Chapter SOA-Based Aeronautical Service Integration

Space scienc

SOA-Based Aeronautical Service Integration

Space scienc

Characteristics of Heavy Particulate Matter Pollution Events Over Hong Kong and Their Relationships With Vertical Wind Profiles Using High-Time-Resolution Doppler Lidar Measurements

This is the final version. Available from American Geophysical Union (AGU) via the DOI in this record. Previous studies have reported boundary layer features related to air pollution. However, few studies have comprehensively evaluated the characteristics and mechanisms of vertical wind in the formation and evolution of heavy particulate matter pollution episodes (EP) in Hong Kong. In this study, we analyzed the vertical characteristics of heavy particulate matter (PM) pollutions over Hong Kong and their relationships with vertical wind profiles using high-time-resolution Doppler lidar measurements and hourly meteorological and air quality observations. We identified nine EPs and show that the events were closely coupled to various vertical wind profiles in the planetary boundary layer (PBL). Our analysis suggests that strong vertical wind speed with wind shear at certain heights in the PBL had a positive correlation with surface PM during most superregional transboundary EPs. The maximum transport height extends from the surface to about 2.0 km or even higher; these transport heights differed among superregional and regional transboundary EPs. At peak surface pollution concentrations during the nine EPs, the surface PM10 had a significant negative correlation with PBL heights/mixing layer heights, while the averaged wind shear in the PBL was significantly positively correlated. These EPs with different mixing layer heights were mainly driven by different vertical wind shear conditions under various weather systems related to surface high pressure, cold fronts, dust storms, and typhoons. This work provides scientific evidence that surface PM pollutions were closely related to the characteristics of vertical profiles during the transboundary air pollutions.Chinese University of Hong Kong ‐ University of Exeter Joint Centre for Environmental Sustainability and Resilience (ENSURE)The Chinese University of Hong Kon

Technology demonstrator of a novel software defined radio-based aeronautical communications system

YesThis paper presents the architectural design, software implementation, the validation and flight trial results of an aeronautical communications system developed within the Seamless Aeronautical Networking through integration of Data links Radios and Antennas (SANDRA) project funded by the European 7th Framework Aeronautics and Transport Programme. Based on Software Defined Radio (SDR) techniques, an Integrated Modular Radio (IMR) platform was developed to accommodate several radio technologies. This can drastically reduce the size, weight and cost in avionics with respect to current radio systems implemented as standalone equipment. In addition, the modular approach ensures the possibility to dynamically reconfigure each radio element to operate on a specific type of radio link. A radio resource management (RRM) framework is developed in the IMR consisting of a communication manager for the resource allocation and management of the different radio links and a radio adaptation manager to ensure protocol convergence through IP. The IMR has been validated though flight trials held at Oberpfaffenhofen, Germany in June 2013. The results presented in the paper validate the flexibility and scalability of the IMR platform and demonstrate seamless service coverage across different airspace domains through interworking between the IMR and other components of the SANDRA network.European Commissio

On the use of CO2 laser induced surface patterns to modify the wettability of Poly(methyl methacrylate) (PMMA)

CO2 lasers can be seen to lend themselves to materials processing applications and have been used extensively in both research and industry. This work investigated the surface modification of PMMA with a CO2 laser in order to vary the wettability characteristics. The wettability characteristics of the PMMA were modified by generating a number of patterns of various topography on the surface using the CO2 laser. These induced patterns were trench and hatch with scan dimensions of 50 and 100 μm. Through white light interferometry it was found that for all laser patterned samples the surface roughness had significantly increased by up to 3.1 μm. The chemical composition of selected samples were explored using X-ray photoelectron spectroscopy and found that the surface oxygen content had risen by approximately 4% At. By using a sessile drop device it was found that, in comparison to the as-received sample, 50 μm dimensions gave rise to a more hydrophilic surface; whereas 100 μm dimensions gave rise to either no change in contact angle or an increase making the PMMA hydrophobic. This can be explained by combinations of surface roughness and γp contributing to the observed contact angle, in addition to the possibility of different wetting regimes taking place owed to the variation of topographies over the as-received and laser patterned samples

Explicit and Implicit Semantic Ranking Framework

The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates. Existing industry solutions, especially latency-constrained services, often rely on similarity algorithms that sacrifice quality for speed. In this paper we introduce a generic semantic learning-to-rank framework, Self-training Semantic Cross-attention Ranking (sRank). This transformer-based framework uses linear pairwise loss with mutable training batch sizes and achieves quality gains and high efficiency, and has been applied effectively to show gains on two industry tasks at Microsoft over real-world large-scale data sets: Smart Reply (SR) and Ambient Clinical Intelligence (ACI). In Smart Reply, $sRank$ assists live customers with technical support by selecting the best reply from predefined solutions based on consumer and support agent messages. It achieves 11.7% gain in offline top-one accuracy on the SR task over the previous system, and has enabled 38.7% time reduction in composing messages in telemetry recorded since its general release in January 2021. In the ACI task, sRank selects relevant historical physician templates that serve as guidance for a text summarization model to generate higher quality medical notes. It achieves 35.5% top-one accuracy gain, along with 46% relative ROUGE-L gain in generated medical notes

A targeted gene panel that covers coding, non-coding and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

Genetic testing for neurodegenerative diseases (NDs) is highly challenging because of genetic heterogeneity and overlapping manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington\u27s disease and spinocerebellar ataxias type 3 (SCA3). Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known or likely pathogenic variation was found in 54 patients; 27 patients demonstrated clinical profiles that matched the variants; and 16 patients whose original diagnosis were refined. A high concordance of variant calling were observed when comparing the results from TGP and whole-exome sequencing of four patients. Our in-house STR detection algorithm has reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combined comprehensive TGPs-bioinformatics pipeline can improve the clinical diagnosis of NDs

A switchable pH-differential unitized regenerative fuel cell with high performance

Regenerative fuel cells are a potential candidate for future energy storage, but their applications are limited by the high cost and poor round-trip efficiency. Here we present a switchable pH-differential unitized regenerative fuel cell capable of addressing both the obstacles. Relying on a membraneless laminar flow-based design, pH environments in the cell are optimized independently for different electrode reactions and are switchable together with the cell process to ensure always favorable thermodynamics for each electrode reaction. Benefiting from the thermodynamic advantages of the switchable pH-differential arrangement, the cell allows water electrolysis at a voltage of 0.57 V, and a fuel cell open circuit voltage of 1.89 V, rendering round-trip efficiencies up to 74%. Under room conditions, operating the cell in fuel cell mode yields a power density of 1.3 W cm¯², which is the highest performance to date for laminar flow-based cells and is comparable to state-of-the-art polymer electrolyte membrane fuel cells