Miracle-dz/UniFAR-FTUs huggingface dataset:
The revival of thermal utilization from the Sun: interfacial solar vapor generation

Abstract Since solar energy is the ultimate energy resource and a significant amount of global energy utilization goes through heat, there have been persistent efforts for centuries to develop devices and systems for solar–thermal conversion. Most recently, interfacial solar vapor generation, as an emerging concept of solar–thermal conversion, has gained significant attention for its great potentials in various fields such as desalination, sterilization, catalysis, etc. With the advances of rationally designed materials and structures and photon and thermal management at the nanoscale, interfacial solar vapor generation has demonstrated both thermodynamic and kinetical advantages over conventional strategies. In this review, we aim to illustrate the definition, mechanism and figures of merit of interfacial solar vapor generation, and to summarize the development progress of relevant materials and applications, as well as to provide a prospective view of the future.
Lexical Diachronic Semantic Maps. Mapping the evolution of time-related lexemes

This paper extends the scope of application of the semantic map model to diachronic lexical semantics. Combining a quantitative approach to large-scale synchronic polysemy data with a qualitative evaluation of the diachronic material in two text languages, ancient Egyptian and ancient Greek, it shows that weighted diachronic semantic maps can capture informative generalizations about the organization of the lexicon and its reshaping over time. The general methodology developed in the paper is illustrated with a case study of the semantic extension of time-related lexemes. This case study shows that the blend of tools well established in linguistic typology with proven methods of historical linguistics enables a principled approach to long-standing questions in the fields of diachronic semasiology and onomasiology.
Nouveaux composites graphite/sel destinés au stockage de l'énergie thermique à haute température : De l'élaboration au développement de méthodes de caractérisation thermique de matériaux conducteurs orthotropes.

Cette these a ete effectuee dans le cadre des projets DISTOR (Europeen) et HTPSTOCK (Francais) qui visent a concevoir et etudier de nouveaux composites graphite/sel destines au stockage de l'energie thermique a haute temperature (>200°C). Elle est scindee en deux parties distinctes. La premiere partie detaille les travaux relatifs a l'elaboration et la caracterisation thermique de ces nouveaux composites. On y presente les differentes voies d'elaboration des composites (dispersion, compression uniaxiale, isostatique) associees aux differents types de graphite (graphite naturel expanse (GNE), graphite synthetique) investigues au cours de cette these. On y retrouve egalement les resultats lies a la campagne de caracterisation thermique de ces composites permettant de mettre en exergue l'impact du graphite sur les proprietes conductrices des materiaux etudies. A partir de ces resultats, des etudes de modelisation de l'evolution de la conductivite thermique ont pu etre entrepris afin d'approfondir la comprehension de l'effet du graphite (quantite, taille de particules) sur la conductivite effective des composites. La deuxieme partie presente essentiellement les dispositifs de caracterisation thermique et les modeles thermocinetiques associes qui ont du etre developpes et adaptes aux specificites des materiaux nouvellement elabores. Cela concerne principalement les materiaux elabores par compression, materiaux difficilement reproductible et possedant des proprietes orthotropes. La caracterisation de ce type de materiaux s'avere tres delicate et pour le moins fastidieuse. C'est pourquoi nous nous sommes attaches a developper et adapter les moyens de caracterisation existants afin de permettre la caracterisation thermique complete d'un materiau conducteur orthotrope a partir d'une seule mesure sur un seul echantillon
Detection of the Amphibian Pathogen Batrachochytrium Dendrobatidis on Spadefoots in the Kamloops Region

Thesis Supervisor: Dr. Jonathan Van Hamme Batrachochytrium dendrobatidis is a species of chytrid fungus responsible for the decline and extinction of amphibian populations worldwide (Longcore 1999 and others). Recently, B. dendrobatidis has been detected on western toads (Bufo boreas) in southwestern British Columbia (Deguise & Richardson 2009) and has therefore become a concern for conservationists across the province. Chytrid DNA can be extracted from tissue samples, skin swabs, and water bodies and subsequently detected using real time PCR techniques. It is currently unknown whether the chytrid fungus is affecting amphibian populations in the Kamloops area. This project aimed to first validate an effective real time PCR assay for detecting the presence of the amphibian pathogen B. dendrobatidis, and second to employ this real time PCR assay to detect B. dendrobatidis on experimental samples collected in the Kamloops region. Specifically, spadefoots were captured during the summer of 2013 from the New Afton mining site located south of Kamloops, BC. A total of 78 spadefoots were captured, swabbed, and subsequently weighed, measured and released. Parameters for optimizing an effective real time PCR assay were tested to detect B. dendrobatidis in experimental samples; initially a Taqman assay was tried, but due to a lack of success, a SYBR Green assay was used. DNA was extracted from all samples and 30 of these samples were tested for B. dendrobatidis using the SYBR Green assay and gel electrophoresis. B. dendrobatidis DNA was not detected in any of the 30 experimental samples. Melt curve analysis indicated the presence of multiple PCR products, products which were observed by gel electrophoresis at lengths less than 150 base pairs. It is possible that nonspecific primer binding occurred, or that primer dimers formed, at low template DNA concentrations as these bands were not observed above 42 standard DNA copy numbers. Based on the results in hand, it is possible that (1) chytrid is not present in the ponds at New Afton mine, (2) chytrid is present but is not affecting the spadefoots, (3) the sensitivity of the SYBR Green assay was not sufficient to detect chytrid DNA, (4) PCR inhibitors interfered with the detection of chytrid DNA on samples, and (5) chytrid DNA did not adhere to the swabs that were used in this work. Future research should employ swabs with smaller tips
Collective Memory for Detecting Nonconcurrent Clones: A Localized Approach for Global Topology and Identity Tracing in IoT Networks

Clone attack is considered as a severely destructive threat in Internet of Things (IoT), because: 1) the attack may be easily launched due to the deficiency of hardware architecture and the limited resources against physical capture and compromise and 2) it may trigger a large variety of insider and outsider attacks. Different from traditional clone attack detection approaches that ground on a large amount of data traversing the network (e.g., locations and identities), this article tackles this problem by answering the following fundamental questions: do we really need so much raw information? whether there is an alternative for local event detection by a node far away from that event? when acquiring/tracing global knowledge of a system/network, do we really need a global collection effort? These questions are of much importance in a large variety of networks. Specifically, this article provides a collective memory design for global topology and identity tracing (GTI Tracing), via a localized computing paradigm within neighborhood. This localized paradigm computationally builds a connection of identity and topology from time and space domain to a new computation domain. Such a computation domain retains four properties: 1) transitivity; 2) global convergence; 3) determinacy; and 4) causality. With byte-size information at an arbitrary device, it can recover and keep tracing global topology and identity information, and thus providing deterministic detection of clones. Both theoretical analysis and experimental study have shown the advantages of the proposed design in both detection accuracy and privacy protection, at a cost of light communication, storage, and computation overhead at each device.
Compact CMOS Miller OpAmp With High EMI-Immunity

In this work, a CMOS Miller Operational Amplifier (OpAmp) with high immunity to electromagnetic interference (EMI) is proposed. The proposed OpAmp has unique features, such as compact power and low output offset voltage, when compared to the classical Miller OpAmp and the robust Miller OpAmp reported in the literature. The proposed compact Miller OpAmp has a reduced area, when compared to the robust Miller OpAmp. The output offset current modeling equations are derived for the proposed compact Miller OpAmp including the body effect and channel length modulation. The results are validated with simulations in the presence of a test EMI signal of 1 Vpp in 1.8 V, 0.18 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m CMOS technology. The power consumption of the proposed compact Miller OpAmp with source degeneration resistance <inline-formula><tex-math notation="LaTeX">$R_S$</tex-math></inline-formula> is 8% less and the area is 5.6% less than the robust Miller OpAmp with source degeneration resistance <inline-formula><tex-math notation="LaTeX">$R_S$</tex-math></inline-formula>. The performance result shows that the maximum EMI-induced input offset voltage for the proposed compact Miller OpAmp with source degeneration resistance <inline-formula><tex-math notation="LaTeX">$R_S$</tex-math></inline-formula> is 4.6 mV over a wide frequency range from 1 MHz to 1 GHz, which is lower when compared to the classical Miller OpAmp and the robust Miller OpAmp with source degeneration resistance <inline-formula><tex-math notation="LaTeX">$R_S$</tex-math></inline-formula>.
Deep-Reinforcement-Learning-Based Spectrum Resource Management for Industrial Internet of Things

The Industrial Internet of Things (IIoT) has attracted tremendous interest from both industry and academia as it can significantly improve production efficiency and system intelligence. However, with the explosive growth of various types of user equipment (UE) and data flow, IIoT experiences spectrum resource scarcity for wireless applications. In this article, we propose a solution for spectrum resource management for the IIoT network, with the objective of facilitating the limited spectrum sharing between different kinds of UEs. To overcome the challenges of unknown dynamic IIoT environments, a modified deep $Q$ -learning network (MDQN) is developed. Considering the cost effectiveness of IIoT devices, the base station (BS) acts as a single agent and centrally manages the spectrum resources, which can be executed without coordination or exchange between UEs. In this article, we first built a realistic IIoT model and design a simple medium access control (MAC) frame structure to facilitate the environment state observation. Then, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various types of UEs. In addition, to improve the learning efficiency, we compress the action space and propose a priority experience replay strategy based on decreasing temporal difference (TD) error. Finally, simulation results show that the proposed algorithm can successfully achieve dynamic spectrum resource management in the IIoT network. Compared with other algorithms, it can achieve superior network performance with a faster convergence rate.
Midgut barriers prevent the replication and dissemination of the yellow fever vaccine in Aedes aegypti

Background To be transmitted to vertebrate hosts via the saliva of their vectors, arthropod-borne viruses have to cross several barriers in the mosquito body, including the midgut infection and escape barriers. Yellow fever virus (YFV) belongs to the genus Flavivirus, which includes human viruses transmitted by Aedes mosquitoes, such as Dengue and Zika viruses. The live-attenuated YFV-17D vaccine has been used safely and efficiently on a large scale since the end of World War II. Early studies have shown, using viral titration from salivary glands of infected mosquitoes, that YFV-17D can infect Aedes aegypti midgut, but does not disseminate to other tissues. Methodology/Principal Findings Here, we re-visited this issue using a panel of techniques, such as RT-qPCR, Western blot, immunofluorescence and titration assays. We showed that YFV-17D replication was not efficient in Aedes aegypti midgut, as compared to the clinical isolate YFV-Dakar. Viruses that replicated in the midgut failed to disseminate to secondary organs. When injected into the thorax of mosquitoes, viruses succeeded in replicating into midgut-associated tissues, suggesting that, during natural infection, the block for YFV-17D replication occurs at the basal membrane of the midgut. Our NGS analysis revealed that YFV-Dakar genome exhibited a greater diversity than the vaccine strain; a trait that may contribute to its ability to infect and disseminate efficiently in Ae. aegypti. Conclusions/Significance The two barriers associated with Ae. aegypti midgut prevent YFV-17D replication. Our study contributes to our basic understanding of vector–pathogen interactions and may also aid in the development of non-transmissible live virus vaccines. Author summary Most flaviviruses, including yellow fever virus (YFV), are transmitted between hosts by mosquito bites. The yellow fever vaccine (YFV-17D) is one of the safest and most effective live virus vaccine ever developed. It is also used as a platform for engineering vaccines against other health-threatening flaviviruses, such as Japanese encephalitis, West Nile, Dengue and Zika viruses. We studied here the replication and dissemination of YFV-17D in mosquitoes. Our data showed that YFV-17D replicates poorly in mosquito midgut and is unable to disseminate to secondary organs, as compare to a YFV clinical isolate. Our study contributes to our basic understanding of the interactions between viruses and their vectors, which is key for conceiving new approaches in inhibiting virus transmission and designing non-transmissible live virus vaccines.
Theory of Allosteric Regulation in Hsp70 Molecular Chaperones

Abstract Heat-shock proteins of 70 kDa (Hsp70s) are ubiquitous molecular chaperones that function in protein folding as well as other vital cellular processes. They bind and hydrolyze ATP in a nucleotide-binding domain (NBD) to control the binding and release of client polypeptides in a substrate-binding domain (SBD). However, the molecular mechanism for this allosteric action has remained unclear. Here, we develop and experimentally quantify a theoretical model for Hsp70 allostery based on equilibria among Hsp70 conformational states. We postulate that, when bound to ATP, Hsp70 is in equilibrium between a restraining state (R) that restricts ATP hydrolysis and binds peptides poorly, if at all, and a stimulating state (S) that hydrolyzes ATP relatively rapidly and has high intrinsic substrate affinity but rapid binding kinetics; after the hydrolysis to ADP, NBD and SBD disengage into an uncoupled state (U) that binds peptide substrates tightly, but now with slow kinetics of exchange.
Compressed Sensing-Based Robust Phase Retrieval via Deep Generative Priors

Algorithmic phase retrieval offers an alternative means to recover the phase of optical images without requiring sophisticated measurement setups such as holography. This article proposes a framework to regularize the highly ill-posed and non-linear phase retrieval problem through deep generative priors by simply using gradient descent algorithm. We experimentally show effectiveness of the proposed approach for coded diffraction pattern measurements that are relevant in optical imaging setup. We demonstrate that the proposed approach achieves impressive results when compared with conventional hand-engineered priors in terms of number of measurements and robustness against noise. The performance of traditional generative priors based approaches depends upon how well the range of the generator spans the image class. To address this issue, we further modify the proposed algorithm to allow the generative model to explore solutions outside its range, leading to improved performance. Finally, we verify the effectiveness of the proposed approach on a real transmission matrix dataset in an actual application of multiple scattering media imaging.
Cable bacteria promote DNRA through iron sulfide dissolution

Cable bacteria represent a newly discovered group of filamentous microorganisms, which are capable of spatially separating the oxidative and reductive half‐reactions of their sulfide‐oxidizing metabolisms over centimeter distances. We investigated three ways that cable bacteria might interact with the nitrogen (N) cycle: (1) by reducing nitrate through denitrification or dissimilatory nitrate reduction to ammonium (DNRA) within their cathodic cells; (2) by nitrifying ammonium within their anodic cells; and (3) by indirectly affecting denitrification and/or DNRA by changing the Fe2+ concentration in the surrounding sediment. We performed 15N labeling laboratory experiments to measure these three processes using cable bacteria containing sediments from the Yarra River, Australia, and from Vilhelmsborg Sø, Denmark. Our results revealed that in the targeted systems, cable bacteria themselves did not perform significant rates of denitrification, DNRA, or nitrification. However, cable bacteria exhibited an important indirect effect, whereby they increased the Fe2+ pool through iron sulfide dissolution. This elevated availability of Fe2+ significantly increased DNRA and in some cases decreased denitrification. Thus, cable bacteria presence may affect the relative importance of DNRA in sediments and thus the extent by which bioavailable nitrogen is lost from the system.
Predicting Human Mobility via Long Short-Term Patterns

Predicting human mobility has great significance in Location based Social Network applications, while it is challenging due to the impact of historical mobility patterns and current trajectories. Among these challenges, historical patterns tend to be crucial in the prediction task. However, it is difficult to capture complex patterns from long historical trajectories. Motivated by recent success of Convolutional Neural Network (CNN)-based methods, we propose a Union ConvGRU (UCG) Net, which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories. Specifically, we first incorporate historical trajectories into hidden states by a shared-weight layer, and then utilize a 1D CNN to capture short-term pattern of hidden states. Next, an average pooling method is involved to generate separated hidden states of historical trajectories, on which we use a Fully Connected (FC) layer to capture longterm pattern subsequently. Finally, we use a Recurrent Neural Net-work (RNN) to predict future trajectories by integrating current trajectories and long short-term patterns. Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.
FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features

The active recognition of interesting targets has been a vital issue for synthetic aperture radar (SAR) systems. The SAR recognition methods are mainly grouped as follows: extracting image features from the target amplitude image or matching the testing samples with the template ones according to the scattering centers extracted from the target complex data. For amplitude image-based methods, convolutional neural networks (CNNs) achieve nearly the highest accuracy for images acquired under standard operating conditions (SOCs), while scattering center feature-based methods achieve steady performance for images acquired under extended operating conditions (EOCs). To achieve target recognition with good performance under both SOCs and EOCs, a feature fusion framework (FEC) based on scattering center features and deep CNN features is proposed for the first time. For the scattering center features, we first extract the attributed scattering centers (ASCs) from the input SAR complex data, then we construct a bag of visual words from these scattering centers, and finally, we transform the extracted parameter sets into feature vectors with the $k$ -means. For the CNN, we propose a modified VGGNet, which can not only extract powerful features from amplitude images but also achieve state-of-the-art recognition accuracy. For the feature fusion, discrimination correlation analysis (DCA) is introduced to the FEC framework, which not only maximizes the correlation between the CNN and ASCs but also decorrelates the features belonging to different categories within each feature set. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database demonstrate that the proposed FEC achieves superior effectiveness and robustness under both SOCs and EOCs.
A comparative study on community detection and clustering algorithms for text categorisation

One of the main tasks of Text Mining is organising a large number of unlabelled documents into a smaller set of meaningful and coherent clusters, similar with respect to their content. Clustering algorithms are usually carried on documents × terms matrices, algebraically representing each document as a vector. Nevertheless, a collection of documents can also be encoded differently, e.g. by considering a documents × documents representation. This peculiar data structure can be seen as an adjacency matrix and graphically displayed as a graph. In the framework of Network Analysis, community detection is performed on such graphs to find groups of nodes sharing common characteristics, and play similar roles. This paper aims at evaluating the use of different data structures and different grouping criteria, showing the effectiveness of the different alternatives in a text categorisation strategy. We performed a comparative study involving both classical text clustering approaches and community detection approaches, testing and discussing their performances.
An NFC-CPT-Combined Coupler With Series- None Compensation for Metal-Cover Smartphone Applications

A near-field communication capacitive power transfer (NFC-CPT)-combined coupler with a series-none compensation topology is proposed for metal-cover smartphone applications. To achieve capacitive power transfer through metal cover, the metal cover itself is used as the receiving metal plates. To realize NFC through metal cover, the cross slot including a slim horizontal slot and a rectangular slot is embedded on the metal cover, which mitigates the eddy current loss and increases the inductive coupling for the NFC antenna. Besides, the cross slot divides the metal cover into two separate metal plates, which is necessary for capacitive power transfer. Considering the space limitation and safety requirement, no compensation components are integrated inside the smartphone and only a series inductance is integrated on the transmitter side. A prototype of the proposed NFC-CPT-combined coupler has been built. The wireless power transfer and NFC through metal cover have been validated through experiment. The experimental results show that the prototype achieves 5-W output power with a 93.5% circuit efficiency at 6.78 MHz, and the detection range of the NFC antenna is 61 mm.