| 1. |
|
|
| 2. |
- Chen, Xi, et al.
(författare)
-
Photothermally tunable silicon microring-resonator-based optical add-drop filter
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- A themro-optic (TO) silicon photonic add-drop filterwith small switching power and fast response is experimentallydemonstrated. We propose that metal-insulator-metal (MIM)absorbers can be integrated into the silicon TO devices, acting asan efficient and localized heat source. The MIM absorber designintroduces less thermal capacity to the device, comparing to theelectrically driven heater used in conventional TO devices. As a keyelement in silicon photonics, microring resonators have applicationin wavelength-division-multiplexing (WDM) devices, owning to theirunique spectrum properties. In this work, a silicon microring add-dropfilter is equipped with a MIM absorber. Experimentally, the deviceshows a measured optical response time of 5.0 μs and pumping powerderivative of the wavelength shift of 60 pm/mW.
|
|
| 3. |
- Gerkin, RC, et al.
(författare)
-
The best COVID-19 predictor is recent smell loss: a cross-sectional study
- 2020
-
Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundCOVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.MethodsThis preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.ResultsBoth C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.ConclusionsAs smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.
|
|
| 4. |
- Klaric, Lucija, et al.
(författare)
-
Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19.
- 2021
-
Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory. ; , s. 1-28
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at the FAS locus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Zhang, Zhi, et al.
(författare)
-
Two-layered wireless sensor networks for warehouses and supermarkets
- 2009
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The rapid development of wireless sensor network and RFID technologies offers a wide range of novel applications and services. In this paper, we present a two-layered wireless network for warehouses and supermarkets to monitor goods storage and sale, and assist for quality management and market analysis. The hierarchical architecture uses IEEE 802.15.4a impulse ultra-wideband radio (IR-UWB) communication protocol between slave sensor nodes and master sensor nodes, and IEEE 802.11b/g between master sensor nodes and server. The performance of our proposal is evaluated based on the widely used OMNeT++ simulation environment. Simulation results are presented and discussed according to different sampling rates and traffic loads for specific scenarios requirements. © 2009 IEEE.
|
|
| 8. |
- Chen, Hui
(författare)
-
Light Scattering Effects in Transparent Wood Biocomposites
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Transparent wood (TW) shows interesting optical properties and offers a sustainable alternative to petroleum-based polymer glasses. The influence of the TW internal structure (e.g. fiber alignment, volume fraction of cellulose, lignin content, defects from preparation process) on the optical properties is poorly understood, which limits its use in various applications. It is also true for transparent cellulose biocomposites in general. In this thesis, eco-friendly TW biocomposites are investigated. The work focuses on experimental characterization, structure-optical property relationships and possibilities to quantify such relationships. TWs made of delignified wood substrates with longitudinal direction of the tree parallel to the specimen surface are prepared. Relationships between anisotropic scattering and fiber alignment are studied by scattering angle measurement. Anisotropic photons distributions are compared between two fiber directions and various sample thicknesses. Next, attenuation coefficients (related to the anisotropic diffusion coefficients and absorption coefficient) for TWs are obtained by combining the photon diffusion equation with total transmittance measurements. The results indicate strong influence from the air gaps between wood substrate phase and polymer in the lumen pores on the scattering. Beside the airgaps between wood substrate and polymer, refractive index mismatch between polymer and wood substrate strongly influences the scattering. Thus, immersion liquid method (based on the total transmittance measurement) combined with a light transmission model (based on Fresnel reflection theory) is applied to estimate the refractive index of the delignified wood substrate. This facilitates TW design (i.e. the proper polymer selection for various applications) and modelling of the optical properties of delignified wood based transparent materials. Finally, extinction coefficients, Rayleigh scattering and absorption coefficients of TW are extracted from photon budget measurements combined with a light diffusion model developed. With higher volume fraction of cellulose, all these parameters are increased, although polymer-cellulose refractive index mismatch is the dominating factor controlling transmittance. The strong forward scattering in TW is analysed, and Rayleigh scattering has a strong effect on haze. The influence of lignin content on the absorption coefficient is also discussed.
|
|
| 9. |
- Chen, Hao
(författare)
-
Reliable and Efficient Distributed Machine Learning
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the ever-increasing penetration and proliferation of various smart Internet of Things (IoT) applications, machine learning (ML) is envisioned to be a key technique for big-data-driven modelling and analysis. Since massive data generated from these IoT devices are commonly collected and stored in a distributed manner, ML at the networks, e.g., distributed machine learning (DML), has been a promising emerging paradigm, especially for large-scale model training. In this thesis, we explore the optimization and design of DML algorithms under different network conditions. Our main research with regards to DML can be sorted into the following four aspects/papers as detailed below.In the first part of the thesis, we explore fully-decentralized ML by utilizing alternating direction method of multipliers (ADMM). Specifically, to address the two main critical challenges in DML systems, i.e., communication bottleneck and stragglers (nodes/devices with slow responses), an error-control-coding-based stochastic incremental ADMM (csI-ADMM) is proposed. Given an appropriate mini-batch size, it is proved that the proposed csI-ADMM method has a $O( 1/\sqrt{k})$) convergence rate and $O(1/{\mu ^2})$ communication cost, where $k$ denotes the number of iterations and $\mu$ is the target accuracy. In addition, tradeoff between the convergence rate and the number of stragglers, as well as the relationship between mini-batch size and number of stragglers, are both theoretically and experimentally analyzed. In the second part of the thesis, we investigate the asynchronous approach for fully-decentralized federated learning (FL). Specifically, an asynchronous parallel incremental block-coordinate descent (API-BCD) algorithm is proposed, where multiple nodes/devices are active in an asynchronous fashion to accelerate the convergence speed. The solution convergence of API-BCD is theoretically proved and simulation results demonstrate its superior performance in terms of both running speed and communication costs compared with state-of-the-art algorithms.The third part of the thesis is devoted to the study of jointly optimizing communication efficiency and wireless resources for FL over wireless networks. Accordingly, an overall optimization problem is formulated, which is divided into two sub-problems, i.e., the client scheduling problem and the resource allocation problem for tractability. More specifically, to reduce the communication costs, a communication-efficient client scheduling policy is proposed by limiting communication exchanges and reusing stale local models. To optimize resource allocation at each communication round of FL training, an optimal solution based on linear search method is derived. The proposed communication-efficient FL (CEFL) algorithm is evaluated both analytically and by simulation. The final part of the thesis is a case study of implementing FL in low Earth orbit (LEO) based satellite communication networks. We investigate four possible architectures of combining ML in LEO-based computing networks. The learning performance of the proposed strategies is evaluated by simulation and results validate that FL-based computing networks can significantly reduce communication overheads and latency.
|
|
| 10. |
- Chen, S., et al.
(författare)
-
Advanced approaches and applications of energy footprints toward the promotion of global sustainability
- 2020
-
Ingår i: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 261
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Ever-increasing energy demands pose huge environmental challenges globally. The strategies and methods that are chosen to address the energy crisis will, in part, determine the possibility of fulfilling the 1.5-degree global warming target set by the Paris Agreement, and of achieving the United Nations Sustainable Developmental Goals, two vital and ambitious objectives for humans in the coming decades. While numerous inventory and modelling approaches have been developed to evaluate direct and indirect energy requirements at multiple scales from industries to cities and to the global economy, a discussion on their implications for environmental sustainability is long overdue. In this study, we provide an overview of the research paradigm and the important approaches that have been developed to address energy sustainability and review the papers included in this Special Issue, which are representative of some of the major advancements in energy, carbon, and other hybrid footprint approaches. This Special Issue aims to gather and harmonize state-of-the-art energy accounting frameworks, models, and metrics that benefit the promotion of global sustainability.
|
|
