Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial phylogenetics of Japanese ferns: Patterns, processes, and implications for conservation

Premise Biodiversity is often only measured with species richness. However, this ignores evolutionary history and is not sufficient for making conservation decisions. Here, we characterize multiple facets and drivers of biodiversity to understand how these relate to bioregions and conservation status in the ferns of Japan. Methods We compiled a community dataset of 1,239 20 km × 20 km grid-cells including 672 taxa based on > 300,000 specimen records. We combined this with a phylogeny and functional traits to analyze taxonomic, phylogenetic, and functional diversity, and modeled biodiversity metrics in response to environmental factors and reproductive mode. Hierarchical clustering was used to delimit bioregions. Conservation status and threats were assessed by comparing the overlap of significantly diverse grid-cells with conservation zones and range maps of native Japanese deer. Results Taxonomic richness was highest at mid-latitudes. Phylogenetic and functional diversity and phylogenetic endemism were highest in small southern islands. Relative phylogenetic and functional diversity were high at high and low latitudes, and low at mid-latitudes. Grid-cells were grouped into three (phylogenetic) or four (taxonomic) major bioregions. Temperature and apomixis were identified as drivers of biodiversity patterns. Conservation status was generally high for grid-cells with significantly high biodiversity, but the threat due to herbivory by deer was greater for taxonomic richness than other metrics. Conclusions Our integrative approach reveals previously undetected patterns and drivers of biodiversity in the ferns of Japan. Future conservation efforts should recognize that threats can vary by biodiversity metric and consider multiple multiple metrics when establishing conservation priorities.
Demographic factors influencing the adoption and use of social media in university libraries in Ghana: A unified theory of acceptance and use of technology (UTAUT) approach

Abstract The purpose of this study is to examine the influence of demographics as moderators of the factors influencing library workers’ and library patrons’ behavioral intention toward the use of social media in university libraries in Ghana. The theoretical model for this study was based on the unified theory of acceptance and use of technology (UTAUT). The study employed a cross-sectional survey and quantitative data was collected from a total of six-hundred and five usable responses obtained from the sampled respondents using a pre-tested questionnaire. Statistical analyses were carried out using the t-test analysis (using Levene’s Test for Equality of Variances) and the structural equation modeling (SEM) technique. Key findings were that although partial relationships were established, the mediating roles of the demographics of library workers and patrons on factors influencing the intention to use and actual use of social media in the university libraries were significant. Establishing the effect of the user demographics on factors influencing social media use in the context of the UTAUT is novel within the Ghanaian university library setting and thus contributes new knowledge to methodological discussions to improve practices and policies on social media adoption and use. However, since this study was largely quantitative, future research could include both quantitative and qualitative research approaches to explore any other important constructs that fit into the context and can explain adoption in further detail. Recommendations based on findings are provided in the article.
Intraregional Spatial Dynamics of Quality of Life in Shrinking Cities and Their Suburbs

ABSTRACT American shrinking cities have long been characterized by the flight of residents to their surrounding suburbs. However, recently there has been increasing evidence of the ‘comeback’ of former declining industrial cities. This paper examines the intraregional spatial dynamics of quality of life to determine if there is a difference between shrinking cities and their surrounding suburbs. We found shrinking suburban areas to be most in need of planning intervention as they had lower education and physician density than the central shrinking cities, but higher crime rates and inequality than growing suburban areas.
Physical modelling of pressure flushing of sediment using lightweight materials

While designing physical hydraulic model tests to investigate the efficiency of pressure flushing, it is most likely that very fine sediments of cohesive nature are required to satisfy the relevant scaling criteria. Cohesive sediments have different physical properties than sand, and a possibility to avoid such scale effects is to use lightweight materials with a specific gravity larger than water but lower than sand as model sediment. This paper addresses this issue by presenting results from laboratory experiments mimicking pressure flushing through a bottom outlet by using different lightweight materials and sand as model sediments. The results consolidate conclusions of previous studies carried out solely with sand and show that lightweight models can be used to predict the length and volume of flushing cones. Empirical relations to predict the length and volume of flushing cones are proposed and validated against a small set of experimental data from a previous study.
Measurement of the exclusive (ʋμρ → μ⁻ ρ π⁺ ) and inclusive (ʋμN → μ⁻ N' π⁺) single pion ʋ interaction cross section in a carbon target using the SciBar detector at the K2K experiment

The discovery of neutrino oscillations phenomena is one of the most exciting developments made in the recent years in the particles physics field. The announcement of this result revived the interest in the neutrino physics, and estimulated the preparation of current and future neutrino experiments with the aim of making precise measurments of the parameters controlling the neutrino oscillations. The measurements presented in this dissertation use data collected by the K2K long-baseline neutrino experiment. It uses an artificially-produced neutrino beam to measure muon neutrino oscillations. The mean energy of those muon neutrinos is 1.3 GeV. The neutrinos are detected in a near detector system (at the KEK laboratory), and in a far detector (Super-Kamiokande). The detectors capabilities allow the measurement of the neutrino interaction rates and their energy, from which a clear indication of oscillations is obtained. Improving our understanding of neutrino-nucleus cross section is crucial to these precision studies of neutrino oscillations. Interactions in the neutrino energy around 1 GeV are particularly important because this is the region of the expected oscillation signal in many experiments, but the cross sections in this region are not very well-known. This energy region is complicated due to overlapping contributions from quasi-elastic scattering, resonant single pion production, and deep inelastic scattering. All these interactions are measured together to study the deficit in the expected number of neutrino events, and the quasi-elastic interactions apart to give this deficit as a function of the neutrino energy. We describe in this dissertation an integrated and energy neutrino dependent measurement of the cross section for resonant single charged pion production in charged-current muon neutrino interactions with a carbon target. The measurements are given taking as reference the cross section for charged-current quasi-elastic scattering. We measure the exclusive interaction channel, , and the inclusive interaction channel, ! " . The data are collected with a fully active scintillator detector, SciBar, at the K2K near detector system. The results are consistent with previous experiments, and predictions based on a widely-accepted model.
Spectral and Growth Characteristics of Willows and Maize in Soil Contaminated with a Layer of Crude or Refined Oil

Remote sensing holds great potential for detecting stress in vegetation caused by hydrocarbons, but we need to better understand the effects of hydrocarbons on plant growth and specific spectral expression. Willow (Salix viminalis var. Tora) cuttings and maize (Zea mays var. Lapriora) seedlings were grown in pots of loam soil containing a hydrocarbon-contaminated layer at the base of the pot (crude or refined oil) at concentrations of 0.5, 5, or 50 g·kg−1. Chlorophyll concentration, biomass, and growth of plants were determined through destructive and nondestructive sampling, whilst reflectance measurements were made using portable hyperspectral spectrometers. All biophysical (chlorophyll concentration and growth) variables decreased in the presence of high concentrations of hydrocarbons, but at lower concentrations an increase in growth and chlorophyll were often observed with respect to nonpolluted plants, suggesting a biphasic response to hydrocarbon presence. Absorption features were identified that related strongly to pigment concentration and biomass. Variations in absorption feature characteristics (band depth, band area, and band width) were dependent upon the hydrocarbon concentration and type, and showed the same biphasic pattern noted in the biophysical measurements. This study demonstrates that the response of plants to hydrocarbon pollution varies according to hydrocarbon concentration and that remote sensing has the potential to both detect and monitor the variable impacts of pollution in the landscape.
Multi-Layer Quality Inspection System Framework for Industry 4.0

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work-piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.
Validity of a Magnet-Based Timing System Using the Magnetometer Built into an IMU

Inertial measurement units (IMUs) represent a technology that is booming in sports right now. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically to evaluate a magnet-based timing system (M-BTS) for timing short-duration sports actions using the magnetometer built into an IMU in different sporting contexts. Forty-eight athletes (22.7 ± 3.3 years, 72.2 ± 10.3 kg, 176.9 ± 8.5 cm) and eight skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg) performed a 60-m linear sprint running test and a ski slalom, respectively. The M-BTS consisted of placing several magnets along the course in both contexts. The magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. The time between peaks was calculated. The system was validated with photocells. The 95% error intervals for the total times were less than 0.077 s for the running test and 0.050 s for the ski slalom. With the M-BTS, future studies could select and cut the signals belonging to the other sensors that are integrated in the IMU, such as the accelerometer and the gyroscope.
A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.
Tree Extraction from Airborne Laser Scanning Data in Urban Areas

Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.
The Rise of Wearable Devices during the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies.
Deep Siamese Networks Based Change Detection with Remote Sensing Images

Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.
A Novel Approach for Development and Evaluation of LiDAR Navigated Electronic Maize Seeding System Using Check Row Quality Index

Crop geometry plays a vital role in ensuring proper plant growth and yield. Check row planting allows adequate space for weeding in both direction and allowing sunlight down to the bottom of the crop. Therefore, a light detection and ranging (LiDAR) navigated electronic seed metering system for check row planting of maize seeds was developed. The system is comprised of a LiDAR-based distance measurement unit, electronic seed metering mechanism and a wireless communication system. The electronic seed metering mechanism was evaluated in the laboratory for five different cell sizes (8.80, 9.73, 10.82, 11.90 and 12.83 mm) and linear cell speed (89.15, 99.46, 111.44, 123.41 and 133.72 mm·s−1). The research shows the optimised values for the cell size and linear speed of cell were found to be 11.90 mm and 99.46 mm·s−1 respectively. A light dependent resistor (LDR) and light emitting diode (LED)-based seed flow sensing system was developed to measure the lag time of seed flow from seed metering box to bottom of seed tube. The average lag time of seed fall was observed as 251.2 ± 5.39 ms at an optimised linear speed of cell of 99.46 mm·s−1 and forward speed of 2 km·h−1. This lag time was minimized by advancing the seed drop on the basis of forward speed of tractor, lag time and targeted position. A check row quality index (ICRQ) was developed to evaluate check row planter. While evaluating the developed system at different forward speeds (i.e., 2, 3 and 5 km·h−1), higher standard deviation (14.14%) of check row quality index was observed at forward speed of 5 km·h−1.
Students' abilities to evaluate the credibility of online texts: The role of internet-specific epistemic justifications

Previous evaluation studies have rarely used authentic online texts and investigated upper secondary school students' use of evaluation criteria and deep reasoning. The associations between internet-specific epistemic justifications for knowing and credibility evaluation of online texts are not yet fully understood among adolescents. This study investigated upper secondary school students' ( N = 372) abilities to evaluate self-selected authentic online texts and the role of internet-specific epistemic justifications in students' evaluation performance when solving a health-related information problem. Students selected three texts with Google Custom Search Engine and evaluated their credibility. Students' evaluation performance across the three texts was determined according to the different aspects evaluated (author, venue, intentions, evidence and corroboration) and the depth of their evaluations. Students also filled in the Internet-Specific Epistemic Justifications (ISEJ) inventory previously validated with pre-service teachers. The results revealed considerable differences in students' abilities to evaluate online texts. Students' beliefs in justification by authority and justification by multiple sources positively predicted their evaluation performance similarly in both topics. The findings suggest that the ISEJ inventory is also valid for upper secondary school students. Students should be explicitly taught to evaluate different credibility aspects and scaffolded to deeply engage with online information.
PDMS-ZnO Piezoelectric Nanocomposites for Pressure Sensors

The addition of piezoelectric zinc oxide (ZnO) fillers into a flexible polymer matrix has emerged as potential piezocomposite materials that can be used for applications such as energy harvesters and pressure sensors. A simple approach for the fabrication of PDMS-ZnO piezoelectric nanocomposites based on two ZnO fillers: nanoparticles (NP) and nanoflowers (NF) is presented in this paper. The effect of the ZnO fillers’ geometry and size on the thermal, mechanical and piezoelectric properties is discussed. The sensors were fabricated in a sandwich-like structure using aluminium (Al) thin films as top and bottom electrodes. Piezocomposites at a concentration of 10% w/w showed good flexibility, generating a piezoelectric response under compression force. The NF piezocomposites showed the highest piezoelectric response compared to the NP piezocomposites due to their geometric connectivity. The piezoelectric compound NF generated 4.2 V while the NP generated 1.86 V under around 36 kPa pressure. The data also show that the generated voltage increases with increasing applied force regardless of the type of filler.