Miracle-dz/UniFAR-FTUs huggingface dataset:
Absolute phase retrieval for a single-shot fringe projection profilometry based on deep learning

Abstract. A deep learning-based method is proposed to recover the absolute phase value from a single fringe pattern. We propose a deep neural network architecture that includes two subnetworks used for wrapping phase calculation and phase unwrapping, respectively. The training set is generated with the absolute phase obtained by the combination of phase shifting and gray coding. In addition, a reference plane is adopted to provide periodic range information for phase unwrapping. Then according to the output of the well-trained network, a high-quality absolute phase is obtained through only a single fringe pattern of the measured object. Experiments on the test set verify that high accuracy for complex texture objects is acquired using the proposed method, which indicates its potential in high-speed measurement.
Aerosol processes in the Planetary Boundary Layer: High resolution Aerosol Mass Spectrometry on a Zeppelin NT Airship

Atmospheric aerosol plays an ambivalent role for mankind. On the one hand, aerosol is known to have adverse health effects and even increase mortality. On the other hand, a raised aerosol concentration in the atmosphere is the only anthropogenic contribution to the Earth’s climate known to have a net cooling effect by scattering light and acting as cloud condensation nuclei (CCN). Among other things, the magnitude of these effects depend on the chemical composition of the aerosol. In this work, an Aerosol Mass Spectrometer (AMS) was adapted for airborne aerosol chemical composition measurements on a Zeppelin NT airship. The Zeppelin platform allows for measurements with high spatial resolution throughout the entire planetary boundary layer (PBL). The new instrument was successfully operated aboard the Zeppelin NT on nine out of ten flight days of a seven week campaign in two countries in the context of the European project PEGASOS. The mass concentrations of the measured aerosol chemical species are presented for these days. The campaign observations revealed a higher relative concentration of nitrate aerosol in the Netherlands compared to the Po Valley in northern Italy. For sulfate aerosol, the situation was opposite. Furthermore, the organic aerosol was more aged in the Po Valley than in the Netherlands. The influence of the aerosol chemical composition on the cloud droplet activation was studied by computation of the critical activation diameter and comparing to aerosol number size distribution measurements. In some situations, the chemical composition was the driving force behind changes in CCN concentration, in other situations its effect was overshadowed by changes in the size distribution. Differences between the individual layers of the PBL were identified in the mass concentrations of the aerosol chemical species and the organic aerosol age. A backward trajectory analysis was used in conjunction with the obtained data to estimate the aerosol production rate of the Po Valley to 0.60 (μg/m3)/h for organic aerosol, 0.88 (μg/m3)/h for nitrate aerosol, and 0.15 (μg/m3)/h for sulfate aerosol. The aerosol ion balance was used to gain insights into organic aerosol functionalization patterns.
Design and simulation of the optical amplitude-shift keying modulator using Fabry–Perot resonator coupled with graphene at near-infrared frequencies

Abstract. An optical amplitude modulator operating at near-infrared frequencies is presented. The proposed modulator structure consists of a Fabry–Perot (FP) cavity coupled with graphene located in the middle of two dielectric Bragg mirrors. Graphene’s presence as an active material with adjustable optical conductivity, as well as its effective interaction with powerful standing waves confined in the cavity, enables spectral tuning of the transmission spectrum. Since the resonator has a high quality factor, a small resonance frequency shift due to graphene Fermi energy change results in a huge change of the input light transmission value of the resonator, which makes it possible to achieve high modulation depth (MD). The results demonstrate that the proposed optical modulator has insertion loss lower than 0.1 and MD larger than 90%. It is believed that our study may be useful for optical wireless communication acting at near-infrared region.
High focusing efficiency terahertz lens based on Huygens metasurface

Abstract. We propose an ultra-thin Huygens lens with high focusing efficiency and robustness that is composed of a unit cell with a pair of C-shaped split-ring resonators (CSRRs). Compared with the single-layer CSRR lens, the focusing efficiency of the Huygens metasurface lens improves 90% at the best focal lengths and three times at the central frequency. Our device has robustness against geometrical parameters, such as the difference of the opening angle, the mismatch of the opening angle, and the mismatch of the radius, which reduces the manufacturing difficulty. The Huygens lens has unprecedented potential for real-time, quick, electromagnetic wave processing such as holography, beam steering/splitter, imaging, and launcher control.
The global threat from plastic pollution

Plastic pollution accumulating in an area of the environment is considered “poorly reversible” if natural mineralization processes occurring there are slow and engineered remediation solutions are improbable. Should negative outcomes in these areas arise as a consequence of plastic pollution, they will be practically irreversible. Potential impacts from poorly reversible plastic pollution include changes to carbon and nutrient cycles; habitat changes within soils, sediments, and aquatic ecosystems; co-occurring biological impacts on endangered or keystone species; ecotoxicity; and related societal impacts. The rational response to the global threat posed by accumulating and poorly reversible plastic pollution is to rapidly reduce plastic emissions through reductions in consumption of virgin plastic materials, along with internationally coordinated strategies for waste management.
Big Tech platforms in health research: Re-purposing big data governance in light of the General Data Protection Regulation’s research exemption

The emergence of a global industry of digital health platforms operated by Big Tech corporations, and its growing entanglements with academic and pharmaceutical research networks, raise pressing questions on the capacity of current data governance models, regulatory and legal frameworks to safeguard the sustainability of the health research ecosystem. In this article, we direct our attention toward the challenges faced by the European General Data Protection Regulation in regulating the potentially disruptive engagement of Big Tech platforms in health research. The General Data Protection Regulation upholds a rather flexible regime for scientific research through a number of derogations to otherwise stricter data protection requirements, while providing a very broad interpretation of the notion of “scientific research”. Precisely the breadth of these exemptions combined with the ample scope of this notion could provide unintended leeway to the health data processing activities of Big Tech platforms, which have not been immune from carrying out privacy-infringing and socially disruptive practices in the health domain. We thus discuss further finer-grained demarcations to be traced within the broadly construed notion of scientific research, geared to implementing use-based data governance frameworks that distinguish health research activities that should benefit from a facilitated data protection regime from those that should not. We conclude that a “re-purposing” of big data governance approaches in health research is needed if European nations are to promote research activities within a framework of high safeguards for both individual citizens and society.
Toxicity and verbal aggression on social media: Polarized discourse on wearing face masks during the COVID-19 pandemic

Medical and public health professionals recommend wearing face masks to combat the spread of the coronavirus disease of 2019 (COVID-19). While the majority of people in the United States support wearing face masks as an effective tool to combat COVID-19, a smaller percentage declared the recommendation by public health agencies as a government imposition and an infringement on personal liberty. Social media play a significant role in amplifying public health issues, whereby a minority against the imposition can speak loudly, perhaps using tactics of verbal aggression taking the form of toxic language. We investigated the role that toxicity plays in the online discourse around wearing face masks. Overall, we found tweets including anti-mask hashtags were significantly more likely to use toxic language, while tweets with pro-mask hashtags were somewhat less toxic with the exception of #WearADamnMask. We conclude that the tensions between these two positions raise doubt and uncertainty around the issue, which make it difficult for health communicators to break through the clutter in order to combat the infodemic. Public health agencies and other governmental institutions should monitor toxicity trends on social media in order to better ascertain prevailing sentiment toward their recommendations and then apply these data-driven insights to refine and adapt their risk communication messaging toward mask wearing, vaccine uptake, and other interventions.
A review on applications of rotary-wing unmanned aerial vehicle charging stations

Today’s technology allows people to remotely control and monitor many systems. In these technological systems, robots or unmanned vehicles are generally used, which are controlled remotely without human interaction. Unmanned aerial vehicle (UAV), which does not have a pilot on it, is one of the unmanned vehicles capable of flying either remotely or automatically. UAVs are among the systems that are used in many fields for military, civil, and academic purposes and are constantly developing in parallel with the advancement of technology. One of the biggest problems of UAVs that are effectively used in many areas is undoubtedly flight times. To overcome this situation, charging stations are used and allow the UAV batteries to be charged without human intervention. In this article, a review study about the charging stations developed for charging batteries used in UAVs has been made. In this study, the findings obtained as a result of literature research on charging stations were analyzed and the performances of charging stations were compared. The main purpose of this review study is to guide people who will develop a charging station for rotary-wing UAVs by providing a preliminary research opportunity and to help choose one of the wired or wireless charging stations according to the needs in the applications to be developed.
Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study

COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown (8–8.30 p.m. 23 March 2020) on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the UK 48 hours before and 48 hours after the announcement (n = 2,531,888). We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’.
Design and evaluation of a surface electromyography-controlled lightweight upper arm exoskeleton rehabilitation robot

Nowadays, the rehabilitation robot has been developed for rehabilitation therapy. However, there are few studies on upper arm exoskeletons for rehabilitation training of muscle strength. This article aims to design a surface electromyography-controlled lightweight exoskeleton rehabilitation robot for home-based progressive resistance training. The exoskeleton’s lightweight structure is designed based on the kinematic model of the elbow joint and ergonomics sizes of the arm. At the same time, the overall weight of the exoskeleton is controlled at only 3.03 kg. According to the rehabilitation training task, we use torque limit mode to ensure stable torque output at variable velocity. We also propose a surface electromyography-based control method, which uses k- Nearest Neighbor algorithm to classify surface electromyographic signals under progressive training loads, and utilizes principal component analysis to improve the recognition accuracy to control the exoskeleton to provide muscle strength compensation. The assessment experiment of the exoskeleton rehabilitation robot shows that the dynamic recognition accuracy of this control method is 80.21%. Muscle activity of biceps brachii and triceps brachii under each training load decreases significantly when subjects with the exoskeleton robot. The results indicate that the exoskeleton rehabilitation robot can output the corresponding torque to assist in progressive resistance training. This study provides a solution to potential problems in the family-oriented application of exoskeleton rehabilitation robots.
Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.
Seed Germination Ecology of Southeastern Australian Rigid Ryegrass (Lolium rigidum) Populations

Abstract Herbicide resistance is an increasing issue in many weed species, including rigid ryegrass (Lolium rigidum Gaudin); a major weed of winter cropping systems in southern Australia. Recently, this weed has also been found in summer crops in the southeastern region of Australia. Effective control of this herbicide-resistant weed across southeastern Australia requires alternative management strategies. These strategies can be informed by analyses on the interaction of germinable seeds with their regional environments and by identifying the differences between populations of varying herbicide-resistance levels. In this study, we explore how various environmental factors differentially affect the seed germination and seedling emergence of three L. rigidum populations, including one glyphosate-resistant population (GR), one glyphosate-susceptible population (GS), and one population of unknown resistance status (CC04). Germination was greater than 90% for all populations at each temperature regime, except 15/5 C. Populations germinated at a lower rate under 15/5 C, ranging from 74% to 87% germination. Salt stress had a similar effect on the germination of all populations, with 0% germination occurring at 250 mM salt stress. Population GS had greater tolerance to osmotic stress, with 65% germination at –0.4 MPa compared with 47% and 43% germination for CC04 and GR, respectively; however, germination was inhibited at –0.8 and –1.6 MPa for all populations. All populations had lower germination when placed in complete darkness as opposed to alternating light/dark. Germination in darkness was lower for CC04 (69%) than GR (83%) and GS (83%). Seedling emergence declined with increasing burial depth with the lowest emergence occuring at 8 cm (37%) when averaged over the populations. These results indicate that L. rigidum can survive under a range of environmental variables and that the extent of survival differs based on population; however, there was no difference based on herbicide-resistance status.
The endoplasmic reticulum-resident protein TMEM-120/TMEM120A promotes fat storage in C. elegans and mammalian cells

The synthesis of triacylglycerol (TAG) is essential for the storage of excess fatty acids, which can subsequently be used for energy or cell growth. A series of enzymes act in the endoplasmic reticulum (ER) to synthesize TAG, prior to its transfer to lipid droplets (LDs), which are conserved organelles for fat storage. Here, we report that the deficiency of TMEM-120/TMEM120A, a protein with 6-transmembrane helices, retards TAG synthesis and LD expansion in C. elegans. A missense mutation near the predicted coenzyme A binding site of TMEM-120 confers strong loss of function phenotypes. GFP fusion proteins of TMEM-120, expressed at the endogenous level in live worms, were observed throughout the ER network. Using Stimulated Raman Scattering, we discovered a specific requirement of TMEM-120 in the storage of exogenous fatty acids in LDs. Knockdown of TMEM120A impedes adipogenesis of pre-adipocytes in vitro, while its over-expression is sufficient to promote LD expansion. Pharmacological studies indicate that TMEM120A most likely acts upstream of diacylglycerol O-acyltransferase 1 (DGAT1). Our results suggest that TMEM-120/TMEM120A plays a conserved role in increasing the efficiency of TAG synthesis.
A centronuclear myopathy-causing mutation in dynamin-2 perturbs the actin-dependent structure of dendritic spines leading to excitatory synaptic defects in a murine model of the disease

Dynamin-2 is a large GTP-ase, member of the dynamin superfamily, that regulates membrane remodeling and cytoskeleton dynamics. In the mammalian nervous system dynamin-2 modulates synaptic vesicle (SV)-recycling at the nerve terminals and receptor-trafficking to and from postsynaptic densities (PSDs). Mutations in dynamin-2 cause autosomal dominant centronuclear myopathy (CNM), a congenital neuromuscular disorder characterized by progressive weakness and atrophy of distal skeletal muscles. Cognitive defects have also been reported in dynamin-2-linked CNM patients suggesting a concomitant impairment of the central nervous system. Here we addressed the mechanisms that lead to cognitive defects in dynamin-2-linked CNM using a knock-in mouse model that harbors the p.R465W mutation in dynamin-2, the most common causing CNM. Our results show that these mice exhibit reduced capability to learn and acquire spatial and recognition memory, impaired long-term potentiation of the excitatory synaptic strength and perturbed dendritic spine morphology, which seem to be associated with actin defects. Together, these data reveal for the first time that structural and functional synaptic defects underlie cognitive defects in the CNM context. In addition our results contribute to the still scarce knowledge about the importance of dynamin-2 at central synapses.
OpenNeuro: An open resource for sharing of neuroimaging data

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 500 datasets including data from more than 18,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.
Alchemy: Distributed financial quantitative analysis system with high‐level programming model

Nowadays, the financial securities investment and transaction are processed digitally. The securities companies have accumulated a large amount of financial data. Given that, quantitative analysis has become a common method for securities investors. However, as the scale of data continues to grow, data storing and processing have increasingly become a big challenge to financial quantitative analysts. First, most existing stand‐alone quantitative analysis systems are hard to accelerate data processing in a distributed way. Second, existing distributed computing frameworks such as Apache Spark demands financial quantitative analysts with expertise knowledge on distributed computing. Third, transition from traditional stand‐alone financial quantitative analysis (FQA) system to such distributed computing system often introduces learning efforts and development overheads. To solve these problems, we propose Alchemy, a distributed processing platform tailored for FQA with high‐level programming interface. Alchemy offers distributed computing service and provides a pipeline‐based programming model which shares many similarities with the traditional stand‐alone systems, reducing learning efforts for financial quantitative analysts. The performance of Alchemy is evaluated in both experimental environment and a real‐world production environment in Huatai Securites which is a leading financial company in China. The results show that Alchemy is able to achieve up to 300 times speedup compared against existing financial quantitative analysis systems.