Living and alive : homeless shelter design

Homelessness is a major social problem in the United States. The number of people who are living without a decent shelter is rising daily throughout the country. The problem of homelessness affects various groups in society, including the elderly, children, youths, and middle-aged adults. Recent studies have shown that the percentage of teens and youths who are experiencing homelessness has reached an alarming rate. The homeless adolescent group is defined as teens aged between 13 and 19 years. This rise in homelessness in this population group demands immediate attention, particularly older teens from 15 to 18 years of age. This study seeks to create a new model of shelter through a combination of spatial arrangements and special programs. For those teens who are born with low-income family conditions or housing pressure who have no choice but to be homeless, it is our society\u27s responsibility to give them a better choice and brighter future. There are many ways to help homeless teenagers and change their lives now. The model will be based on the analysis of the specific housing needs of young people facing homelessness. Existing shelter models most often concentrate on the housing problem of the broadest group of the homeless population. This solution, however, will use a space layout that suits the way of life of the teens, by meeting, all the daily needs of teenagers in a limited space, such as housing, diet, activities, health consultation. The most important of these is mental health in this user group. Through research on teenagers, it is found that in addition to the conditions needed for shelter, what they need most is related to psychological counseling. Housing is only the first step. In the existing shelter, it is more about just helping homeless people solve their housing problems. This project is to create a new model of shelter through a combination of reasonable spatial arrangements and special programs. The special arrangement provides a very highly utilized space with the limitation of the square footage, helps them with their mental and physical health, and also respects the relationship between personal space and public. The program of the exhibition will take place in the public area on the first floor. In this way, the teen will regain the respect and understanding of society by showing the public their valuable and creative works

A Modelling Study of the Impact of Photolysis on Indoor Air Quality

Most human exposure to air pollutants happen indoors, where people spend most of their time (~90%). In the ambient atmosphere, photolysis plays a major role in initiating chemical reactions. However, indoor photolysis is less well studied. Consequently, the role that photolysis plays in indoor chemical processing, particularly in the formation of harmful species, is unclear. The major aim of this thesis was, therefore, to improve the representation of indoor lighting and attenuated sunlight in the Indoor Detailed Chemical Model (INDCM). The improved model was then used to investigate the impacts of glass type, indoor artificial light, cloudiness, time of year and latitude on indoor photolysis rates and hence indoor air chemistry. The results show that variations in glass composition produce the highest deviations (~71%) in predicted concentrations of key indoor species (ozone, nitrous acid, nitric oxide, hydroxyl radicals, hydroperoxy radicals, organic peroxy radicals, peroxyacetyl nitrates and organic nitrates), followed by cloud level (~53%) and proximity to artificial light source (~53%), when compared to baseline conditions. These impacts were greatest for predicted hydroxyl radical concentrations, which deviated by an average of ~142% from the baseline scenario depending on the conditions studied. Enhanced radical concentrations were found during two cleaning case studies (automated and traditional techniques), with predicted hydroxyl radical concentrations up to 1.3 × 107 and 1.5106 molecule/cm3 respectively. Furthermore, radical concentrations were found to be highest under stronger lighting conditions, persisting for several hours after the cleaning events. This study provides a valuable contribution to the understanding of the impacts of photolysis on indoor air chemistry. Indoor artificial lights, such as LED, together with low cut-off wavelength glasses, will likely reduce the effects of photolysis indoors, but more research is needed on the health effects of different indoor air mixtures to confirm this recommendation

Is artificial data useful for biomedical Natural Language Processing algorithms?

A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.Comment: BioNLP 201

Research on unsteady performance of a two-stage self-priming centrifugal pump

In order to study the unsteady performance of a two-stage self-priming centrifugal pump, the unsteady numerical calculation in a two-stage self-priming centrifugal pump was performed and energy characteristics experiments and self-priming experiments were carried out. The pressure pulsation and radial force in the pump were then analyzed. The results show that numerical calculation values are close to the experiment values. Head deviation of the pump is less than 3 %, and efficiency deviation of the pump is less than 2 percentage points. Compared with monitoring point P1, the pressure fluctuation coefficient of monitoring point P3 at the design flow rate is reduced by 61 %. Compared with monitoring point P8, the pressure fluctuation coefficient of monitoring point P5 is reduced by 70 %. The radial force on the radial guide-vane is obviously smaller than that on the volute. Under the same flow rate, radial force on the volute of second-stage pump is almost 20 times larger than that on the radial guide-van of first-stage pump

Helix-MO: Sample-Efficient Molecular Optimization on Scene-Sensitive Latent Space

Efficient exploration of the chemical space to search the candidate drugs that satisfy various constraints is a fundamental task of drug discovery. Although many excellent deep molecular generative methods have been proposed to produce promising molecules, applying these methods in practice is still challenging since a great number of assessed molecules (samples) are required to provide the optimization direction, which is a considerable expense for drug discovery. To this end, we design a sample-efficient molecular generative method, namely Helix-MO, which can fast adapt to particular optimization scenes with only a small number of assessed samples. Helix-MO explores the chemical space in a scene-sensitive latent space, dynamically fine-tuned by multiple kinds of learning tasks from multiple perspectives. The learning tasks encourage the model to focus on modeling the more promising molecules during the optimization process to promote sample efficiency. Extensive experiments demonstrate that Helix-MO can achieve competitive performance with only a few assessed samples on four molecular optimization scenes. Ablation studies verify the impact of the learning tasks in the scene-specific latent space, efficiently identifying the critical characters of the satisfactory molecules. We also deployed Helix-MO on the website PaddleHelix (https://paddlehelix.baidu.com/app/drug/drugdesign/forecast) to provide drug design service and apply it to produce inhibitors of a kinase to demonstrate its practicability

Exceptional Performance of Hierarchical Ni-Fe (hydr)oxide@NiCu Electrocatalysts for Water Splitting

Developing low‐cost bifunctional electrocatalysts with superior activity for both the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is of great importance for the widespread application of the water splitting technique. In this work, using earth‐abundant transition metals (i.e., nickel, iron, and copper), 3D hierarchical nanoarchitectures, consisting of ultrathin Ni–Fe layered‐double‐hydroxide (Ni–Fe LDH) nanosheets or porous Ni–Fe oxides (NiFeOx) assembled to a metallic NiCu alloy, are delicately constructed. In alkaline solution, the as‐prepared Ni–Fe LDH@NiCu possesses outstanding OER activity, achieving a current density of 10 mA cm−2 at an overpotential of 218 mV, which is smaller than that of RuO2 catalyst (249 mV). In contrast, the resulting NiFeOx@NiCu exhibits better HER activity, yielding a current density of 10 mA cm−2 at an overpotential of 66 mV, which is slightly higher than that of Pt catalyst (53 mV) but superior to all other transition metal (hydr)oxide‐based electrocatalysts. The remarkable activity of the Ni–Fe LDH@NiCu and NiFeOx@NiCu is further demonstrated by a 1.5 V solar‐panel‐powered electrolyzer, resulting in current densities of 10 and 50 mA cm−2 at overpotentials of 293 and 506 mV, respectively. Such performance renders the as‐prepared materials as the best bifunctional electrocatalysts so far

ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs

The integration of Computer-Assisted Diagnosis (CAD) with Large Language Models (LLMs) holds great potential in clinical applications, specifically in the roles of digital family doctors and clinic assistants. However, current works in this field are plagued by limitations, specifically a restricted scope of applicable image domains and the provision of unreliable medical advice This restricts their overall processing capabilities. Furthermore, the mismatch in writing style between LLMs and radiologists undermines their practical usefulness. To tackle these challenges, we introduce ChatCAD+, which is designed to be universal and reliable. It is capable of handling medical images from diverse domains and leveraging up-to-date information from reputable medical websites to provide reliable medical advice. Additionally, it incorporates a template retrieval system that improves report generation performance via exemplar reports, enabling seamless integration into existing clinical workflows. The source code is available at https://github.com/zhaozh10/ChatCAD.Comment: Authors Zihao Zhao, Sheng Wang, Jinchen Gu, Yitao Zhu contributed equally to this work and should be considered co-first author

Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors

Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor processing, in-sensor processing, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers, however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, we for the first time, propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2x lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.Comment: 17 pages, 11 figures, 2 table