bluuebunny/crossref_metadata_2025 huggingface dataset:
Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover
Authors: Venter Zander S. , Barton David N. , Chakraborty Tirthankar , Simensen Trond , Singh Geethen
Published: August 2022

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m2, we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m2, we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.
Using UAV and Structure-From-Motion Photogrammetry for the Detection of Boulder Movement by Storms on a Rocky Shore Platform in Laghdira, Northwest Morocco
Authors: Sedrati Mouncef , Morales Juan A. , El M’rini Abdelmounim , Anthony Edward J. , Bulot Glen , Le Gall Romain , Tadibaght Abdelkarim
Published: August 2022

The detachment and mobilization of boulders from rocky shore platforms by waves involves complex geomorphic and hydrodynamic processes. Understanding these processes requires precise information on the rates and patterns of movement of these megaclasts scaled against the wave conditions that generate boulder mobility. Repeat photogrammetry and structure-from-motion (SfM) models commonly used in geomorphic analyses are an interesting option for monitoring boulder dynamics. In this study, we used unmanned aerial vehicle (UAV)-based digital photogrammetry and SfM differential models to identify recent boulder movements over a rocky shore platform in Laghdira, Morocco. Combining these results with data on storm occurrence in the study area allowed us to identify storm waves as the unique driver of the dislodged and mobilized boulders. The identified storm event had a significant wave height of 5.2 m. The UAV models were built from imagery captured in September and December 2019 using a DJI MAVIC PRO PLATINUM, and we used QGIS to produce 2D and 3D model outputs. The exploitation of the 2D model differentials allowed us to appreciate the response of the boulders to the storm waves and to determine platform volumetric changes and, therefore, boulder mobility. The 3D models were valuable in determining the mode of transport of the boulders. Mobility patterns included sliding, overturning with no further mobility, and rotation and saltation, as well as boulder breakup. Storm waves did not have a preferential impact on any particular boulder shape, size category, or position at the outer edge of the platform. These results highlight the utility of combining UAV surveys with identified storm events, which are much more frequent than tsunamis, in determining observed boulder initiation and mobility.
Imbalanced Underwater Acoustic Target Recognition with Trigonometric Loss and Attention Mechanism Convolutional Network
Authors: Ma Yanxin , Liu Mengqi , Zhang Yi , Zhang Bingbing , Xu Ke , Zou Bo , Huang Zhijian
Published: August 2022

A balanced dataset is generally beneficial to underwater acoustic target recognition. However, the imbalanced class distribution is always meted out in a real scene. To address this, a weighted cross entropy loss function based on trigonometric function is proposed. Then, the proposed loss function is applied in a multi-scale residual convolutional neural network (named MR-CNN-A network) embedded with an attention mechanism for the recognition task. Firstly, a multi-scale convolution kernel is used to obtain multi-scale features. Then, an attention mechanism is used to fuse these multi-scale feature maps. Furthermore, a cosx-function-weighted cross-entropy loss function is used to deal with the class imbalance in underwater acoustic data. This function adjusts the loss ratio of each sample by adjusting the loss interval of every mini-batch based on cosx term to achieve a balanced total loss for each class. Two imbalanced underwater acoustic data sets, ShipsEar and autonomous underwater vehicle (self-collected data) are used to evaluate the proposed network. The experimental results show that the proposed network outperforms the support vector machine and a simple convolutional neural network. Compared with the other three loss functions, the proposed loss function achieves better stability and adaptability. The results strongly demonstrate the validity of the proposed loss function and the network.
Successful Derivation of Absorbing Aerosol Index from the Environmental Trace Gases Monitoring Instrument (EMI)
Authors: Tang Fuying , Wang Weihe , Si Fuqi , Zhou Haijin , Luo Yuhan , Qian Yuanyuan
Published: August 2022

We retrieved the absorbing aerosol index (AAI) based on the measured reflectance from the Environmental Trace Gases Monitoring Instrument (EMI) for the first time. EMI is a push-broom spectrometer onboard the Chinese GeoFen-5 satellite launched on 9 May 2018, which was initially developed to determine the global distribution of atmospheric composition. The EMI initial AAI results were corrected from physical stripes and yielded an offset of 5.92 as calibration errors from a background value based on the statistical method that count the EMI AAI over the Pacific Ocean under cloudless scenes. We also evaluated the consistency of the EMI AAI and data with the TROPOspheric Monitoring Instrument (TROPOMI) observations. A comparison between the monthly average EMI AAI data and TROPOMI AAI revealed regional consistencies between these instruments with a similar spatial distribution of AAI (correlation coefficient, r > 0.9). The daily-scale results demonstrated that EMI was also consistent with TROPOMI AAI (r = 0.9). The spatial distribution of EMI AAI is consistent with Aerosol Optical Depth (AOD) from TROPOMI. The daily variation of EMI AAI in an Australian wildfire event was consistent with TROPOMI (r = 0.92). Overall, we demonstrated that EMI AAI can be efficiently used to detect large aerosol events for reconstructing the spatial variability of Ultraviolet (UV) absorbing aerosols.
Fault Identification in Membrane Structures Using the Hilbert Transforms
Authors: Waszczuk-Młyńska Aleksandra , Gałęzia Adam , Stanisław Radkowski
Published: August 2022

Fault diagnostics present a crucial technical issue in the areas of both the condition monitoring of machines and the monitoring of structural health. The identification of faults at an early stage in their development has an immense effect on the safety of monitored structures. Correct identification allows for the monitoring of the development of faults and the choosing of optimal operation strategies. This article discusses a method of monitoring structural health, based on the application of the Hilbert transforms (GHT and FrHT) for detecting fault formations and their development in membrane structures. A signal resulting from the HT is analyzed using spectral analysis to identify features indicating the technical state.
Paper-Based Enzymatic Electrochemical Sensors for Glucose Determination
Authors: Amor-Gutiérrez Olaya , Costa-Rama Estefanía , Fernández-Abedul M. Teresa
Published: August 2022

The general objective of Analytical Chemistry, nowadays, is to obtain best-quality information in the shortest time to contribute to the resolution of real problems. In this regard, electrochemical biosensors are interesting alternatives to conventional methods thanks to their great characteristics, both those intrinsically analytical (precision, sensitivity, selectivity, etc.) and those more related to productivity (simplicity, low costs, and fast response, among others). For many years, the scientific community has made continuous progress in improving glucose biosensors, being this analyte the most important in the biosensor market, due to the large amount of people who suffer from diabetes mellitus. The sensitivity of the electrochemical techniques combined with the selectivity of the enzymatic methodologies have positioned electrochemical enzymatic sensors as the first option. This review, focusing on the electrochemical determination of glucose using paper-based analytical devices, shows recent approaches in the use of paper as a substrate for low-cost biosensing. General considerations on the principles of enzymatic detection and the design of paper-based analytical devices are given. Finally, the use of paper in enzymatic electrochemical biosensors for glucose detection, including analytical characteristics of the methodologies reported in relevant articles over the last years, is also covered.
Research on the Time Drift Stability of Differential Inductive Displacement Sensors with Frequency Output
Authors: Lu Xiaolong , Tian Guiyun , Wang Zongwen , Li Wentao , Yang Dehua , Li Haoran , Wang You , Ni Jijun , Zhang Yong
Published: August 2022

An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital converter (LDC) demonstrated significant advantages in comparison with conventional sensors with analog-to-digital converter (ADC) interfaces. In order for the differential inductive frequency output displacement (DIFOD) sensor to meet the high-stability requirements of segmented mirror astronomical telescopes, it is important to understand the factors for time drift of the sensor. This paper focuses on the investigation of key factors of sensor structure and material, signal conditioning and interface, and fixtures for time drift to permanently installed applications. First, the measurement principle and probe structural characteristics of the sensor are analyzed. Then, two kinds of signal conditioning and digitalization methods using resonance circuits and LDC chips are implemented and compared. Finally, the time drift stability experiments are performed on the sensors with different signal conditioning methods and fixtures under controlled temperature. Experimental results show that the magnetic shield ring effectively improves the sensitivity and quality factor of the sensors, the time drift stability of the sensor using the signal conditioning based on resonance circuits is better than that of the sensors using LDC chips, and the root mean square (RMS) of the sensor time drift meets the requirement of 0.01 μm/24 h. This study will help further development of high-stability of frequency output sensors and IoT-based systems for scaled-up applications in the future.
Real-Time Regulation of Beam-Based Feedback: Implementing an FPGA Solution for a Continuous Wave Linear Accelerator
Authors: Maalberg Andrei , Kuntzsch Michael , Petlenkov Eduard
Published: August 2022

Control applications targeting fast industrial processes rely on real-time feasible implementations. One of such applications is the stabilization of an electron bunch arrival time in the context of a linear accelerator. In the past, only the electric field accelerating the electron bunches was actively controlled in order to implicitly stabilize the accelerated electron beam. Nowadays, beam properties are specifically measured at a target position and then stabilized by a dedicated feedback loop acting on the accelerating structures. This dedicated loop is usually referred to as a beam-based feedback (BBF). Following this, the control system of the electron linear accelerator for beams with high brilliance and low emittance (ELBE) is planned to be upgraded by the BBF, and the problem of implementing a designed control algorithm becomes highly relevant. In this work, we propose a real-time feasible implementation of a high-order H2 regulator based on a field-programmable gate array (FPGA). By presenting simulation and synthesis results made in hardware description language (HDL) VHDL, we show that the proposed digital solution is fast enough to cover the bunch repetition rates frequently used at ELBE, such as 100 kHz. Finally, we verify the implementation by using a dedicated FPGA testbench.
Relative Humidity Measurement of Air in Low-Temperature Ranges Using Low-Frequency Acoustic Waves and Correlation Signal Processing Techniques
Authors: Guo Miao , Li Yue , Gao Jingmin
Published: August 2022

Air relative humidity (RH) is an important control parameter in many industrial processes. The acoustic method is a novel technique to measure air humidity non-intrusively. Relevant research is limited. Existing methods use ultrasonic waves as a sound source and air humidity is measured by measuring the sound attenuation. In this paper, a novel air humidity measurement system using low-frequency sound waves as a sound source and two acoustic sensors is proposed. Air humidity is acquired by measuring sound speed in the air. Sound speed mainly depends on air temperature, humidity, atmospheric pressure, and air composition. The influence of air temperature, atmospheric pressure, and air constituent concentrations on the RH measurement is analyzed theoretically. A 0.1 s linear chirp signal in the frequency range of 200–500 Hz is selected as the sound source. Sound travel time is calculated by cross-correlating the sound signals received by the two acoustic sensors. To improve the accuracy of the sound speed measurement, sound speed under different RH points is obtained through reference RH experiments and substituted into the calibration equation. Then, equivalent sound path length and systematic delay are estimated using the least squares method. After obtaining these two parameter values, the sound speed measured by the system is closer to the theoretical value at the same RH point. In validation experiments using RH measured by a thermo-hygrometer as a comparison, the relative errors of the acoustically measured RH are within 9.9% in the RH range of 40.7–87.1%, and the standard deviation is within 4.8%.
Development of a Noninvasive Real-Time Ion Energy Distribution Monitoring System Applicable to Collisional Plasma Sheath
Authors: Seong Inho , Kim Sijun , Lee Youngseok , Cho Chulhee , Lee Jangjae , Jeong Wonnyoung , You Yebin , You Shinjae
Published: August 2022

As the importance of ion-assisted surface processing based on low-temperature plasma increases, the monitoring of ion energy impinging into wafer surfaces becomes important. Monitoring methods that are noninvasive, real-time, and comprise ion collision in the sheath have received much research attention. However, in spite of this fact, most research was performed in invasive, not real-time, and collisionless ion sheath conditions. In this paper, we develop a noninvasive real-time IED monitoring system based on an ion trajectory simulation where the Monte Carlo collision method and an electrical model are adopted to describe collisions in sheaths. We technically, theoretically, and experimentally investigate the IED measurement with the proposed method, and compared it with the result of IEDs measured via a quadrupole mass spectrometer under various conditions. The comparison results show that there was no major change in the IEDs as radio-frequency power increased or the IED gradually became broad as gas pressure increased, which was in a good agreement with the results of the mass spectrometer.
Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks
Authors: Turcu Flaviu , Lazar Andrei , Rednic Vasile , Rosca Gabriel , Zamfirescu Ciprian , Puschita Emanuel
Published: August 2022

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values.
Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data
Authors: Zichichi Mirko , Ferretti Stefano , Rodríguez-Doncel Víctor
Published: August 2022

Big Tech companies operating in a data-driven economy offer services that rely on their users’ personal data and usually store this personal information in “data silos” that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, we present a solution that promotes the development of decentralized personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs), Decentralized File Storages (DFS) and smart contracts for storing personal data and managing access control in a decentralized way. Moreover, we focus on the issue of a lack of efficient decentralized mechanisms in DLTs and DFSs for querying a certain type of data. For this reason, we propose the use of a hypercube-structured Distributed Hash Table (DHT) on top of DLTs, organized for efficient processing of multiple keyword-based queries on the ledger data. We test our approach with the implementation of a use case regarding the creation of citizen-generated data based on direct participation and the involvement of a Decentralized Autonomous Organization (DAO). The performance evaluation demonstrates the viability of our approach for decentralized data searches, distributed authorization mechanisms and smart contract exploitation.
A QoS Prediction Approach Based on Truncated Nuclear Norm Low-Rank Tensor Completion
Authors: Xia Hong , Dong Qingyi , Zheng Jiahao , Chen Yanping , Gao Cong , Wang Zhongmin
Published: August 2022

With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services. In the real MEC service invocation environment, due to time and network instability factors, users’ QoS data feedback results are limited. Therefore, effectively predicting the Qos value to provide users with high-quality services has become a key issue. In this paper, we propose a truncated nuclear norm Low-rank Tensor Completion method for the QoS data prediction. This method represents complex multivariate QoS data by constructing tensors. Furthermore, the truncated nuclear norm is introduced in the QoS data tensor completion in order to mine the correlation between QoS data and improve the prediction accuracy. At the same time, the general rate parameter is introduced to control the truncation degree of tensor mode. Finally, the prediction approximate tensor is obtained by the Alternating Direction Multiplier Method iterative optimization algorithm. Numerical experiments are conducted based on the public QoS dataset WS-Dream. The results indicate that our QoS prediction method has better prediction accuracy than other methods under different missing density QoS data.
Integrating Wearable Sensors and Video to Determine Microlocation-Specific Physiologic and Motion Biometrics-Method Development for Competitive Climbing
Authors: Breen Miyuki , Reed Taylor , Breen Hannah M. , Osborne Charles T. , Breen Michael S.
Published: August 2022

Competitive indoor climbing has increased in popularity at the youth, collegiate, and Olympic levels. A critical aspect for improving performance is characterizing the physiologic response to different climbing strategies (e.g., work/rest patterns, pacing) and techniques (e.g., body position and movement) relative to location on climbing wall with spatially varying characteristics (e.g., wall inclinations, position of foot/hand holds). However, this response is not well understood due to the limited capabilities of climbing-specific measurement and assessment tools. In this study, we developed a novel method to examine time-resolved sensor-based measurements of multiple personal biometrics at different microlocations (finely spaced positions; MLs) along a climbing route. For the ML-specific biometric system (MLBS), we integrated continuous data from wearable biometric sensors and smartphone-based video during climbing, with a customized visualization and analysis system to determine three physiologic parameters (heart rate, breathing rate, ventilation rate) and one body movement parameter (hip acceleration), which are automatically time-matched to the corresponding video frame to determine ML-specific biometrics. Key features include: (1) biometric sensors that are seamlessly embedded in the fabric of an athletic compression shirt, and do not interfere with climbing performance, (2) climbing video, and (3) an interactive graphical user interface to rapidly visualize and analyze the time-matched biometrics and climbing video, determine timing sequence between the biometrics at key events, and calculate summary statistics. To demonstrate the capabilities of MLBS, we examined the relationship between changes in ML-specific climbing characteristics and changes in the physiologic parameters. Our study demonstrates the ability of MLBS to determine multiple time-resolved biometrics at different MLs, in support of developing and assessing different climbing strategies and training methods to help improve performance.
A Survey on UAV Computing Platforms: A Hardware Reliability Perspective
Authors: Ahmed Foisal , Jenihhin Maksim
Published: August 2022

This study describes the Computing Platforms (CPs) and the hardware reliability issues of Unmanned Aerial Vehicles (UAVs), or drones, which recently attracted significant attention in mission and safety-critical applications demanding a failure-free operation. While the rapid development of the UAV technologies was recently reviewed by survey reports focusing on the architecture, cost, energy efficiency, communication, and civil application aspects, the computing platforms’ reliability perspective was overlooked. Moreover, due to the rising complexity and diversity of today’s UAV CPs, their reliability is becoming a prominent issue demanding up-to-date solutions tailored to the UAV specifics. The objective of this work is to address this gap, focusing on the hardware reliability aspect. This research studies the UAV CPs deployed for representative applications, specific fault and failure modes, and existing approaches for reliability assessment and enhancement in CPs for failure-free UAV operation. This study indicates how faults and failures occur in the various system layers of UAVs and analyzes open challenges. We advocate a concept of a cross-layer reliability model tailored to UAVs’ onboard intelligence and identify directions for future research in this area.
Automatic Stones Classification through a CNN-Based Approach
Authors: Tropea Mauro , Fedele Giuseppe , De Luca Raffaella , Miriello Domenico , De Rango Floriano
Published: August 2022

This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution.
A Novel Wearable Soft Glove for Hand Rehabilitation and Assistive Grasping
Authors: Zhu Yinlong , Gong Weizhuang , Chu Kaimei , Wang Xu , Hu Zhiqiang , Su Haijun
Published: August 2022

In order to assist patients with finger rehabilitation training and grasping objects, we propose a new type of soft rehabilitation gloves (SRGs), which has both flexion/extension and abduction/adduction movement function for every finger. This paper describes the structure design of the bending actuator and rotating actuator, the fabrication process of the soft actuator, and the implementation of the soft wearable gloves based on a fabric glove. FEM simulation analysis and experiments were conducted to characterize the mechanical behavior and performance of the soft glove in terms of the angle output and force output upon pressurization. To operate this soft wearable glove, we designed the hardware system for SRGs with a flexible strain sensor and force sensor in the loop and introduced a force/position hybrid PID control algorithm to regulate the pressure inputted. Experiment evaluation focused on rehabilitation training gestures; motions and the precise grasping assistance function were executed. The rotating actuator between each finger can supply abduction/adduction motion manner for patients, which will improve rehabilitation effect. The experimental results demonstrated that the developed SRGs have the potential to improve hand movement freedom and the range of grasping successfully.
Directivity Dependence of a Distributed Fiber Optic Hydrophone on Array Structure
Authors: Li Wenmin , Chen Yu , Liang Yan , Lu Yang , Meng Zhou
Published: August 2022

A distributed fiber optic hydrophone (DFOH) is a new type of fiber optic hydrophone (FOH) with adjustable structure. The dependence of the directivity of a DFOH on array structure is theoretically and experimentally studied. The directivity function of a sensing channel and that of a DFOH are derived. Based on the directivity function, the simulations are performed. Finally, the theoretical analysis is demonstrated by the experiments performed on Qingyang lake, and the results reveal that the longer sensing channel length guarantees the lower first-order side lobe and the narrower main lobe. As the channel length increased from 1 to 3, the main lobe width and first-order side lobe height decreased by 4.9° and 6 dB, respectively. In addition, channel spacing is irrelevant to directivity as the spacing is shorter than the wavelength. As the channel spacing increased from 0 to 1, the variations of the main lobe width and first-order side lobe height are lower than 0.5° and 0.94 dB, respectively. This study would provide guidance for the structure design of a distributed fiber optic hydrophone in signal processing.
Significance of Soil Seed Bank in Forest Vegetation—A Review
Authors: V. Anju M. , Warrier Rekha R. , Kunhikannan C.
Published: August 2022

Seed banks present in soils provide information on past/existing standing vegetation and the probable future vegetation of the area. We reviewed 187 articles related to seed banks that were published worldwide from 1859 to 2022 to understand the dynamics of soil seed banks. The heterogeneity and biodiversity of most ecosystems appear vulnerable due to the over-exploitation of soil over the years. The studies on seeds present in soil provide knowledge on species cover, which supports the restoration of degraded areas. An interesting feature observed in most references was that species identified in seed banks do not necessarily represent or reflect the standing vegetation—this is attributed to the varied dispersal mechanisms of different species. The most significant contributions of a seed bank are conservation and the provision of propagules to the ecosystem. These banks are critical for identifying the composition and spatial organisation of understorey plant communities in degraded forests following soil disturbances, such as wildfire, harvesting, and/or logging. Regeneration from soil seed banks enables biodiversity recovery in tropical forests. There has been little understanding of the function soil seed banks play in forest restoration, which is critical to the natural regeneration of forest reserves. The impact of invasive alien species and the associated soil seed banks on vegetation dynamics is poorly researched. With landscape restoration gaining significance in the present decade, it becomes exceedingly important to maintain existing vegetation. It is relevant to India, which has international commitments to restore 26 mha of degraded and deforested land by 2030. This review reveals the importance of soil seed banks and their role in the future maintenance of forest cover.
Examining the Role of Source Evaluation in Athlete Advocacy: How Can Advocate Athletes Inspire Public Involvement in Racial Issues?
Authors: Ogiso Waku , Funahashi Hiroaki , Mano Yoshiyuki
Published: August 2022

Athlete advocacy is recognized as an important method of persuading the public on social issues, and it demonstrates the role of athletes in achieving racial justice. However, how athlete advocacy can gain the persuasiveness to encourage public involvement remains unclear. This study investigates how the evaluation of an advocate athlete functions to encourage public issue involvement, focusing on Naomi Osaka’s racial advocacy. In particular, driven by balance theory and attribution theory, this study examines the effects of five sociopsychological factors on public involvement in racial issues: perceived credibility, hypocrisy, cause fit, effort expended, and role model status of advocate athletes. Data were collected from a cross-sectional online survey of 855 Japanese adults who were aware of Osaka’s advocacy. The findings highlight that public involvement in racial issues is significantly associated with the evaluations of the athlete’s credibility and hypocrisy. These evaluations are further influenced by perceptions of the athlete’s cause fit and role model status. This study enriches the literature on the management of sports for social change by demonstrating the importance of source evaluation in athlete advocacy in achieving advocacy outcomes. Our evidence implies that athletes looking to promote racial justice issues should effectively be seen as credible, knowledgeable, and non-hypocritical in their issue advocacy.
Researching Students’ Experiences of Sexual and Gender-Based Violence and Harassment: Reflections and Recommendations from Surveys of Three UK HEIs
Authors: Bull Anna , Duggan Marian , Livesey Louise
Published: August 2022

In the US, ‘campus climate surveys’ are an established measure of the prevalence of, and students’ awareness of and attitudes to sexual and gender-based violence and harassment (SGBVH). They are regularly carried out by universities to assist SGBVH prevention and responses. Such surveys have only recently started to be carried out within UK higher education institutions (HEIs) and the three authors of this article all independently undertook such surveys in different HEIs. Comparing our experiences of undertaking these surveys across three HEIs allows us to explore similarities and differences in our experiences of this type of research, in particular the challenges which arose in carrying out such research in three very different types of HEI. This article presents reflections on the methodological and political challenges of such work. We discuss our rationales for initiating these projects, the methodological approaches we employed, the governance structures navigated in pursuing the research and the difficulties that arose in conducting and reporting on the research. This article will be of interest to academics, activists, and policy-makers—domestically and internationally—who wish to carry out such research. By comparing approaches, we draw attention to issues and potential impediments of relevance to others wanting to embark on similar work within their own HEI.
Changes in Share Prices of Macrosector Companies on the Warsaw Stock Exchange as a Reaction to the COVID-19 Pandemic
Authors: Bieszk-Stolorz Beata , Markowicz Iwona
Published: August 2022

The crisis caused by the emergence of the COVID-19 pandemic had an impact on the economic situation worldwide, including the stock exchange quotations. The aim of the research is to assess the reaction to crisis situations of share prices of companies listed on the Warsaw Stock Exchange (Poland) belonging to three main macrosectors: Industry, Finance and Services. The main part of the analysis concerns the market reaction to the COVID-19 pandemic during its first wave. The study utilises the survival analysis methods, which allowed for an assessment and comparison of the situation of the three macrosectors. The duration of the decline in share prices and the recovery time were analysed. The intensity and probability of the decline and subsequent increase in share prices were also examined. The Kaplan–Meier estimator, Cox regression model and logit model were used in the study. The pandemic shocks differed significantly from shocks caused by economic crises in the past. We showed that the differences for macro-sector share price declines were statistically insignificant. For price increases, the Finance macrosector differed from the other macrosectors. The probability, intensity and odds of an increase in share prices of companies belonging to this macrosector were lower than for the other macrosectors. In addition, we compared the obtained results with the results of previous studies conducted for the period of the financial crisis in 2008–2009 and the bear market in 2011. We pointed out the differences between the crisis caused by the pandemic and the other crises.
Well-Being in the Time of Corona: Associations of Nearby Greenery with Mental Well-Being during COVID-19 in The Netherlands
Authors: Shentova Ralitsa , de Vries Sjerp , Verboom Jana
Published: August 2022

Nature’s mental health benefits are well-established in the literature, but there is little research on which types and characteristics of urban greenery are most relevant for mental well-being in general, and during the COVID-19 pandemic in particular. This study examined the link between having a (green) garden or a green view from the main window of the home, as well as the perceived quantity and quality of neighbourhood green areas and streetscape greenery, and the self-reported change in mental well-being since the onset of the COVID-19 pandemic. Adults residing in the Netherlands (N = 521, 67% female) completed an online survey in December 2020 and January 2021. It included items on the frequency of contact with the aforementioned outdoor spaces, as well as their quantity, natural features, and quality. Hierarchical regression analyses revealed that the quantity of the greenery mattered, but the quality was more strongly associated with well-being. In particular, well-maintained, attractive, and varied streetscape greenery was just as relevant as a garden with diverse plants. This beneficial association between streetscape greenery and mental well-being was stronger for female participants. Understanding the benefits of the different types and characteristics of urban greenery, and who they are most relevant for, can assist policymakers and planners in designing cities that promote health and resilience.
Evaluation of the Effects of Water and Salinity Stress on the Growth and Biochemistry of Alfalfa (Medicago sativa L.) at the Branching Stage
Authors: Hou Chenli , Li Xianyue , Tian Delong , Xu Bing , Zhang Chen , Ren Jie , Chen Ning
Published: August 2022

The response of alfalfa to water and salinity stress differs during the whole growth period, and water stress has the most severe effects on the yield of alfalfa at the branching stage. However, the presence of soil salt can also enhance its drought resistance and alleviate the impact of water stress on yield. Thus, information on the responses of aboveground biomass, water-use efficiency and osmolytes to water and salinity stress at the branching stages of alfalfa development is urgently required. A pot experiment that combined three irrigation levels of 55–70% (W1), 70–85% (W2) and 85–100% (W3) of field capacity (FC) and four salinity levels was conducted in Dengkou County, Inner Mongolia, China, in 2018 and 2019. The percentage of mixed salt (NaCl:Na2SO4 = 1:1 [w/w]) added for the salinity treatments was 0, 2, 4 and 6% of the soil dry weight and was designated as S0–S3, respectively. The water consumption, biomass, osmolytes, such as proline and Na+, and the activities of antioxidant enzymes, such as superoxide dismutase (SOD) and peroxidase (POD), of alfalfa were measured during its early flowering stage. In general, the plant height, aboveground biomass, root biomass and water consumption of alfalfa increased with the decrease in soil salinity and increase in the amount of irrigation applied. When the salt >3 g kg−1, alfalfa could improve its stress resistance by increasing the contents of proline and Na+ and the activity of POD and decreasing the activity of SOD, but the aboveground biomass and water consumption decreased. However, alfalfa has a certain cross adaptation ability under water and salt stress at the branching stage, particularly when salt is less than 3 g kg−1. Compared with single water stress, adding an appropriate amount of salt (≤3 g kg−1) increased the contents of proline and Na+ and the activities of SOD and POD, which led to water consumption and aboveground biomass of alfalfa increases of 11.93% and 17.51%, respectively. In conclusion, the alfalfa was tolerant to moderate (3 g kg−1) salt stress. The alfalfa with higher proline, SOD and POD activity and Na+ was better able to yield well under salt stress. Meanwhile, combined with moderate irrigation (70–85% FC), the productivity of alfalfa was improved better. The results can provide a theoretical basis for the utilization of alfalfa in salinized land.
Peruvian Native Bacterial Strains as Potential Bioremediation Agents in Hg-Polluted Soils by Artisanal Mining Activities in Southern Peru
Authors: López-Casaperalta Patricia , Febres-Molina Camilo , Aguilar-Pineda Jorge Alberto , Bernabe-Ortiz Julio Cesar , Fernandez-F Fernando
Published: August 2022

Bioremediation of soils and waters due to mercury (Hg) contamination represents one of the most critical environmental challenges. In addition, this challenge is even greater in arid soils due to the low economic interest in these regions. Such is the case of the Secocha Annex, located in the Arequipa province of Camaná in Southern Peru. In this region, the excessive use of Hg in artisanal and small-scale gold mining (ASGM) activities has seriously endangered the health of its inhabitants. Unfortunately, there are few projects aimed at improving the environmental and sanitary conditions of this locality. An alternative to conventional cleaning technology is the use of native microorganisms that allow the recovery of ecological environments with low-cost and low-tech techniques. This work aims to present two new Hg-resistant bacterial strains obtained from Hg-polluted soils of the Secocha Annex as potential bioremediation agents. Both strains showed growth capacity on Hg substrates and their adsorption behaviors and Hg removal capacities were evaluated. By deoxyribonucleic acid (DNA) analysis, both Gram-positive strains were identified as Kocuria sp. (99.35% similarity) and Zhihengliuella sp. (99.78% similarity). Spectrometry results showed elimination capacities with values close to 28.4 and 33.3 % in an incubation time period of 45 days, with the maximum elimination efficiency in the first 24 h. These results are encouraging and offer new possibilities for the use of native strains in the bioremediation of arid soils contaminated with Hg in the Secocha Annex. Furthermore, due to the low cost and minimization of negative impacts, this technique and our bacterial strains could be used in other regions of Peru.
Changes in Consumer Behavior during the COVID-19 Pandemic: A Comparative Analysis between Polish and Turkish Consumers
Authors: Bareja-Wawryszuk Ola , Pajewski Tomasz , Çakaröz Kübra Müge , Kavas Baki
Published: August 2022

Undoubtedly, the COVID-19 pandemic has left an indelible mark on every aspect of human life, and, perhaps most significantly, on the operation of the world’s economy. Restrictions and limitations on freedom of movement had a clear impact on the ways in which companies and households functioned, and consumer behaviors and purchasing habits were demonstrably influenced in turn. Thus, the aim of this article is to illustrate these changes in light of the pandemic and, moreover, to highlight the related challenges pertaining to purchasing practices and consumer concerns. The study sample focused on students from Poland and Turkey, with data gathered using an online survey method. The resulting data were subsequently analyzed with the SPSS 26 program. The application of Pearson Chi-Square methods, as well as the analysis of descriptive statistics such as percentage and frequency, revealed significant differences between the two countries. As a result of the comparative analysis, clear differences were demonstrated in the nature of changes in consumer behavior between respondents from Turkey and Poland, most strikingly, the fact that the purchasing habits of respondents in Turkey have changed to a greater extent. It has been observed that limitations imposed during the COVID-19 pandemic had a greater impact on consumer behavior, particularly for students in Turkey.
A Refined Evaluation Analysis of Global Healthcare Accessibility Based on the Healthcare Accessibility Index Model and Coupling Coordination Degree Model
Authors: Sun Zhenyu , Sun Ying , Liu Xueyi , Tu Yixue , Chen Shaofan , Qian Dongfu
Published: August 2022

Healthcare accessibility (HCA) is directly related to the general well-being of citizens, and the HCA index model is widely used in HCA evaluation. However, the evaluation results of the HCA index model are rough and potentially misleading because it cannot measure the coordination of a country’s performance across the various evaluation dimensions. This study aimed to introduce a coupling coordination degree model to remedy this defect of the HCA index model, conduct a more meticulous evaluation for the global HCA development over the past two decades, present a panorama of global HCA current status, and further contribute precise strategies to enhance global HCA development. Combining the HCA index model and the coupling coordination model, we used the Global Health Observatory (GHO) data to evaluate the development levels of HCA in 186 countries across the world from 2000 to 2018. The results showed that, first, global HCA development has shown a slow upward trend over the past two decades. Second, of the selected 60 representative countries in 2018, the HCA in 86.7% of high-income countries belonged to the optimum development type, the HCA in 73.4% of upper-middle-income countries was in the antagonism-coordination stage of the transition development category, and the HCA in 66.7% of low-income and lower-middle-income countries (LMICs) was in the inferior and worst development forms. Third, the Spearman’s correlation coefficient between HCA index and HCA coupling coordination degree was 0.787 (p < 0.001). The above results indicate that the dilemma of HCA development in LMICs lies not only in the poor healthcare resources, but also in weak ability to allocate healthcare resources effectively. As the lack of healthcare resources cannot be alleviated in a short time, LMICs ought to prioritize effective healthcare resources allocation, such as developing new basic healthcare kits adapted to low-resource setting.
Is COVID-19 Herd Immunity Influenced by Population Densities of Cities?
Authors: Arbel Yuval , Arbel Yifat , Kerner Amichai , Kerner Miryam
Published: August 2022

The objective of the current study is to compare between densely and sparsely populated cities in the context of herd immunity against the SARS-CoV-2 virus. The sample refers to 46 (45) densely populated (sparsely populated) Israeli cities and towns, whose population density is below (above) the median of 2388 personssq.km, covering above 64.3% of the entire Israeli population. Findings suggest, on the one hand, a higher projected scope of morbidity per 10,000 persons in sparsely populated cities with zero prevalence of vaccination (37.79 vs. 17.61 cases per 10,000 persons). On the other hand, the outcomes propose a steeper drop in the scope of COVID-19 morbidity with higher vaccination rates in sparsely populated cities. Findings suggest that in terms of vaccination campaigns, below 60–70 percent vaccination rates, more efforts should be invested in sparsely populated cities. If, however, the 70 percent threshold is achieved, a further reduction in the scope of morbidity would require a higher (lower) rate of vaccination in densely populated (sparsely populated) cities.