515 research outputs found
Numerical investigation of airborne contaminant transport under different vortex structures in the aircraft cabin.
Airborne contaminants such as pathogens, odors and CO2 released from an individual passenger could spread via air flow in an aircraft cabin and make other passengers unhealthy and uncomfortable. In this study, we introduced the airflow vortex structure to analyze how airflow patterns affected contaminant transport in an aircraft cabin. Experimental data regarding airflow patterns were used to validate a computational fluid dynamics (CFD) model. Using the validated CFD model, we investigated the effects of the airflow vortex structure on contaminant transmission based on quantitative analysis. It was found that the contaminant source located in a vorticity-dominated region was more likely to be "locked" in the vortex, resulting in higher 62% higher average concentration and 14% longer residual time than that when the source was on a deformation dominated location. The contaminant concentrations also differed between the front and rear parts of the cabin because of different airflow structures. Contaminant released close to the heated manikin face was likely to be transported backward according to its distribution mean position. Based on these results, the air flow patterns inside aircraft cabins can potentially be improved to better control the spread of airborne contaminant
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Pulmonary diseases induced by ambient ultrafine and engineered nanoparticles in twenty-first century.
Air pollution is a severe threat to public health globally, affecting everyone in developed and developing countries alike. Among different air pollutants, particulate matter (PM), particularly combustion-produced fine PM (PM2.5) has been shown to play a major role in inducing various adverse health effects. Strong associations have been demonstrated by epidemiological and toxicological studies between increases in PM2.5 concentrations and premature mortality, cardiopulmonary diseases, asthma and allergic sensitization, and lung cancer. The mechanisms of PM-induced toxicological effects are related to their size, chemical composition, lung clearance and retention, cellular oxidative stress responses and pro-inflammatory effects locally and systemically. Particles in the ultrafine range (<100 nm), although they have the highest number counts, surface area and organic chemical content, are often overlooked due to insufficient monitoring and risk assessment. Yet, ample studies have demonstrated that ambient ultrafine particles have higher toxic potential compared with PM2.5. In addition, the rapid development of nanotechnology, bringing ever-increasing production of nanomaterials, has raised concerns about the potential human exposure and health impacts. All these add to the complexity of PM-induced health effects that largely remains to be determined, and mechanistic understanding on the toxicological effects of ambient ultrafine particles and nanomaterials will be the focus of studies in the near future
Algorithms for VLSI Circuit Optimization and GPU-Based Parallelization
This research addresses some critical challenges in various problems of VLSI design
automation, including sophisticated solution search on DAG topology, simultaneous
multi-stage design optimization, optimization on multi-scenario and multi-core
designs, and GPU-based parallel computing for runtime acceleration.
Discrete optimization for VLSI design automation problems is often quite complex,
due to the inconsistency and interference between solutions on reconvergent
paths in directed acyclic graph (DAG). This research proposes a systematic solution
search guided by a global view of the solution space. The key idea of the proposal
is joint relaxation and restriction (JRR), which is similar in spirit to mathematical
relaxation techniques, such as Lagrangian relaxation. Here, the relaxation and
restriction together provides a global view, and iteratively improves the solution.
Traditionally, circuit optimization is carried out in a sequence of separate optimization
stages. The problem with sequential optimization is that the best solution
in one stage may be worse for another. To overcome this difficulty, we take the approach
of performing multiple optimization techniques simultaneously. By searching
in the combined solution space of multiple optimization techniques, a broader view
of the problem leads to the overall better optimization result. This research takes
this approach on two problems, namely, simultaneous technology mapping and cell placement, and simultaneous gate sizing and threshold voltage assignment.
Modern processors have multiple working modes, which trade off between power
consumption and performance, or to maintain certain performance level in a powerefficient
way. As a result, the design of a circuit needs to accommodate different
scenarios, such as different supply voltage settings. This research deals with this
multi-scenario optimization problem with Lagrangian relaxation technique. Multiple
scenarios are taken care of simultaneously through the balance by Lagrangian multipliers.
Similarly, multiple objective and constraints are simultaneously dealt with by
Lagrangian relaxation. This research proposed a new method to calculate the subgradients
of the Lagrangian function, and solve the Lagrangian dual problem more
effectively.
Multi-core architecture also poses new problems and challenges to design automation.
For example, multiple cores on the same chip may have identical design
in some part, while differ from each other in the rest. In the case of buffer insertion,
the identical part have to be carefully optimized for all the cores with different
environmental parameters. This problem has much higher complexity compared to
buffer insertion on single cores. This research proposes an algorithm that optimizes
the buffering solution for multiple cores simultaneously, based on critical component
analysis.
Under the intensifying time-to-market pressure, circuit optimization not only
needs to find high quality solutions, but also has to come up with the result fast.
Recent advance in general purpose graphics processing unit (GPGPU) technology
provides massive parallel computing power. This research turns the complex computation
task of circuit optimization into many subtasks processed by parallel threads.
The proposed task partitioning and scheduling methods take advantage of the GPU
computing power, achieve significant speedup without sacrifice on the solution quality
HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition
To tackle Named Entity Recognition (NER) tasks, supervised methods need to
obtain sufficient cleanly annotated data, which is labor and time consuming. On
the contrary, distantly supervised methods acquire automatically annotated data
using dictionaries to alleviate this requirement. Unfortunately, dictionaries
hinder the effectiveness of distantly supervised methods for NER due to its
limited coverage, especially in specific domains. In this paper, we aim at the
limitations of the dictionary usage and mention boundary detection. We
generalize the distant supervision by extending the dictionary with headword
based non-exact matching. We apply a function to better weight the matched
entity mentions. We propose a span-level model, which classifies all the
possible spans then infers the selected spans with a proposed dynamic
programming algorithm. Experiments on all three benchmark datasets demonstrate
that our method outperforms previous state-of-the-art distantly supervised
methods.Comment: 9 pages, 2 figure
Spectroscopic study of light scattering in linear alkylbenzene for liquid scintillator neutrino detectors
We has set up a light scattering spectrometer to study the depolarization of
light scattering in linear alkylbenzene. From the scattering spectra it can be
unambiguously shown that the depolarized part of light scattering belongs to
Rayleigh scattering. The additional depolarized Rayleigh scattering can make
the effective transparency of linear alkylbenzene much better than it was
expected. Therefore sufficient scintillation photons can transmit through the
large liquid scintillator detector of JUNO. Our study is crucial to achieving
the unprecedented energy resolution 3\%/ for JUNO
experiment to determine the neutrino mass hierarchy. The spectroscopic method
can also be used to judge the attribution of the depolarization of other
organic solvents used in neutrino experiments.Comment: 6 pages, 5 figure
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