210 research outputs found
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 Ă 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
Edge Caching Based on Deep Reinforcement Learning and Transfer Learning
This paper addresses the escalating challenge of redundant data transmission
in networks. The surge in traffic has strained backhaul links and backbone
networks, prompting the exploration of caching solutions at the edge router.
Existing work primarily relies on Markov Decision Processes (MDP) for caching
issues, assuming fixed-time interval decisions; however, real-world scenarios
involve random request arrivals, and despite the critical role of various file
characteristics in determining an optimal caching policy, none of the related
existing work considers all these file characteristics in forming a caching
policy. In this paper, first, we formulate the caching problem using a
semi-Markov Decision Process (SMDP) to accommodate the continuous-time nature
of real-world scenarios allowing for caching decisions at random times upon
file requests. Then, we propose a double deep Q-learning-based caching approach
that comprehensively accounts for file features such as lifetime, size, and
importance. Simulation results demonstrate the superior performance of our
approach compared to a recent Deep Reinforcement Learning-based method.
Furthermore, we extend our work to include a Transfer Learning (TL) approach to
account for changes in file request rates in the SMDP framework. The proposed
TL approach exhibits fast convergence, even in scenarios with increased
differences in request rates between source and target domains, presenting a
promising solution to the dynamic challenges of caching in real-world
environments
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 3Â %, 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 61Â %. Compared with monitoring point P8, the pressure fluctuation coefficient of monitoring point P5 is reduced by 70Â %. 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 lowâcost 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 earthâabundant transition metals (i.e., nickel, iron, and copper), 3D hierarchical nanoarchitectures, consisting of ultrathin NiâFe layeredâdoubleâhydroxide (NiâFe LDH) nanosheets or porous NiâFe oxides (NiFeOx) assembled to a metallic NiCu alloy, are delicately constructed. In alkaline solution, the asâprepared NiâFe LDH@NiCu possesses outstanding OER activity, achieving a current density of 10 mA cmâ2 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 cmâ2 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)oxideâbased electrocatalysts. The remarkable activity of the NiâFe LDH@NiCu and NiFeOx@NiCu is further demonstrated by a 1.5 V solarâpanelâpowered electrolyzer, resulting in current densities of 10 and 50 mA cmâ2 at overpotentials of 293 and 506 mV, respectively. Such performance renders the asâprepared 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
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