Microscaled and nanoscaled platinum sensors

We show small and robust platinum resistive heaters and thermometers that are defined by microlithography on silicon substrates. These devices can be used for a wide range of applications, including thermal sensor arrays, programmable thermal sources, and even incandescent light emitters. To explore the miniaturization of such devices, we have developed microscaled and nanoscaled platinum resistor arrays with wire widths as small as 75 nm, fabricated lithographically to provide highly localized heating and accurate resistance (and hence temperature) measurements. We present some of these potential applications of microfabricated platinum resistors in sensing and spectroscopy

Effects of Fixed and Motorized Window Louvers on the Daylighting and Thermal Performance of Open-Plan Office Buildings

This study investigates the daylighting and thermal performance of open-plan office buildings with two scenarios of daylight louvers – fixed and motorized ones. Both types are for facade window applications. They redirect transmitted daylight to eliminate glare on occupants and increase daylight levels deeper in the interior space, but have significantly different daylight transmitting characteristics. In addition to daylighting, these louvers also affect solar heat gain. The tilt angle of slats in motorized louvers can be adjusted to control solar heat gain and daylight. In this study, an existing energy-efficient office building with fixed louvers is used. A combined thermal and daylighting model for a typical section of the building is developed using a simplified approach, and validated with measured data. The option of motorized louvers is then added to this model. The daylighting and thermal performance for different designs and seasons are assessed using the model. Results show that motorized louvers can effectively enhance useful solar heat gain and/or daylighting. The effect of building depth is also investigated

Differentially Private Generative Adversarial Networks with Model Inversion

To protect sensitive data in training a Generative Adversarial Network (GAN), the standard approach is to use differentially private (DP) stochastic gradient descent method in which controlled noise is added to the gradients. The quality of the output synthetic samples can be adversely affected and the training of the network may not even converge in the presence of these noises. We propose Differentially Private Model Inversion (DPMI) method where the private data is first mapped to the latent space via a public generator, followed by a lower-dimensional DP-GAN with better convergent properties. Experimental results on standard datasets CIFAR10 and SVHN as well as on a facial landmark dataset for Autism screening show that our approach outperforms the standard DP-GAN method based on Inception Score, Fr\'echet Inception Distance, and classification accuracy under the same privacy guarantee.Comment: Best Student Paper Award of 13th IEEE International Workshop on Information Forensics and Security (WIFS 2021), Montpellier, Franc

Polyphasic taxonomy of Aspergillus section Cervini

Species belonging to Aspergillus section Cervini are characterised by radiate or short columnar, fawn coloured, uniseriate conidial heads. The morphology of the taxa in this section is very similar and isolates assigned to these species are frequently misidentified. In this study, a polyphasic approach was applied using morphological characters, extrolite data, temperature profiles and partial BenA, CaM and RPB2 sequences to examine the relationships within this section. Based on this taxonomic approach the section Cervini is resolved in ten species including six new species: A. acidohumus, A. christenseniae, A. novoguineensis, A. subnutans, A. transcarpathicus and A. wisconsinensis. A dichotomous key for the identification is provided

Performance analysis and discussion on the thermoelectric element footprint for PV–TE maximum power generation

Geometrical optimisation is a valuable way to improve the efficiency of a thermoelectric element (TE). In a hybrid photovoltaic-thermoelectric (PV-TE) system, the photovoltaic (PV) and thermoelectric (TE) components have a relatively complex relationship; their individual effects mean that geometrical optimisation of the TE element alone may not be sufficient to optimize the entire PV–TE hybrid system. In this paper, we introduce a parametric optimisation of the geometry of the thermoelectric element footprint for a PV–TE system. A uni-couple TE model was built for the PV–TE using the finite element method and temperature-dependent thermoelectric material properties. Two types of PV cells were investigated in this paper and the performance of PV–TE with different lengths of TE elements and different footprint areas was analysed. The outcome showed that no matter the TE element's length and the footprint areas, the maximum power output occurs when An/Ap= 1. This finding is useful, as it provides a reference whenever PV–TE optimisation is investigated

Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning

Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of 1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process; 2) a forward pass and backward pass parallelisation and optimisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed; and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach

De novo design of bioactive protein switches.

Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix 'cage' with a single interface that can interact either intramolecularly with a terminal 'latch' helix or intermolecularly with a peptide 'key'. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage-key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering

Quasi‐periodic ionospheric disturbances with a 40‐min period during prolonged northward interplanetary magnetic field

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95506/1/grl12118.pd