSAC paradigm and employs the physical constraints of the pedestrian's walk described by biomechanics. In some cases, it enables the processing of reflected radio signals. A user equipped with a MEMS module and an UWB transceiver walked in the premises of the EPFL, following nine independent paths, for a total length of 380 m. The benefit of the MEMS/UWB hybridization filters are evaluated based on this experiment. The tight integration outperforms the loose coupling and enables indoor pedestrian localization with a one metre accuracy