222 research outputs found
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Light-output enhancement of InGaN light emitting diodes regrown on nanoporous distributed Bragg reflector substrates
Utilising our novel wafer-scale electrochemical porosification approach which proceeds through the top surface by means of nanoscale vertical etching pathways, we have prepared full 2 inch wafers containing alternating solid GaN and nanoporous GaN (NP-GaN) layers that form distributed Bragg reflectors (DBRs), and have regrown InGaN-based light emitting diode (LED) heterostructures on these wafers. The NP-GaN DBR wafer is epi-ready and exhibits a peak reflectance of 95% at 420 nm prior to growth of the LED heterostructure. We observe a 1.8Ă— enhancement in peak intensity of LED electroluminescence from processed devices, and delayed onset of efficiency droop with increased injection current.This work was supported partly by the UK Engineering and Physical Sciences Research Council Grant No. EP/M011682/1 and the EPSRC Impact Acceleration Account Follow on Fund of the University of Cambridge
Enzymatic Characterization of Leishmania major Phosphatidylethanolamine Methyltransferases LmjPEM1 and LmjPEM2
Phosphatidylcholine (PC) is the most abundant phospholipid in the membranes of the human parasite Leishmania. It is synthesized via two metabolic routes, the de novo pathway that starts with the uptake of choline, and the threefold methylation of phosphatidylethanolamine. Choline was shown to be dispensable for Leishmania; thus, the methylation pathway likely represents the primary route for PC production. Here, we have identified and characterized two phosphatidylethanolamine methyltransferases, LmjPEM1and LmjPEM2. Both enzymes are expressed in promastigotes as well as in the vertebrate form amastigotes, suggesting that these methyltransferases are important for the development of the parasite throughout its life cycle. Heterologous expression in yeast has demonstrated that LmjPEM1 and LmjPEM2 complement the choline auxotrophy phenotype of a yeast double null mutant lacking phosphatidylethanolamine methyltransferase activity. LmjPEM1 catalyzes the first, and to a lesser extent, the second methylation reaction. In contrast, LmjPEM2 has the capacity to add the second and third methyl group onto phosphatidylethanolamine to yield (lyso)PC; it can also add the first methyl group, albeit with very low efficiency
Ultra-low-threshold InGaN/GaN quantum dot micro-ring lasers.
In this work, we demonstrate ultra-low-threshold, optically pumped, room-temperature lasing in GaN microdisk and micro-ring cavities containing InGaN quantum dots and fragmented quantum wells, with the lowest measured threshold at a record low of 6.2  μJ/cm2. When pump volume decreases, we observe a systematic decrease in the lasing threshold of micro-rings. The photon loss rate, γ, increases with increasing inner ring diameter, leading to a systematic decrease in the post-threshold slope efficiency, while the quality factor of the lasing mode remains largely unchanged. A careful analysis using finite-difference time-domain simulations attributes the increased γ to the loss of photons from lower-quality higher-order modes during amplified spontaneous emission
Sound Absorption Property of Polyurethane Foam with Polyethylene Fiber
Flexible polyurethane (PU) foams with varying polyethylene fiber contents were synthesized to improve their acoustic performances. The purpose of this study was to investigate the effects of different polyethylene fiber contents of the PU foams on the resultant sound absorption, which was characterized by the impedance tube technique to obtain the incident sound absorption coefficient. Other parameters related to acoustic absorption performance of polyurethane foams were also measured such as microstructure, porosity and airflow resistivity. In this paper, these parameters were analyzed and compared with those of pure polyurethane foam. The results showed that the acoustic absorption properties of the PU foams were improved especially in the low frequency region by adding polyethylene fiber. When 0.2 g polyethylene fiber was added into the PU foam composite, the sound absorption coefficient is best especially around 125 – 315 Hz. The maximum enhancement in the acoustic properties of the PU foams was obtained by adding 0.1 g polyethylene fiber
Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases
Large Language Models (LLMs) have demonstrated remarkable performance in code
completion. However, due to the lack of domain-specific knowledge, they may not
be optimal in completing code that requires intensive domain knowledge for
example completing the library names. Although there are several works that
have confirmed the effectiveness of fine-tuning techniques to adapt language
models for code completion in specific domains. They are limited by the need
for constant fine-tuning of the model when the project is in constant
iteration.
To address this limitation, in this paper, we propose NM-LM, a
retrieval-augmented language model (R-LM), that integrates domain knowledge
into language models without fine-tuning. Different from previous techniques,
our approach is able to automatically adapt to different language models and
domains. Specifically, it utilizes the in-domain code to build the
retrieval-based database decoupled from LM, and then combines it with LM
through Bayesian inference to complete the code. The extensive experiments on
the completion of intra-project and intra-scenario have confirmed that NM-LM
brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A
deep analysis of our tool including the responding speed, storage usage,
specific type code completion, and API invocation completion has confirmed that
NM-LM provides satisfactory performance, which renders it highly appropriate
for domain adaptive code completion. Furthermore, our approach operates without
the requirement for direct access to the language model's parameters. As a
result, it can seamlessly integrate with black-box code completion models,
making it easy to integrate our approach as a plugin to further enhance the
performance of these models.Comment: Accepted by ASE202
A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture
Considering the unmanned aerial vehicle (UAV) three-dimensional (3D) posture,
a novel 3D non-stationary geometry-based stochastic model (GBSM) is proposed
for multiple-input multiple-output (MIMO) UAV-to-vehicle (U2V) channels. It
consists of a line-of-sight (LoS) and non-line-of-sight (NLoS) components. The
factor of fuselage posture is considered by introducing a time-variant 3D
posture matrix. Some important statistical properties, i.e. the temporal
autocorrelation function (ACF) and spatial cross correlation function (CCF),
are derived and investigated. Simulation results show that the fuselage posture
has significant impact on the U2V channel characteristic and aggravate the
non-stationarity. The agreements between analytical, simulated, and measured
results verify the correctness of proposed model and derivations. Moreover, it
is demonstrated that the proposed model is also compatible to the existing GBSM
without considering fuselage posture.Comment: 12 pages, 8 figures, CNCO
The Pyramiding of Three Key Root Traits Aid Breeding of Flood-Tolerant Rice
Flooding is constantly threatening the growth and yield of crops worldwide. When flooding kicks in, the soil becomes water-saturated and, therefore, the roots are the first organs to be exposed to excess water. Soon after flooding, the soil turns anoxic and the roots can no longer obtain molecular oxygen for respiration from the rhizosphere, rendering the roots dysfunctional. Rice, however, is a semi-aquatic plant and therefore relatively tolerant to flooding due to adaptive traits developed during evolution. In the present review, we have identified three key root traits, viz. cortical aerenchyma formation, a barrier to radial oxygen loss and adventitious root growth. The understanding of the physiological function, the molecular mechanisms, and the genetic regulation of these three traits has grown substantially and therefore forms the backbone of this review. Our synthesis of the recent literature shows each of the three key root traits contributes to flood tolerance in rice. One trait, however, is generally insufficient to enhance plant tolerance to flooding. Consequently, we suggest comprehensive use of all three adaptive traits in a pyramiding approach in order to improve tolerance to flooding in our major crops, in general, and in rice, in particular
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Effects of microstructure and growth conditions on quantum emitters in gallium nitride
Single-photon emitters in gallium nitride (GaN) are gaining interest as attractive quantum systems due to the well-established techniques for growth and nanofabrication of the host material, as well as its remarkable chemical stability and optoelectronic properties. We investigate the nature of such single- photon emitters in GaN with a systematic analysis of various samples produced under different growth conditions. We explore the effect that intrinsic structural defects (dislocations and stacking faults), doping and crystal orientation in GaN have on the formation of quantum emitters. We investigate the relationship between the position of the emitters—determined via spectroscopy and photoluminescence measurements—and the location of threading dislocations—characterised both via atomic force microscopy and cathodoluminescence. We find that quantum emitters do not correlate with stacking faults or dislocations; instead, they are more likely to originate from point defects or impurities whose density is modulated by the local extended defect density
Brain information processing capacity modeling
Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that-for a given cognitive task and subject-higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity-as estimated from fMRI data-predicted task and age-related differences in reaction times, speaking to the model's predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making
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