446 research outputs found
Light-induced enhancement of the minority carrier lifetime in boron-doped Czochralski silicon passivated by doped silicon nitride
No full textThis study reports a doubling of the effective minority carrier lifetime under light soaking conditions, observed in a boron-doped p-type Czochralski grown silicon wafer passivated by a phosphorus-doped silicon nitride thin film. The analysis of capacitance-voltage curves revealed that the fixed charge in this phosphorus-doped silicon nitride film was negative, which was unlike the well-known positive fixed charges observed in traditional undoped silicon nitride. The analysis results revealed that the enhancement phenomenon of minority carrier lifetime was caused by the abrupt increase in the density of negative fixed charge (from 7.2 x 10(11) to 1.2 x 10(12) cm(-2)) after light soaking. (C) 2015 Elsevier B.V. All rights reserved
Doplor Sleep: Monitoring Hospital Soundscapes for Better Sleep Hygiene
No full textGood sleep is conducive to the recovery process of hospital patients - and yet, in many wards, sleep duration and quality can often be suboptimal, in part due to modifiable hospital-related sounds and noises. At the neurological ward of the Reinier de Graaf hospital in Delft, the Netherlands, we developed and evaluated a prototype information exchange system to raise awareness of specific sounds as disturbing patients' sleep. The system both classifies different relevant sound events and tracks sleep quality (using a Fitbit device). This information is then visualized for patients and staff to present the influence of the soundscape on patients' sleep hygiene in a friendly and comprehensive way. We discuss the design process, including a context study and various evaluations of the technology, interface, and created affordances. Our initial findings indicate that visualizing hospital soundscapes may, indeed, support both patients and staff in their efforts towards better sleep hygiene. Design AestheticsInternet of Thing
Improvement on the Si/PEDOT:PSS hybrid solar cells by rear-sided passivation with SiNx:H layers
No full textA patterned silicon nitride (SiNx:H) passivation layer was employed to improve the performance of silicon/poly(3,4-ethylenedioxythiophene):poly(stylenesulfonate) (Si/PEDOT:PSS) hybrid solar cells, achieving of an enhancement in the power conversion efficiency (PCE) of 0.6%. The insertion of patterned SiNx:H layer with a 80% SiNx:H-to-substrate ratio boosted the open circuit voltage (V-oc) from 523.1 mV to 573.4 mV, suggesting the well-passivation property of the patterned SiNx:H thin layer that was created by plasma enhanced chemical vapor deposition and lithography processes
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On Arbitrage in Single- and Multi-token Uniswap Markets
No full textUniswap and other constant product market makers have proven to be popular decentralized cryptocurrency exchanges, despite their simplicity. However, the exchange rate between pairs of currencies need not be consistent across the entire Uniswap market, and such price discrepancies open up the possibility of arbitrage. In this paper, we propose a theoretical examination into the possibility of efficient arbitrage in single- and multi-token Uniswap markets. We construct a polynomial-time cyclic arbitrage algorithm for single-token Uniswap markets and give insights into the state of the market after arbitrage. We then generalize to multi-token Uniswap markets by constructing a linear program that allows the arbitrage of asset bundles. After arbitraging the market, we then extend results by Goyal et al. (2023) to provide an optimal liquidity provision strategy for a Uniswap market. This provides a pipeline for an arbitrageur: they first extract profit from the market via arbitrage, and then they may reinvest their profits back into the market in the form of liquidity provision. Finally, we conclude with an empirical study into the profitability of arbitrage in historical Uniswap markets
A Machine with Short-Term, Episodic, and Semantic Memory Systems
No full textInspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, the Room , where an agent has to learn how to encode, store, and retrieve memories to maximize its return by answering questions. We show that our deep Q-learning based agent successfully learns whether a short-term memory should be forgotten, or rather be stored in the episodic or semantic memory systems. Our experiments indicate that an agent with human-like memory systems can outperform an agent without this memory structure in the environment
Deep characteristics analysis on travel time of emergency traffic
No full textOwing to the rapid development of emergency rescue transportation in cities and the frequent emergencies, demand for emergency rescue is increasing drastically. How to select an emergency rescue route quickly and shorten the rescue travel time under the condition of limited urban road resources is of great significance. Based on the characteristics analysis of emergency rescue, this paper classifies priority levels of different emergency traffic, moreover, the travel times are also analysed with three scenarios: 1) emergency rescue vehicles encountering no queues; 2) encountered queues but lanes available; 3) encountered queues with no available lanes. Related case study shows that model in this paper can effectively shorten travel time of emergency traffic in the route and improve its efficiency.Accepted Author ManuscriptTransport and Plannin
Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Neural Representation
No full textAcoustic-Resolution Photoacoustic Microscopy (ARPAM) has demonstrated great potential in subcutaneous vascular imaging. However, its spatial resolution is limited by the system’s Point Spread Function (PSF). To enhance resolution, various deconvolution-based methods can be employed. Traditional deconvolution methods, such as Richardson-Lucy deconvolution and model-based deconvolution, typically use the PSF as prior knowledge to improve spatial resolution. However, accurately measuring the system’s PSF is challenging, leading to the widespread adoption of blind deconvolution methods, which often suffer from inaccurate deconvolution. Another major challenge of AR-PAM is the long scanning time. To accelerate image acquisition, downsampling can be applied to reduce scanning time. Subsequently, interpolation methods are commonly used to recover high-resolution images from the downsampled measurements. However, conventional interpolation methods struggle to achieve high-fidelity image recovery, particularly under high downsampling conditions. In this study, we propose a method based on Implicit Neural Representations (INR) to simultaneously address the challenges of unknown PSF and under-sampled image recovery. By leveraging INR, we learn a continuous mapping from spatial positions to initial acoustic pressure, effectively compensating for the discretization of the image space and enhancing the resolution of AR-PAM. Specifically, we treat the PSF as a learnable parameter to mitigate inaccuracies in PSF measurement. We qualitatively and quantitatively evaluated the proposed method on leaf vein data, mouse brain data, and real in vivo AR-PAM data, demonstrating superior performance compared to existing methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).This is a manuscript of an article published as Xiao, Youshen, Zhengyuan Zhang, Ruixi Sun, Yiling Shi, Sheng Liao, Fan Zhang, Yunhui Jiang et al. "Resolution Enhancement of Under-Sampled Photoacoustic Microscopy Images Using Neural Representation." IEEE Transactions on Computational Imaging 11 (2025): 678-688. doi: https://doi.org/10.1109/TCI.2025.3565129
Data structure animation tutorial
No full textThis study is an animation tutorial for the people who wants to learn the Data Structure. The emphasis is placed on vivid animations to help the people to understand algorithms for data structure easily. Some of the implementations to be addressed are: stack (Array-Based Stack, Linked Stack), queue (Array-Based Queue), List (Circular Linked List, Double Linked List, Linear Linked List), sort (Quick Sort, Merge Sort, Bubble Sort, Shell Sort, Insertion Sort, Heap Sort, Radix Sort, Selection Sort), heap (Priority Queue, Heap Build, Heap Sort), recursive (Tower of Hanio), hashing (Open Hashing, Close Hashing) binary search (Loop, Recursive), tree (2-3 Tree, Huffman Tree, Binary Search Tree, Balance Tree). Conclusions are formulated in terms of further work to be accomplished in order to better help understanding the completed algorithm
Hyphae-on-a-chip : a microfluidic platform for the study of zoospore germination and protrusive forces in hyphal invasion.
No full textFungal and oomycete pathogens have a significant influence on species extinctions, food security, ecosystem disturbances, and even human health. The large diversity of pathogenic fungi and oomycetes, and their acquired resistance to most antifungal agents, make treatment extremely difficult. Therefore, the aim of this work was to develop a lab-on-a-chip platform for high-throughput screening on individual spores and hyphae of fungi and oomycetes. This will help investigating and understanding the mechanisms of their invasive growth, and the development of antifungal compounds to control them.
The ability of invasive growth of fungal and oomycete hyphae to penetrate through host tissue is essential for pathogenicity. Given the importance of protrusive force, which is considered as the force generated by hyphae to grow invasively, this thesis introduces two Lab-on-a-Chip (LOC) platforms with elastomeric micropillars as force sensors for the study of underlying mechanisms enabling force generation. In the first case, an existing mycelial LOC platform was improved to contain single free-bending micropillars in channel constrictions, which enabled the measurement of protrusive forces exerted by individual hyphal tips of fungi and oomycetes. The platform design, fabrication process and photoresist optimization required to adapt the microfluidic platform to different hyphae sizes and corresponding high aspect-ratio micropillars are reported. To demonstrate the applicability of the platform, oomycete Achlya bisexualis and fungus Neurospora crassa were cultured on the devices and the forces exerted by individual hyphae measured.
For the second case, this thesis described the development of a novel monolithic LOC platform enabling high-throughput screening of different lifecycle stages and parameters of fungi and oomycetes, including spore germination, growth of resulting hyphae and their protrusive force generation. The platform integrates single zoospore capture and culture function with micropillar force sensing, allowing for investigations on an individual organism level. This is achieved by introducing hydrodynamic trapping of single cells and pneumatic membrane valves for compartmentalization. Single zoospores of oomycete A. bisexualis were demonstrated to be successfully captured in the trap sites via constriction structures in the parallel measurement channels, and the trapping and germination efficacies of two types of constrictions were tested. Two types of pneumatic membrane valves, normally-open and normally-closed microvalves, were implemented and evaluated for quality of compartmentalization in this thesis. Normally-closed microvalves with individual control for each measurement channel showed more effective single spore capture and compartmentalization. Using these valves, germinated hyphae from trapped oomycete A. bisexualis zoospores were observed to extend along measurement channels of the LOC platform, impacting with the force sensing micropillars, allowing for their growth rate and protrusive forces to be evaluated.
In addition to the two LOC platforms, this thesis presents a number of other improvements on and contributions to device fabrication and experimentation, including high-resolution alignment marks for two-layer photoresist master; PDMS chip alignment and assembly for producing platforms with membrane valves, especially normally-closed microvalves; experimental setup for independent and precise control of channels with liquid or air, and in-depth characterization of flow on the platform during operation of normally-closed microvalves
Doplor Sleep: Friendly feedback towards a better hospital soundscape for sleep
No full textRecently in the Netherlands, researchers have found that sleep duration and quality were suboptimal in the hospital. Evidence proved that many modifiable hospitalrelated factors negatively associate with patients' sleep (JAMA Internal Medicine, 2018). The sound factor is the most significant sleep disturbance in the hospital. In this graduation project, collaborating with Reiner de Graaf hospital and Critical Alarms lab, an information exchange system was proposed to raise awareness of sound as sleep disturbance. The system captures the sound-producing events and visualizes them with visually attractive graphics. In this system, we use the smartphone as the sound captor. The recorded sounds are processed locally on the phone and converted into information such as sound level and the category it belongs to (alarm, speech, incidental sounds, or snore). Fitbit is implemented in the system to collect sleep information. To both patients and medical staff, The Doplor sleep system presents the influence of sound on sleep in a friendly and comprehensive way. During this project, a functioning prototype was developed. We have tested its functionality and user experience with the potential users
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