Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Global Population Datasets: Differences and Spatial Distribution Characteristics

Spatial data of regional populations are indispensable in studying the impact of human activities on resource utilization and the ecological environment. Because the differences between datasets and their spatial distribution are still unclear, this has become a puzzle in data selection and application. This study is based on four mainstream spatialized population datasets: the History Database of the Global Environment version 3.2. 000 (HYDE), Gridded Population of the World version 4 (GPWv4), Global Human Settlement Layer (GHSL), and WorldPop. In view of possible influences of geographical factors, this study analyzes the differences in accuracy of population estimation by computing relative errors and population spatial distribution consistency in different regions by comparing datasets pixel by pixel. The results demonstrate the following: (1) Source data, spatialization methods, and case area features affect the precision of datasets. As the main data source is statistical data and the spatialization method maintains the population in the administrative region, the populations of GPWv4 and GHSL are closest to the statistical data value. (2) The application of remote sensing, mobile communication, and other geospatial data makes the datasets more accurate in the United Kingdom, with rich information, and the absolute value of relative errors is less than 4%. In the Tibet Autonomous Region of China, where data are hard to obtain, the four datasets have larger relative errors. However, the area where the four datasets are completely consistent is as high as 84.73% in Tibet, while in the UK it is only 66.76%. (3) The areas where the spatial patterns of the four datasets are completely consistent are mainly distributed in areas with low population density, or with developed urbanization and concentrated population distribution. Areas where the datasets have poor consistency are mainly distributed in medium population density areas with high urbanization levels. Therefore, in such areas, a more careful assessment should be made during the data application process, and more emphasis should be placed on improving data accuracy when using spatialization methods.
Perceptions and Misperceptions of Smartphone Use: Applying the Social Norms Approach

The social norms approach is an established technique to bring about behaviour change through challenging misperceptions of peer behaviour. This approach is limited by a reliance on self-report and a lack of interactivity with the target population. At the same time, excessive use of digital devices, known as digital addiction, has been recognized as an emergent issue. There is potential to apply the social norms approach to digital addiction and, in doing so, address some of the limitations of the social norms field. In this study, we trialled a social norms intervention with a sample of smartphone users (n = 94) recruited from the users of a commercial app designed to empower individuals to reduce their device usage. Our results indicate that most of the sample overestimated peer use of smartphone apps, demonstrating the existence of misperceptions relating to smartphone use. Such misperceptions are the basis for the social norms approach. We also document the discrepancy between self-report and smartphone usage data as recorded through data collected directly from the device. The potential for the application of the social norms approach and directions for future research are discussed.
catwoman: A transit modelling Python package for asymmetric light curves

When exoplanets pass in front of their stars from our point of view on Earth, they imprint a transit signature on the stellar light curve which, to date, has been assumed to be symmetric in time, owing to the planet being modelled as a circular area occulting the stellar surface (Kreidberg, 2015; Luger et al., 2019; see, e.g., Mandel & Agol, 2002). However this signature might be asymmetric due to several possible effects, one of which is the different temperature/pressure and/or chemical compositions the different terminator regions a transiting planet could have (see, e.g., Powell et al., 2019). Being able to model these asymmetric signatures directly from transit light curves could give us an unprecedented glimpse into planetary 3-dimensional structure, helping constrain models of atmospheric evolution, structure and composition.
Aspects of Digitalization and Related Impact on Green Tourism in European Countries

The digital world requires the implementation of new technologies and customer-driven business transformation. As the tourism sector may experience unanticipated ways of developing new technologies due to the current global health crisis, the standard travel experience could be changed. The main aim of this study was to analyze the influence of digitalization and tourists’ preferences in terms of accommodation and economic well-being implying sustainability. This paper applied a regression analysis and principal component analysis to achieve the above objective. Research exposed the fact that tourists’ preferences towards green destinations and ecological accommodation establishments, as well as Internet use in travel planning, may have a significant influence on the sustainability of tourism. This study identified behavioral models of 30 European countries from the sustainable tourism and digitalization perspective and made recommendations on economic and social policy measures to ensure the sustainable nature of tourism activity.
Impact of UAV Surveying Parameters on Mixed Urban Landuse Surface Modelling

Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.
Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration

Point cloud registration combines multiple point cloud data sets collected from different positions using the same or different devices to form a single point cloud within a single coordinate system. Point cloud registration is usually achieved through spatial transformations that align and merge multiple point clouds into a single globally consistent model. In this paper, we present a new segmentation-based approach for point cloud registration. Our method consists of extracting plane structures from point clouds and then, using the 4-Point Congruent Sets (4PCS) technique, we estimate transformations that align the plane structures. Instead of a global alignment using all the points in the dataset, our method aligns 2-point clouds using their local plane structures. This considerably reduces the data size, computational workload, and execution time. Unlike conventional methods that seek to align the largest number of common points between entities, the new method aims to align the largest number of planes. Using partial point clouds of multiple real-world scenes, we demonstrate the superiority of our method compared to raw 4PCS in terms of quality of result (QoS) and execution time. Our method requires about half the execution time of 4PCS in all the tested datasets and produces better alignment of the point clouds.
28pAK-16 Spin-Orbital Quantum Liquid on the Honeycomb Lattice

The main characteristic of Mott insulators, as compared to band insulators, is to host low-energy spin fluctuations. In addition, Mott insulators often possess orbital degrees of freedom when crystal-field levels are partially filled. While in the majority of Mott insulators, spins and orbitals develop long-range order, the possibility for the ground state to be a quantum liquid opens new perspectives. In this paper, we provide clear evidence that the spin-orbital SU(4) symmetric Kugel-Khomskii model of Mott insulators on the honeycomb lattice is a quantum spin-orbital liquid. The absence of any form of symmetry breaking-lattice or SU(N)-is supported by a combination of semiclassical and numerical approaches: flavor-wave theory, tensor network algorithm, and exact diagonalizations. In addition, all properties revealed by these methods are very accurately accounted for by a projected variational wave function based on the pi-flux state of fermions on the honeycomb lattice at 1/4 filling. In that state, correlations are algebraic because of the presence of a Dirac point at the Fermi level, suggesting that the symmetric Kugel-Khomskii model on the honeycomb lattice is an algebraic quantum spin-orbital liquid. This model provides an interesting starting point to understanding the recently discovered spin-orbital-liquid behavior of Ba3CuSb2O9. The present results also suggest the choice of optical lattices with honeycomb geometry in the search for quantum liquids in ultracold four-color fermionic atoms.
MemSZ

This article describes Memory Squeeze (MemSZ), a new approach for lossy general-purpose memory compression. MemSZ introduces a low latency, parallel design of the Squeeze (SZ) algorithm offering aggressive compression ratios, up to 16:1 in our implementation. Our compressor is placed between the memory controller and the cache hierarchy of a processor to reduce the memory traffic of applications that tolerate approximations in parts of their data. Thereby, the available off-chip bandwidth is utilized more efficiently improving system performance and energy efficiency. Two alternative multi-core variants of the MemSZ system are described. The first variant has a shared last-level cache (LLC) on the processor-die, which is modified to store both compressed and uncompressed data. The second has a 3D-stacked DRAM cache with larger cache lines that match the granularity of the compressed memory blocks and stores only uncompressed data. For applications that tolerate aggressive approximation in large fractions of their data, MemSZ reduces baseline memory traffic by up to 81%, execution time by up to 62%, and energy costs by up to 25% introducing up to 1.8% error to the application output. Compared to the current state-of-the-art lossy memory compression design, MemSZ improves the execution time, energy, and memory traffic by up to 15%, 9%, and 64%, respectively.
Mechanistic Principles of Enzyme Activity

Structural Aspects of Zinc Protease Mechanisms Stereoelectronic Analysis of Enzymatic Reactions Intramolecularity: Proximity and Strain Structural and Energetic Aspects of Protolytic Catalysis by Enzymes: Charge-Relay Catalysis in the Function of Serine Proteases Electron Transfer in Cytochromes C and B Chemical Studies Related to Iron Protoporphyrin-IX Mixed Function Oxidases Porphyrin Metalation Reactions in Biochemistry Chemical Studies and the Mechanism of Flavin and Porphyrin Mixed Function Oxidase Enzymes Glutahione-Dependent Aldehyde Oxidation Reactions.
Cucumber Mosaic Virus and their associated satellite RNAs infecting banana (Musa sp. Genomic group AAA) in Côte d’Ivoire : A molecular characterization

In Côte d’Ivoire, banana (Musa sp.) ranks third among exportation products and represents 3% of the Gross Domestic Product with a national production up to 500000 tons in 2019. Banana is subject to numerous disease agents among which viruses cause significant losses. To figure out the impact of viruses in Ivorian industrial banana fields, surveys were conducted in the 7 main banana production departments. A total of 260 leaf fragments presenting viral symptoms were collected and analyzed. From the 65 leaf fragments used for biological indexing, 14 showed symptoms related to Cucumber mosaic virus (CMV). CMV presence was confirmed by double-antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) using CMV polyclonal antibodies. CMV strains we isolated, appeared to be highly infectious and to produce various symptoms like mosaic, chlorosis, and necrotic spots on Cucumis sativus, Cucurbita pepo, and Nicotiana tabacum. Satellite RNAs (SatRNAs) associated with CMV isolates were also detected using reverse transcription polymerase chain reaction (RT-PCR) with a degenerate primer pair. CMV’s coat protein as well as satRNAs was sequenced. Novel Ivorian coat proteins and satRNAs were compared to publicly available sequences. We noticed a single amino acid substitution (Serine to Leucine) at position 73 of the novel coat protein that allowed us to divide Ivorian CMV strains into two groups. Molecular and phylogenetic analysis suggested that Ivorian strains might be classified into CMV Subgroup IA. We also discovered that satellite RNA associated with Ivorian CMVs form a separate clade.
Identification of Gait Events in Healthy Subjects and With Parkinson’s Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson’s disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( ${F}_{{1}}$ -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.
Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method.
Chronic stress has different immediate and delayed effects on hippocampal calretinin‐ and somatostatin‐positive cells

Past studies find that chronic stress alters inhibitory, GABAergic circuitry of neurons in distinct hippocampal subregions. Less clear is whether these effects persist weeks after chronic stress ends, and whether these effects involve changes in the total number of hippocampal GABAergic neurons or modulates the function of specific GABAergic subtypes. A transgenic mouse line (VGAT:Cre Ai9) containing an indelible marker for GABAergic neurons (tdTomato) throughout the brain was used to determine whether chronic stress alters total GABAergic neuronal number or the expression of two key GABAergic cell subtypes, calretinin expressing (CR+) and somatostatin expressing (SOM+) neurons, and whether these changes endure weeks later. Male and female mice were chronically stressed in wire mesh restrainers for 6h/d/21d (Str) or not (Con), and then allowed a 3 week rest period (Str‐Rest) and compared to those without a rest period (Str‐NoRest). Epifluorescent microscope images of immunohistochemistry‐processed brains were quantified to estimate the total number of fluorescently‐labeled hippocampal GABAergic neurons and the proportion that were CR+ or SOM+. Neither chronic stress nor sex altered the total number of GABAergic cells. In contrast, chronic stress reduced the expression of CR+ in the CA3 region of the hippocampus in both males and females, with robust reductions in the DG region of males, but not females, and these changes reversed following a rest period. Chronic stress also reduced the proportion of hippocampal SOM+ neurons and this reduction persisted even with a rest period. These results show chronic stress dynamically reduced CR expression without changing total inhibitory neuronal number and point to CR as a potential new lead to understand mechanisms by which chronic stress alters hippocampal function.
Relative age-related differences between different competitive levels and field positions in young soccer players

ABSTRACT The aims of this study were to report the relative age effect in different competitive levels and field positions and to analyse the differences within and between different competitive levels and field positions. Data for 203 young soccer players (14.2 ± 1.1 years) included anthropometrics and physical performance (Countermovement jump [CMJ], 30-m sprint, T-test and Yo-Yo IR1). Their competitive level and their field position were registered. The percentage of relative older players (1stHY) was higher in the better competitive levels (L1: 80.6%, p <.001; L2: 68.2%, p <.001 and L3: 58.5%, p <.01), but it was similar between field positions (DF: 68.1%, p <.001; MF: 69.6%, p <.001 and FW: 67.2%, p <.001). Anthropometrical and physical performance differences were found between players of different competitive levels but not between relative older and younger players in each competitive level and field position. The relative age effect is higher in the better competitive levels. Anthropometrical and physical performance differences between players are not due to the relative age but to the level of competition. Relatively older players do not seem to be more likely to be selected for specific field positions. The causes of relative age effect need more research.
Accessible dynamic micropatterns in monolayer cultures via modified desktop xurography

Micropatterned cell cultures provide an important tool to understand dynamic biological processes, but often require specialized equipment and expertise. Here we present subtractive bioscribing (SuBscribing), a readily accessible and inexpensive technique to generate dynamic micropatterns in biomaterial monolayers on-the-fly. We first describe our modifications to a commercially available desktop xurographer and demonstrate the utility and limits of this system in creating micropatterned cultures by mechanically scribing patterns into a brittle, non-adhesive biomaterial layer. Patterns are sufficiently small to influence cell morphology and orientation and can be extended to pattern large areas with complex reproducible shapes. We also demonstrate the use of this system as a dynamic patterning tool for cocultures. Finally, we use this technique to explore and improve upon the well-established epithelial scratch assay, and demonstrate that robotic control of the scratching tool can be used to create custom-shaped wounds in epithelial monolayers, and that the scribing direction leaves trace remnants of matrix molecules that may significantly affect conventional implementations of this common assay.
Combination Therapy to Treat Fungal Biofilm-Based Infections

An increasing number of people is affected by fungal biofilm-based infections, which are resistant to the majority of currently-used antifungal drugs. Such infections are often caused by species from the genera Candida, Aspergillus or Cryptococcus. Only a few antifungal drugs, including echinocandins and liposomal formulations of amphotericin B, are available to treat such biofilm-based fungal infections. This review discusses combination therapy as a novel antibiofilm strategy. More specifically, in vitro methods to discover new antibiofilm combinations will be discussed. Furthermore, an overview of the main modes of action of promising antibiofilm combination treatments will be provided as this knowledge may facilitate the optimization of existing antibiofilm combinations or the development of new ones with a similar mode of action.