bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Activation Modes on the Property Characterization of Crumb Rubber Powder from Waste Tires and Performance Analysis of Activated Rubber-Modified Asphalt Binder
Authors: Zhang Honggang , Zhang Yangpeng , Chen Jie , Liu Wenchang , Wang Wensheng
Published: June 2022

The rubber molecular chain in waste vulcanized tire rubber will be crosslinked to form a network structure that would be difficult to degrade in asphalt. Crumb rubber treated by desulfurization activation could form active groups on the surface by interrupting the crosslinking bond to improve the compatibility between crumb rubber powder and asphalt. To explore the influence of activation modes on crumb rubber powder and the corresponding rubber-modified asphalt binder, crumb rubber powder was firstly activated through three commonly used activation methods and asphalt binder samples modified by activated crumb rubber powder were also prepared. The basic properties of activated crumb rubber powder were characterized by infrared spectroscopy, and conventional tests were used to study the conventional physical properties of the asphalt binder. The infrared spectroscopy and elemental analysis showed that the crumb rubber powder was mainly composed of alkanes, alkenes, sulfonic acids, aromatics, and a little silica rubber and antioxidant zinc oxide, which is suitable for asphalt modification. The simple heat activation treatment method is not enough to greatly destroy the cross-linking structure of crumb rubber powder, but the “C=C” bond was destroyed more seriously. Under the action of adjuvants, the polysulfide cross-linking bond could be broken in crumb rubber powder. The heat treatment and chemical treatment could not achieve the purpose of reducing the viscosity and improving the compatibility of rubber asphalt binder through desulfurization activation. The mechanochemical treatment would help to improve the performance of crumb-rubber-powder-modified asphalt binder. The data correlation analysis based on the grey relational degree can provide a reference for the selection of activated crumb rubber powder for different application requirements in the asphalt modification procedure.
Hydrophobic Recovery of Plasma-Hydrophilized Polyethylene Terephthalate Polymers
Authors: Primc Gregor , Mozetič Miran
Published: June 2022

Oxygen plasma is widely used for enhancing the wettability of numerous polymers, including polyethylene terephthalate (PET). The treatment with plasma containing oxygen will cause surface functionalization with polar functional groups, which will, in turn, improve the wettability. However, the exact mechanisms leading to the hydrophilic or even super-hydrophilic surface finish are still insufficiently explored. The wettability obtained by plasma treatment is not permanent, since the hydrophobic recovery is usually reported. The mechanisms of hydrophobic recovery are reviewed and explained. Methods for suppressing this effect are disclosed and explained. The recommended treatment which assures stable hydrophilicity of PET samples is the treatment with energetic ions and/or vacuum ultraviolet radiation (VUV). The influence of various plasma species on the formation of the highly hydrophilic surface finish and stability of adequate wettability of PET materials is discussed.
Performance-Based Evaluation of Healing Efficiency on Mechanical Properties of Self-Healing Cementitious Materials Incorporated with PMMA/Epoxy Microcapsule
Authors: Ren Jun , Liu Birunxuan , Li Hao , Zhang Ji , Zhu Haiyan , Xiao Meilin , Liu Guojian , Luo Shuqiong
Published: June 2022

In this study, based upon the investigation of its effect on workability and the mechanical property of cementitious materials, the Box–Behnken design was adopted to establish models describing self-healing performance on mechanical properties of cementitious materials with polymethylmethacrylate (PMMA)/epoxy microcapsule in terms of healing rate of peak strength (Y1), the recovery rate of peak strength (Y2), the healing rate of Young’s modulus (Y3), the recovery rate of Young’s modulus (Y4), the healing rate of peak strain (Y5), and recovery rate of peak strain (Y6). This was performed under the influence of the four factors, including microcapsule size (X1), microcapsule content (X2), pre-loading (X3), and curing age (X4). The results showed the four factors significantly affect the healing rate and recovery rate of the peak strength, Young’s modulus, and peak strain, except the healing rate on peak strain. Moreover, the interaction between the factors showed some influence as well. The numerically optimised values of X1, X2, X3, and X4 are 203 nm, 5.59%, 43.56%, and 21 days, respectively, and the self-healing cementitious materials with desirable mechanical characteristics (Y1 63.67%, Y2 145.22%, Y3 40.34%, Y4 132.22%, Y5 27.66%, and Y6 133.84%) with the highest desirability of 0.9050 were obtained. Moreover, the porosity of the specimen confirmed the healing performance of PMMA/epoxy microcapsules in cementitious materials.
Functionalized Electrospun Scaffold–Human-Muscle-Derived Stem Cell Construct Promotes In Vivo Neocartilage Formation
Authors: Jankauskaite Lina , Malinauskas Mantas , Aukstikalne Lauryna , Dabasinskaite Lauryna , Rimkunas Augustinas , Mickevicius Tomas , Pockevičius Alius , Krugly Edvinas , Martuzevicius Dainius , Ciuzas Darius , Baniukaitiene Odeta , Usas Arvydas
Published: June 2022

Polycaprolactone (PCL) is a non-cytotoxic, completely biodegradable biomaterial, ideal for cartilage tissue engineering. Despite drawbacks such as low hydrophilicity and lack of functional groups necessary for incorporating growth factors, it provides a proper environment for different cells, including stem cells. In our study, we aimed to improve properties of scaffolds for better cell adherence and cartilage regeneration. Thus, electrospun PCL–scaffolds were functionalized with ozone and loaded with TGF-β3. Together, human-muscle-derived stem cells (hMDSCs) were isolated and assessed for their phenotype and potential to differentiate into specific lineages. Then, hMDSCs were seeded on ozonated (O) and non-ozonated (“naïve” (NO)) scaffolds with or without protein and submitted for in vitro and in vivo experiments. In vitro studies showed that hMDSC and control cells (human chondrocyte) could be tracked for at least 14 days. We observed better proliferation of hMDSCs in O scaffolds compared to NO scaffolds from day 7 to day 28. Protein analysis revealed slightly higher expression of type II collagen (Coll2) on O scaffolds compared to NO on days 21 and 28. We detected more pronounced formation of glycosaminoglycans in the O scaffolds containing TGF-β3 and hMDSC compared to NO and scaffolds without TGF-β3 in in vivo animal experiments. Coll2-positive extracellular matrix was observed within O and NO scaffolds containing TGF-β3 and hMDSC for up to 8 weeks after implantation. These findings suggest that ozone-treated, TGF-β3-loaded scaffold with hMDSC is a promising tool in neocartilage formation.
Application of Mixed Potential Theory to Leaching of Mineral Phases
Authors: Sequeira César A. C.
Published: June 2022

Leaching is a central unit operation in the hydrometallurgical processing of minerals, which often occurs by means of electrochemical reactions. Application of mixed potential theory to explain the kinetics of oxidative and reductive leaching processes is a useful concept in explaining observed results. Native metals, selected oxides, and most base metal sulfides are electron-conducting phases. For these minerals, leaching may take place by normal corrosion, passivation or galvanic couple mechanisms, which provide individual electrode kinetics enabling the calculation of mixed potentials and overall reaction kinetics. Examples of the electrochemical nature of selected leaching processes are presented and include the effect of mixed potentials, geometry, and associated kinetic reactions.
Introduction: The Charisma of Liturgy in the Middle Ages
Authors: Aurell Jaume
Published: June 2022

This collective volume is devoted to the various manifestations of liturgy in the Middle Ages, based on a great variety and wealth of primary sources [...]
Dynamic Antenna Selection for Colocated MIMO Radar
Authors: Zhang Gangsheng , Xie Junwei , Zhang Haowei , Li Zhengjie , Qi Cheng
Published: June 2022

Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramér–Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.
Efficient Superpixel Generation for Polarimetric SAR Images with Cross-Iteration and Hexagonal Initialization
Authors: Li Meilin , Zou Huanxin , Qin Xianxiang , Dong Zhen , Sun Li , Wei Juan
Published: June 2022

Clustering-based methods of polarimetric synthetic aperture radar (PolSAR) image superpixel generation are popular due to their feasibility and parameter controllability. However, these methods pay more attention to improving boundary adherence and are usually time-consuming to generate satisfactory superpixels. To address this issue, a novel cross-iteration strategy is proposed to integrate various advantages of different distances with higher computational efficiency for the first time. Therefore, the revised Wishart distance (RWD), which has better boundary adherence but is time-consuming, is first integrated with the geodesic distance (GD), which has higher efficiency and more regular shape, to form a comprehensive similarity measure via the cross-iteration strategy. This similarity measure is then utilized alternately in the local clustering process according to the difference between two consecutive ratios of the current number of unstable pixels to the total number of unstable pixels, to achieve a lower computational burden and competitive accuracy for superpixel generation. Furthermore, hexagonal initialization is adopted to further reduce the complexity of searching pixels for relabelling in the local regions. Extensive experiments conducted on the AIRSAR, RADARSAT-2 and simulated data sets demonstrate that the proposed method exhibits higher computational efficiency and a more regular shape, resulting in a smooth representation of land cover in homogeneous regions and better-preserved details in heterogeneous regions.
Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview
Authors: Vaudour Emmanuelle , Gholizadeh Asa , Castaldi Fabio , Saberioon Mohammadmehdi , Borůvka Luboš , Urbina-Salazar Diego , Fouad Youssef , Arrouays Dominique , Richer-de-Forges Anne C. , Biney James , Wetterlind Johanna , Van Wesemael Bas
Published: June 2022

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.
The Outburst of a Lake and Its Impacts on Redistribution of Surface Water Bodies in High-Altitude Permafrost Region
Authors: Ding Zekun , Niu Fujun , Li Guoyu , Mu Yanhu , Chai Mingtang , He Pengfei
Published: June 2022

The lakes distributed in permafrost areas on the Tibetan Plateau (TP) have been experiencing significant changes during the past few decades as a result of the climate warming and regional wetting. In September 2011, an outburst occurred on an endorheic lake (Zonag Lake) in the interior of the TP, which caused the spatial expansion of three downstream lakes (Kusai Lake, Haidingnor Lake and Salt Lake) and modified the four independent lake catchments to one basin. In this study, we investigate the changes in surficial areas and water volumes of the outburst lake and related downstream water bodies 10 years after the outburst. Based on the meteorological and satellite data, the reasons for the expansion of downstream lakes were analyzed. Additionally, the importance of the permafrost layer in determining hydrological process on the TP and the influence of from lake expansion on engineering infrastructures were discussed. The results in this study showed the downstream lakes increased both in area and volume after the outburst of the headwater. Meanwhile, we hope to provide a reference about surface water changes and permafrost degradation for the management of lake overflow and flood on the TP in the background of climate warming and wetting.
A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain
Authors: Pan Yuetao , Liu Danfeng , Wang Liguo , Benediktsson Jón Atli , Xing Shishuai
Published: June 2022

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution.
A Real-Time Digital Self Interference Cancellation Method for In-Band Full-Duplex Underwater Acoustic Communication Based on Improved VSS-LMS Algorithm
Authors: Lu Yinheng , Qiao Gang , Yang Chenlu , Zhao Yunjiang , Yang Guang , Li Huizhe
Published: June 2022

Theoretically, the spectral efficiency of in-band full-duplex underwater acoustic communications (IBFD-UWAC) is twice that of a half-duplex one. However, the actual achievable spectral efficiency of IBFD-UWAC is determined by the performance of the self-interference cancellation (SIC). In addition, the hostile underwater environment poses a challenge to the tracking performance of the SIC due to its complexity and variability. In this paper, we propose a digital SIC method based on the improved variable step-size least mean square (IVSS-LMS) algorithm where we modify the step-size adjustment criterion in the classical LMS filter and establishes a nonlinear relationship with the Sigmoid function to control the step-size using the instantaneous state error, thus improving the robustness and tracking performance of IVSS-LMS. Hardware-in-loop simulation (HLS) based on Simulink® platform verifies the real-time implementability and effectiveness of the proposed IVSS-LMS algorithm. Furthermore, the sea trial results show that the digital SIC method based on the proposed algorithm can be implemented in real-time and the convergence speed, and steady-state performance are significantly improved.
Attention-Unet-Based Near-Real-Time Precipitation Estimation from Fengyun-4A Satellite Imageries
Authors: Gao Yanbo , Guan Jiping , Zhang Fuhan , Wang Xiaodong , Long Zhiyong
Published: June 2022

Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the effectiveness of the proposed model, we compare it with operational near-real-time satellite-based precipitation products and deep learning models which proved to be effective in precipitation estimation. We use classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR), Critical success index (CSI), and regression metrics including Root Mean Square Error (RMSE) and Pearson correlation coefficient (CC) to evaluate the performance of precipitation identification and precipitation amounts estimation, respectively. Furthermore, we select an extreme precipitation event to validate the generalization ability of our proposed model. Statistics and visualizations of the experimental results show the proposed model has better performance than operational precipitation products and baseline deep learning models in both precipitation identification and precipitation amounts estimation. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. This study suggests that applying an appropriate deep learning algorithm may provide an opportunity to improve the quality of satellite-based precipitation products.
Smartphone-Based Unconstrained Step Detection Fusing a Variable Sliding Window and an Adaptive Threshold
Authors: Xu Ying , Li Guofeng , Li Zeyu , Yu Hao , Cui Jianhui , Wang Jin , Chen Yu
Published: June 2022

Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconstrained step detection method fusing a variable sliding window and an adaptive threshold is proposed. In this method, the dynamic updating algorithm of a peak threshold is developed, and the minimum peak value filtered after a sliding window filter is used as the adaptive peak threshold, which solves the problem that the peak threshold of different motion states is difficult to update adaptively. Then, a variable sliding window collaborative time threshold method is proposed, which solves the problem that the adjacent windows cannot be contacted, and the initial peak and the end peak are difficult to accurately identify. To evaluate the performance of the proposed unconstrained step detection algorithm, 50 experiments in constrained and unconstrained states are conducted by 25 volunteers holding 21 different types of smartphones. Experimental results show: The average step counting accuracy of the proposed unconstrained step detection algorithm is over 98%. Compared with the open source program Stepcount, the average step counting accuracy of the proposed algorithm is improved by 10.0%. The smartphone-based unconstrained step detection fusing a variable sliding window and an adaptive threshold has a strong ability to adapt to complex unconstrained states, and the average step counting accuracy rate is only 0.6% lower than that of constrained states. This algorithm has a wide audience and is friendly for different genders and smartphones with different prices.
UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques
Authors: Impollonia Giorgio , Croci Michele , Ferrarini Andrea , Brook Jason , Martani Enrico , Blandinières Henri , Marcone Andrea , Awty-Carroll Danny , Ashman Chris , Kam Jason , Kiesel Andreas , Trindade Luisa M. , Boschetti Mirco , Clifton-Brown John , Amaducci Stefano
Published: June 2022

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using 15 VIs time series, and predicted yield using peak descriptors derived from these VIs time series with root mean square error of 2.3 Mg DM ha−1. The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production.
Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars
Authors: Atmani Farid , Bookhagen Bodo , Smith Taylor
Published: June 2022

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
Wide and Deep Fourier Neural Network for Hyperspectral Remote Sensing Image Classification
Authors: Xi Jiangbo , Ersoy Okan K. , Cong Ming , Zhao Chaoying , Qu Wei , Wu Tianjun
Published: June 2022

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose a wide and deep Fourier network to learn features efficiently by using pruned features extracted in the frequency domain. It is composed of multiple wide Fourier layers to extract hierarchical features layer-by-layer efficiently. Each wide Fourier layer includes a large number of Fourier transforms to extract features in the frequency domain from a local spatial area using sliding windows with given strides.These extracted features are pruned to retain important features and reduce computations. The weights in the final fully connected layers are computed using least squares. The transform amplitudes are used for nonlinear processing with pruned features. The proposed method was evaluated with HSI datasets including Pavia University, KSC, and Salinas datasets. The overall accuracies (OAs) of the proposed method can reach 99.77%, 99.97%, and 99.95%, respectively. The average accuracies (AAs) can achieve 99.55%, 99.95%, and 99.95%, respectively. The Kappa coefficients are as high as 99.69%, 99.96%, and 99.94%, respectively. The experimental results show that the proposed method achieved excellent performance among other compared methods. The proposed method can be used for applications including classification, and image segmentation tasks, and has the ability to be implemented with lightweight embedded computing platforms. The future work is to improve the method to make it available for use in applications including object detection, time serial data prediction, and fast implementation.
Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment
Authors: Bai Heming , Shi Yuli , Seong Myeongsu , Gao Wenkang , Li Yuanhui
Published: June 2022

Satellite-based PM2.5 estimation has been widely used to assess health impact associated with PM2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect.
Evaluation and Hydrological Application of Four Gridded Precipitation Datasets over a Large Southeastern Tibetan Plateau Basin
Authors: Zhang Yueguan , Ju Qin , Zhang Leilei , Xu Chong-Yu , Lai Xide
Published: June 2022

Reliable precipitation is crucial for hydrological studies over Tibetan Plateau (TP) basins with sparsely distributed rainfall gauges. In this study, four widely used precipitation products, including the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE), the High Asia Reanalysis (HAR), and the satellite-based precipitation estimates from Global Precipitation Measurement (GPM) and Tropical Rainfall Measurement Mission (TRMM), were comprehensively evaluated by combining statistical analysis and hydrological simulation over the Upper Brahmaputra (UB) River Basin of TP during 2001–2013. In respect to the statistical assessment, the overall performances of GPM and HAR are comparable to each other, and both are superior to the other two datasets. For hydrological assessment, both daily and monthly GPM-based streamflow simulations perform the best not only at the UB outlet with very good results, but they also illustrate satisfactory results at Yangcun and Lhasa hydrological stations within the UB. Runoff simulation using HAR only performs well at the UB outlet, whereas it shows poor results at both Yangcun and Lhasa stations. The simulated results based on APHRODITE and TRMM show poor performances at UB. Generally, the GPM shows an encouraging potential for hydro-meteorological investigation over UB, although with some bias in flood simulation.
When Helping Hurts: A Zemiological Analysis of a Child Protection Intervention in Adolescence—Implications for a Critical Child Protection Studies
Authors: Wroe Lauren Elizabeth
Published: June 2022

This paper presents data from a three-year, mixed methods study into the rate and impact of ‘relocation’ as a response to extra-familial harm in adolescence by children’s social care teams. Participatory approaches to research design, data collection and analysis are used to gain insights from young people, parents/carers and professionals about the impact of relocations on safety. Professionals and young people report a range of harms implicated in the use of relocations, whilst sharing that the intervention often increases safety. Data are analysed zemiologically to understand this ambivalence, connecting micro accounts of harm with meso, institutional and macro structures that determine child protection intervention. Zemiology is put forward as a promising approach for a Critical Child Protection Studies.
Young Women, Employment and Precarity: The Face of Two Periods of Crisis in Spain (2008–2021)
Authors: Cabasés M. Àngels , Úbeda Miquel
Published: June 2022

Youth employment in Spain is characterised by temporary contracts, part-time jobs, and low wages, a long-standing situation that has been further accentuated since the 2008 crisis, placing young people, especially women, in a position of vulnerability at the onset of the COVID-19 pandemic. Through different data, this article argues that young women’s working conditions have deteriorated in comparison to those of previous generations and young men, in a period in which there have been two crises that have affected youth employment. Linking the results with the main youth employment policies allows us to observe why the precarisation of Spanish youth has not been stopped.
An Innovative Technique for Energy Assessment of a Highly Efficient Photovoltaic Module
Authors: Spertino Filippo , Malgaroli Gabriele , Amato Angela , Qureshi Muhammad , Ciocia Alessandro , Siddiqi Hafsa
Published: June 2022

For a photovoltaic (PV) generator, knowledge of the parameters describing its equivalent circuit is fundamental to deeply study and simulate its operation in any weather conditions. In the literature, many papers propose methods to determine these parameters starting from experiments. In the most common circuit, there are five of these parameters, and they generally refer to specific weather conditions. Moreover, the dependence on irradiance and temperature is not investigated for the entire set of parameters. In fact, a few papers present some equations describing the dependence of each parameter on weather conditions, but some of their coefficients are unknown. As a consequence, this information cannot be used to predict the PV energy in any individual weather condition. This work proposes an innovative technique to assess the generated energy by PV modules starting from the knowledge of their equivalent parameters. The model is applied to a highly efficient PV generator with all-back contact, monocrystalline silicon technology, and rated power of 370 W. The effectiveness of the model is investigated by comparing its energy prediction with the value estimated by the most common model in the literature to assess PV energy. Generated energy is predicted by assuming PV power to be constant for a time interval of 1 min.
Compressing the Channels in the Crystal Structure of Copper Squarate Metal-Organic Framework
Authors: Colmenero Francisco , Lobato Álvaro , Timón Vicente
Published: June 2022

The crystal structure of a copper squarate metal-organic framework is fully determined using first principles methods based in density functional theory. The compressibility of this material is studied by optimizing the structure under different isotropic pressures and uniaxial stresses directed along the direction of minimum compressibility, [1 0 0]. Under isotropic compression, channels become wider along [1 0 0], leading to negative linear compressibility, NLC. Under compression along [1 0 0], the unit-cell volume increases leading to negative volumetric compressibility.
Impact of Citizens’ Personal Values, Knowledge, Awareness, Informing, Advertising, and Truth of Environmental and Climate Challenges in Support of the Sustainable Development Goals
Authors: Krsnik Sabina , Erjavec Karmen , Klopčič Marija
Published: June 2022

In order to raise citizens’ awareness of the Sustainable Development Goals (SDGs) and to develop an effective communication campaign on the SDGs, it is crucial to know citizens’ values, their awareness and knowledge of the SDGs, as well as how they receive information on sustainability. As no previous study has addressed this issue, this study aimed to fill a research gap and find out how demographic variables, personal values, awareness of sustainability challenges, information, and advertising influence citizens’ support for the SDGs. An online survey was conducted with representative Slovenian citizens (N = 502). The results showed that Slovenian citizens know little about sustainability issues and the SDGs. The greater the personal values, awareness of sustainability issues, higher education, efficient information, and trust in advertising, the greater the public support for the SDGs. To gain citizen support for the SDGs, it is important to plan a systematic communication campaign and government-led communication about the SDGs in the media. This includes information on sustainability issues, presentation of the environmental friendliness of a product/service, potential positive actions, and potential negative consequences.
The Impact of a Multilevel Innovation Network and Government Support on Innovation Performance—An Empirical Study of the Chengdu–Chongqing City Cluster
Authors: Sun Mingbo , Zhang Xueqing , Zhang Xiaoxiao
Published: June 2022

A national strategy has been deployed for the development of China’s western region. The Chengdu–Chongqing city cluster is an important platform for collaborative regional innovation. In this empirical study, we constructed multilevel innovation networks for each of the 16 cities in the Chengdu–Chongqing city cluster and the inter-city networks for these 16 cities based on a panel of data on applications for invention patents by industry–university–research collaborators in the city cluster. We used social network analysis and a negative binomial regression model with fixed effects to examine the impact of the multilevel innovation networks on the innovation performance of the 16 cities. The moderating effect of government support was also analyzed. The results show that the average weighted degree of the intra-city innovation network has significant positive effects on the innovation performance of the 16 cities. For the inter-city innovation networks, the network density and cooperation intensity have significant positive effects on the innovation performance of the 16 cities. Regarding the moderating effect, our results show that a high level of government support enhances the positive effects of the average weighted degree of the intra-city innovation networks and the network density of the inter-city innovation networks on the innovation performance.
Green Innovation’s Promoting Impact on the Fusion of Industry and Talent: The Case of Pharmaceutical Industry in the Yangtze River Economic Belt of China
Authors: Hu Qi , Wu Fang , Qu Yingna , Guo Ke , Du Xinyi
Published: June 2022

This study aims to explore the promoting impact of green innovation on the fusion of industry and talent (FIT). The primary objectives of the study also include showing how FIT affects the Yangtze River Economic Belt of China and evaluating the development status of three subsystems: the pharmaceutical industry, talent support, and green innovation. In this study, an index system comprising 28 indicators is established to characterize the three subsystems, based on which a comprehensive evaluation model is used to assess the development of each subsystem. A fusion model is used to explore the current status of FIT and the role that green innovation plays in this, based on panel data obtained for 11 provinces and cities in the Yangtze River Economic Belt from 2010 to 2019. The results suggest that: (1) the three subsystems in the Belt have all maintained growth, though the development score for the pharmaceutical industry fluctuated greatly and has been somewhat unstable, while growth trends for talent support and green innovation have been stable; (2) the extent of FIT is low, with nearly half of the provinces and cities lacking organization, with a typical spatial pattern of higher levels in the downstream region and lower levels in the upstream region. The downstream region has obvious advantages in the degree of FIT, while the upstream region has a more optimistic growth trend; and (3) green innovation stimulated the development of FIT in the Belt, with a “strong and stronger” trend depending on the foundation of FIT. To promote FIT, the government should (1) focus on enhancing the development and efficiency of green innovation to help promote FIT; (2) promote the stable and sustainable growth of the pharmaceutical industry as well as talent’s support to consolidate the foundation of fusion; and (3) implement regional coordinated development and interaction policies to narrow the regional gap.
Economic Policy Uncertainty, Social Financing Scale and Local Fiscal Sustainability: Evidence from Local Governments in China
Authors: Xia Yuanting , Hu Wenxiu , Su Zhenxing
Published: June 2022

The motivation for this paper is to investigate a previously unexplored exogenous shock to fiscal sustainability from economic policy uncertainty. To control for the impact of local government institutional and cultural factors on fiscal sustainability, we select data from 30 Chinese provinces for the 2012–2020 period for empirical analysis. We find that economic policy uncertainty shocks have a robust negative effect on fiscal sustainability after controlling for endogeneity. Economic policy uncertainty has an impact on fiscal sustainability mainly through the mediating variable of the social financing scale. To cope with the impact of uncertain economic policies, corporations reduce the scale of investment and financing, and residents have defensive savings motivations, reducing consumption and increasing savings. Therefore, economic policy uncertainty can lead to a reduction in the social financing scale. A reduction in the social financing scale can reduce government revenue and increase the debt scale. Ultimately, this leads to a decrease in the fiscal sustainability of local governments. This paper can provide a theoretical reference for reducing the negative impact of government policy adjustments and improving fiscal sustainability.
Reinterpreting Spatial Planning Cultures to Define Local Adaptation Cultures: A Methodology from the Central Veneto Region Case
Authors: Litt Giovanni , Bertin Mattia , Negretto Vittore , Musco Francesco
Published: June 2022

This paper focuses on recognising the underlying component of climate risk adaptation and management that is present at the local planning level. Starting from a comparative analysis of four Italian cities in the Central Veneto Area, the aim is to understand how plans and regulations have already directed their efforts toward adaptation and climate risk reduction over the years, without explicitly labelling these measures as such. This process is carried out by co-ordinating the technicians of local administrations in the recognition and classification of already active measures that can be brought within the framework of combating the effects of climate change. The analysis of the identified measures shows that there is already considerable attention to flooding-related and heat-related issues in the local planning corpus. Understanding this dimension of local planning allows access to a set of adaptation intervention models that are already integrated into the planning system and support incorporating adaptation practices in a more co-ordinated way at various planning levels.
Entrepreneurial Intention of Students (Managers in Training): Personal and Family Characteristics
Authors: Dragin Aleksandra , Mijatov Maja , Munitlak Ivanović Olja , Jovičić Vuković Ana , Ivkov Džigurski Anđelija , Košić Kristina , Nedeljković Knežević Milena , Tomić Slavica , Stankov Uglješa , Vujičić Miroslav , Stojanović Vladimir , Ivanović Bibić Ljubica , Đerčan Bojan , Stoiljković Aleksandra
Published: June 2022

To increase the entrepreneurial intention, it is necessary to understand the main drivers that are driving students, as carriers of future economic development, towards starting a personal business venture. With regards to that, the main aim of this research was to analyze the entrepreneurial intention among students (managers in training) in the field of tourism and hospitality management. Particular attention was paid to the influence of their socio-demographic characteristics, as well as their parents on entrepreneurial intention. The research was conducted in survey form on a sample of 310 students in Serbia in 2020. The main findings indicated that entrepreneurial intention is higher among the male students, as well as among those students whose parents are entrepreneurs or whose fathers are retired. Besides theoretical contribution, the research results might contribute to scientific and research organizations and institutions in providing the guidelines for forming study programs that will raise the students’ entrepreneurial intention.
A Dynamic Perspective on the Gender Diversity–Firms’ Environmental Performances Nexus: Evidence from the Energy Industry
Authors: Sraieb Mohamed M. , Labadze Lasha
Published: June 2022

We explore the role that a country’s economic and political uncertainty plays in shaping its environmental performance. We put emphasis on the role played by gender diversity in the board of firms, and we address two limitations characterizing the literature on the topic: (i) the use of static modelling that prevents identifying static and dynamic endogeneity and (ii) the assumption that the relationship is linear, which prevents accounting for the factors that affect the magnitude and the shape of this nexus. Using a System-GMM approach, we find evidence that gender diversity is associated positively with firms’ environmental results. Furthermore, the intensity of this relationship is increasing in gender diversity, and more importantly, the effect tends to be greater in less uncertain countries. These findings are of first importance in terms of the policy. Improving environmental quality can be achieved cost-effectively through the promotion of gender diversity, along with building/strengthening institutions to mitigate the effects of economic and political uncertainty. The benefits of these actions can support an effective implementation of the UN SDGs related to gender equality (Goal 5) and several environment-related SDGs (Goal 13 and Goal 15).
A Computation Method for the Typhoon Waves Using the Field Wave Spectrum
Authors: Liu Can , Dong Zhiyong , Zhou Yang , Pan Yun
Published: June 2022

Wave features and propagation characteristics during typhoons are the key factors to study the dynamic response of ocean engineering and coastal disaster prevention and mitigation under extreme climate. Based on the Longuet-Higgins theory, the method of the field spectrum during the typhoon was used to compute the typhoon waves. And the directional spectrum, the formulas of wave surface, the velocity of water particles, and the acceleration of water particles were investigated. The results showed that the interpolated field wave spectrum combined with the SWOP direction spectrum could accurately simulate the three-dimensional typhoon waves. The significant wave height and the average period of the simulated wave surface at fixed point were statistically evaluated by the upward zero-crossing method, and the relative errors were ± 5% and ± 15%, respectively. The typhoon wave surface computed by a dual peak spectrum had small periodic waves, and the velocity and acceleration of water particles differed considerably from the JONSWAP spectrum. Finally, a fastened slender cylinder was simulated under action of the typhoon waves, which proved the applicability of the computation method. This study aims at providing a basis for the simulation of the dynamic response of marine structures under the typhoon waves action.