bluuebunny/crossref_metadata_2025 huggingface dataset:
Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE)
Authors: Zhao Yifan , Wu Qirui , Wei Panpan , Zhao Hao , Zhang Xiwang , Pang Chenkun
Published: July 2022

The urban surface temperature is a complex integrated natural-human geographic phenomena; with the development of geostatistical methods and the application of multisource data, its research has gradually shifted from a single perspective to a study that integrates multiple factors such as nature and humanity. However, based on the context of the integration of natural and human factors and mutual constraints of each factor, the research on the mechanism of influence on urban habitat thermal environment needs to be further deepened. Therefore, this paper explores the spatial and temporal heterogeneity of urban surface temperature in Zhengzhou City during the summer of 2013–2020 from the perspective of multi-source data fusion, and uses the Geodetector model to quantitatively reveal the main influencing factors of urban surface temperature and the impact of superimposed factors on the compound effect of surface temperature. The results show that: (1) the urban thermal environment in the central of Zhengzhou city (region within the first ring) is obvious, and it is mainly concentrated in commercial and densely populated areas. (2) According to trend analysis, the northwest-southeast direction of the city continues to increase in temperature from 2013–2020, coupled with the direction of urban development. (3) Among the factors affecting urban surface temperature, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and human elements are particularly typical. NDVI and TCW are strongly negatively correlated with the urban thermal environment, while NDBI and human elements are strongly positively correlated. (4) Mitigation of the urban thermal environment can start with the interaction mechanism of positive and negative factors. This study provides new ideas for the mechanism analysis of spatial and temporal evolution patterns of the urban thermal environment under multifactorial constraints, and provides suggestions and decisions for promoting green and sustainable urban development.
AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection
Authors: Zuo Zhen , Tong Xiaozhong , Wei Junyu , Su Shaojing , Wu Peng , Guo Runze , Sun Bei
Published: July 2022

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking.
Analysis of the 10–20-Day Intraseasonal Oscillation in the Indian Ocean Using Surface Winds
Authors: Roman-Stork Heather L. , Bourassa Mark A.
Published: July 2022

The 10–20-day mode of surface wind is examined in the Indian Ocean, with special reference to the Arabian Sea, Bay of Bengal, equatorial, southern, and southeastern Indian Ocean during a strong (1994), weak (2002), and normal (1995) southwest monsoon season. Results indicate the 10–20-day mode of surface winds in the Bay of Bengal, southern Indian Ocean, and southeastern Indian Ocean is more energetic than in other regions. The strongest 10–20-day signal is found to be in the southeastern Indian Ocean, where 45% of surface wind variability can be explained by this mode during a strong monsoon year. Composite analysis based on a time series in this region revealed a positive surface wind anomaly that appears at 60°E, centered on 15°S, and propagates zonally eastward to 90°E before reflecting back to propagate westward and then disperse off the coast of Madagascar. It is proposed that this oscillating positive wind anomaly is a feature of the southernmost cell of the 10–20-day convective double-cell structure that has extended farther south into the southern Indian Ocean and that this mode connects the northern and southern Indian Ocean through surface winds and atmospheric convection through the motion of the linked double-cell structure.
Analysis of Interannual and Seasonal Nearshore Bar Behaviour Observed from Decadal Optical Satellite Data in the Curonian Spit, Baltic Sea
Authors: Janušaitė Rasa , Jarmalavičius Darius , Jukna Laurynas , Žilinskas Gintautas , Pupienis Donatas
Published: July 2022

Long-term observations of nearshore bar behaviour are a vital component of coastal monitoring, management, and prediction. Optical satellite remote sensing enables the possibility of such observations over large spatial areas, but its full potential remains unexploited. This study assessed alongshore variability in cross-shore nearshore bar behaviour on a wave-dominated multi-bar coast of the Curonian Spit (south-eastern Baltic Sea) between 2011 and 2021, using satellite-derived bar data. Nearshore bars were extracted from a time series of PlanetScope and RapidEye satellite images with an automated GIS-based algorithm, previously proposed by the study authors. The cross-shore behaviour of a multiple bar system was analysed by adapting traditional bathymetry-based analysis techniques to satellite-derived data that included bar crestlines and images of multi-scale Relative Bathymetric Position Index (RBPI). The analysis was performed on 1071 shore-perpendicular transects. Multi-bar onshore and offshore migration rates were quantified on interannual and seasonal timescales. The results show that, on an interannual timescale, bars migrated offshore at rates up to 9.7 m/month, while the rates of onshore migration reached up to 11 m/month. During the months of low wave energy, bars moved offshore at rates up to 6.2 m/month, and during the months of high wave energy, up to 12.9 m/month. However onshore migration rates, during the months of low and high wave energy, reached up to 7.0 and 13.4 m/month, respectively. A complex empirical orthogonal function (CEOF) analysis was performed on RBPI-derived cross-shore profiles, and cyclic offshore directed bar behaviour was examined. For the first time, the net offshore migration (NOM) cycle with bar cycle return periods of 1.8 to 13.5 years was investigated on the south-eastern Baltic Sea coast. Bar cycle return periods increased and rates of bar cross-shore migration decreased from north to south along the Curonian Spit. Similar nearshore bar behaviour regions were identified using clustering analysis based on quantified temporal and morphological characteristics of the bars. Factors controlling alongshore variability in bar cross-shore behaviour were determined. The study results suggest that small alongshore variations in nearshore hydrodynamics, caused by the local wave climate and its interplay with the shoreline orientation, determine the morphological and temporal variability of the multi-bar system in the Curonian Spit.
What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery
Authors: Suel Esra , Sorek-Hamer Meytar , Moise Izabela , von Pohle Michael , Sahasrabhojanee Adwait , Asanjan Ata Akbari , Arku Raphael E. , Alli Abosede S. , Barratt Benjamin , Clark Sierra N. , Middel Ariane , Deardorff Emily , Lingenfelter Violet , Oza Nikunj C. , Yadav Nishant , Ezzati Majid , Brauer Michael
Published: July 2022

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to the high input data needs of existing estimation approaches. We introduced a computer vision method to estimate annual means for air pollution levels from street-level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250 k images for each city). Our experimental setup is designed to quantify intra- and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Similar to LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities (London, New York, and Vancouver), which have similar pollution source profiles, was moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on cities with very different source profiles, such as Accra in Ghana and Hong Kong, were lower (R2 between zero and 0.21). This suggests a need for local calibration, using additional measurement data from cities that share similar source profiles.
Increasing Impact of Precipitation on Alpine-Grassland Productivity over Last Two Decades on the Tibetan Plateau
Authors: Zha Xinjie , Niu Ben , Li Meng , Duan Cheng
Published: July 2022

Understanding the importance of temperature and precipitation on plant productivity is beneficial, to reveal the potential impact of climate change on vegetation growth. Although some studies have quantified the response of vegetation productivity to climate change at local, regional, and global scales, changes in climatic constraints on vegetation productivity over time are not well understood. This study combines the normalized difference vegetation index (NDVI) and the net primary production (NPP) modeled by CASA during the plant-growing season, to quantify the interplay of climatic (growing-season temperature and precipitation, GST and GSP) constraints on alpine-grassland productivity on the Tibetan Plateau, as well as the temporal dynamics of these constraints. The results showed that (1) 42.2% and 36.3% of grassland NDVI and NPP on the Tibetan Plateau increased significantly from 2000 to 2019. GSP controlled grassland growth in dryland regions, while humid grasslands were controlled by the GST. (2) The response strength of the NDVI and NPP to precipitation (partial correlation coefficient RNDVI-GSP and RNPP-GSP) increased substantially between 2000 and 2019. Especially, the RNDVI-GSP and RNPP-GSP increased from 0.14 and 0.01 in the first 10year period (2000–2009) to 0.83 and 0.78 in the second 10-year period (2010–2019), respectively. As a result, the controlling factor for alpine-grassland productivity variations shifted from temperature during 2000–2009 to precipitation during 2010–2019. (3) The increase in precipitation constraints was mainly distributed in dryland regions of the plateau. This study highlights that the climatic constraints on alpine-grassland productivity might change under ongoing climate change, which helps the understanding of the ecological responses and helps predict how vegetation productivity changes in the future.
Pushbroom Photogrammetric Heights Enhance State-Level Forest Attribute Mapping with Landsat and Environmental Gradients
Authors: Strunk Jacob L. , Bell David M. , Gregory Matthew J.
Published: July 2022

We demonstrate the potential for pushbroom Digital Aerial Photogrammetry (DAP) to enhance forest modeling (and mapping) over large areas, especially when combined with multitemporal Landsat derivatives. As part of the National Agricultural Imagery Program (NAIP), high resolution (30–60 cm) photogrammetric forest structure measurements can be acquired at low cost (as low as $0.23/km2 when acquired for entire states), repeatedly (2–3 years), over the entire conterminous USA. Our three objectives for this study are to: (1) characterize agreement between DAP measurements with Landsat and biophysical variables, (2) quantify the separate and combined explanatory power of the three auxiliary data sources for 19 separate forest attributes (e.g., age, biomass, trees per hectare, and down dead woody from 2015 USFS Forest Inventory and Analysis plot measurements in Washington state, USA) and (3) assess local biases in mapped predictions. DAP showed the greatest explanatory power for the widest range of forest attributes, but performance was appreciably improved with the addition of Landsat predictors. Biophysical variables contribute little explanatory power to our models with DAP or Landsat variables present. There is need for further investigation, however, as we observed spatial correlation in the coarse single-year grid (≈1 plot/25,000 ha), which suggests local biases at typical scales of mapped inferences (e.g., county, watershed or stand). DAP, in combination with Landsat, provides an unparalleled opportunity for high-to-medium resolution forest structure measurements and mapping, which makes this auxiliary data source immediately viable to enhance large-scale forest mapping projects.
Remote Sensing Data Fusion to Evaluate Patterns of Regional Evapotranspiration: A Case Study for Dynamics of Film-Mulched Drip-Irrigated Cotton in China’s Manas River Basin over 20 Years
Authors: Qiao Xuejin , Yang Guang , Shi Jianchu , Zuo Qiang , Liu Lining , Niu Mu , Wu Xun , Ben-Gal Alon
Published: July 2022

The accurate quantification of evapotranspiration (ET) is critical to the sustainable management of irrigated agriculture. In this study, we proposed a remote sensing data fusion method for predicting ET, coupling a surface energy balance system model with an enhanced spatial and temporal adaptive reflectance fusion model utilizing remote sensing inversion with satellite data from Landsat and MODIS. The method was tested for a case study with cotton fields under film-mulched drip irrigation (FMDI) in the Manas River Basin. Areas under FMDI were identified, and ET patterns were evaluated for a 21-year period spanning from 2000 to 2020. A field experiment, a regional survey, and data retrieved from the literature provided results demonstrating that the method allowed reliable estimation of ET distribution with simultaneously, relatively high spatial and temporal resolutions at both field and regional scales. ET was found to decline from upstream to downstream in the basin, with the difference gradually diminishing over time. Supported by the promotion of FMDI technology, the area under cotton production in the basin increased by an average of 4.9% annually. Limited irrigation quotas to farmers and, therefore, water application per area led to a decreasing ratio of relative water supply for potential ET and, thus, to a reduction in average actual ET of 7.5 mm year−1. The average ET in the basin declined to about 415.7 mm in 2020, 287.2 mm lower than its water demand. The dynamics of fused ET provide a reliable scientific basis for agricultural water resources planning and management and for the sustainable utilization of water and soil resources in the basin. The method, with simultaneously high temporal and spatial resolutions, should have both local and global practical potential.
Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review
Authors: García-Santos Vicente , Sánchez Juan , Cuxart Joan
Published: July 2022

Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET) using remote sensing techniques was first considered. Numerous ET models have been proposed, validated and improved along these five decades, as the satellites and sensors onboard were enhanced. This study reviews most of the efforts in the progress towards providing a trustworthy value of ET by means of thermal remote sensing data. It starts with an in-depth reflection of the surface energy balance concept and of each of its terms, followed by the description of the approaches taken by remote sensing models to estimate ET from it in the last thirty years. This work also includes a chronological review of the modifications suggested by several researchers, as well as representative validations studies of such ET models. Present limitations of ET estimated with remote sensors onboard orbiting satellites, as well as at surface level, are raised. Current trends to face such limitations and a future perspective of the discipline are also exposed, for the reader’s inspiration.
GAN-Based LiDAR Translation between Sunny and Adverse Weather for Autonomous Driving and Driving Simulation
Authors: Lee Jinho , Shiotsuka Daiki , Nishimori Toshiaki , Nakao Kenta , Kamijo Shunsuke
Published: July 2022

Autonomous driving requires robust and highly accurate perception technologies. Various deep learning algorithms based on only image processing satisfy this requirement, but few such algorithms are based on LiDAR. However, images are only one part of the perceptible sensors in an autonomous driving vehicle; LiDAR is also essential for the recognition of driving environments. The main reason why there exist few deep learning algorithms based on LiDAR is a lack of data. Recent translation technology using generative adversarial networks (GANs) has been proposed to deal with this problem. However, these technologies focus on only image-to-image translation, although a lack of data occurs more often with LiDAR than with images. LiDAR translation technology is required not only for data augmentation, but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. In other words, driving simulation is a key technology for evaluating and verifying algorithms which are practically applied to vehicles. In this paper, we propose a GAN-based LiDAR translation algorithm for autonomous driving and driving simulation. It is the first LiDAR translation approach that can deal with various types of weather that are based on an empirical approach. We tested the proposed method on the JARI data set, which was collected under various adverse weather scenarios with diverse precipitation and visible distance settings. The proposed method was also applied to the real-world Spain data set. Our experimental results demonstrate that the proposed method can generate realistic LiDAR data under adverse weather conditions.
Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis
Authors: Ghaffari Arash , Rahbek Ole , Lauritsen Rikke Emilie Kildahl , Kappel Andreas , Kold Søren , Rasmussen John
Published: July 2022

Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test–retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93–0.96]. The concordance correlation coefficients for the two systems’ agreement were [0.81–0.91] in the time domain and [0.43–0.99] in the frequency domain. The absolute biases estimated by the Bland–Altman method were [0.0005–0.008] in the time domain and [0–0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring.
Data Acquisition System for On-the-Go Soil Resistance Force Sensor Using Soil Cutting Blades
Authors: Cviklovič Vladimír , Mojžiš Miroslav , Majdan Radoslav , Kollárová Katarína , Tkáč Zdenko , Abrahám Rudolf , Masarovičová Soňa
Published: July 2022

Worldwide, agricultural land is a dominant part of the environment. It is very important to understand the physical properties of soil because they directly or indirectly affect the entire human population. This paper proposes a data acquisition system for an original design of the soil resistance force sensor (SRFS). It serves to evaluate the properties of soil affected and unaffected by tractor passages through the field. The SRSF uses two cutting blades to measure soil mechanical resistance within the tire track and outside the tire track. The proposed system consists of two load cells, datalogger, power supply and software for personal computers. The system was practically tested under field operation. The results showed significant differences between the soil resistance force measured outside the tire track and within the tire track after one, two and three tractor passages. The data were compared with penetrometer resistance and soil bulk density, standardly characterizing soil mechanical resistance. An increase of soil resistance force after one, two and three tractor passages corresponded with an increase in reference parameters. The results showed that the proposed system is suitable for practical applications to evaluate soil mechanical resistance using SRFS.
Unified DeepLabV3+ for Semi-Dark Image Semantic Segmentation
Authors: Memon Mehak Maqbool , Hashmani Manzoor Ahmed , Junejo Aisha Zahid , Rizvi Syed Sajjad , Raza Kamran
Published: July 2022

Semantic segmentation for accurate visual perception is a critical task in computer vision. In principle, the automatic classification of dynamic visual scenes using predefined object classes remains unresolved. The challenging problems of learning deep convolution neural networks, specifically ResNet-based DeepLabV3+ (the most recent version), are threefold. The problems arise due to (1) biased centric exploitations of filter masks, (2) lower representational power of residual networks due to identity shortcuts, and (3) a loss of spatial relationship by using per-pixel primitives. To solve these problems, we present a proficient approach based on DeepLabV3+, along with an added evaluation metric, namely, Unified DeepLabV3+ and S3core, respectively. The presented unified version reduced the effect of biased exploitations via additional dilated convolution layers with customized dilation rates. We further tackled the problem of representational power by introducing non-linear group normalization shortcuts to solve the focused problem of semi-dark images. Meanwhile, to keep track of the spatial relationships in terms of the global and local contexts, geometrically bunched pixel cues were used. We accumulated all the proposed variants of DeepLabV3+ to propose Unified DeepLabV3+ for accurate visual decisions. Finally, the proposed S3core evaluation metric was based on the weighted combination of three different accuracy measures, i.e., the pixel accuracy, IoU (intersection over union), and Mean BFScore, as robust identification criteria. Extensive experimental analysis performed over a CamVid dataset confirmed the applicability of the proposed solution for autonomous vehicles and robotics for outdoor settings. The experimental analysis showed that the proposed Unified DeepLabV3+ outperformed DeepLabV3+ by a margin of 3% in terms of the class-wise pixel accuracy, along with a higher S3core, depicting the effectiveness of the proposed approach.
Rivers’ Water Level Assessment Using UAV Photogrammetry and RANSAC Method and the Analysis of Sensitivity to Uncertainty Sources
Authors: Giulietti Nicola , Allevi Gloria , Castellini Paolo , Garinei Alberto , Martarelli Milena
Published: July 2022

Water-level monitoring systems are fundamental for flood warnings, disaster risk assessment and the periodical analysis of the state of reservoirs. Many advantages can be obtained by performing such investigations without the need for field measurements. In this paper, a specific method for the evaluation of the water level was developed using photogrammetry that is derived from images that were recorded by unmanned aerial vehicles (UAVs). A dense point cloud was retrieved and the plane that better fits the river water surface was found by the use of the random sample consensus (RANSAC) method. A reference point of a known altitude within the image was then exploited in order to compute the distance between it and the fitted plane, in order to monitor the altitude of the free surface of the river. This paper further aims to perform a critical analysis of the sensitivity of these photogrammetric techniques for river water level determination, starting from the effects that are highlighted by the state of the art, such as random noise that is related to the image data quality, reflections and process parameters. In this work, the influences of the plane depth and number of iterations have been investigated, showing that in correspondence to the optimal plane depth (0.5 m) the error is not affected by the number of iterations.
Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data
Authors: Ma Zhoujun , Zhu Hong , He Zhuohao , Lu Yue , Song Fuyuan
Published: July 2022

Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.
An Integrated Chromatographic Strategy for the Large-Scale Extraction of Ergosterol from Tulasnellaceae sp.
Authors: Wang Ze , Wang Qi , Zhang Jinkui , Lin Pengcheng , Dang Jun
Published: July 2022

A reliable chromatographic strategy is crucial for the extraction of target compounds from natural sources as it is related to the preparation efficiency, as well as the purity of the compounds. In this study, medium-pressure normal-phase liquid chromatography and high-pressure reverse-phase liquid chromatography were combined to prepare and purify ergosterol from Tulasnellaceae sp. of Gymnadenia orchidis. First, Tulasnellaceae sp. was extracted three times (2.0 L and 2 h each time) with ethyl acetate, and the 6.0 L of extract solution was concentrated under reduced pressure to yield 2.2 g of crude sample. Then, the crude sample was pretreated utilizing silica gel medium-pressure liquid chromatography to enrich the target ingredient (586.0 mg). Finally, high-pressure reversed-phase liquid chromatography was used to purify the target compound, and the compound was characterized as ergosterol (purity > 95%) using spectral data. Overall, the simple and reproducible integrated chromatographic strategy developed in this study has the potential for the large-scale purification of steroids for laboratory and even industrial research. To the best of our knowledge, this is also the first report of ergosterol in Tulasnellaceae sp.
Fabrication of High-Performance Nanofiltration Membrane Using Polydopamine and Carbon Nitride as the Interlayer
Authors: Ma Lei , Bi Qiuyan , Tang Yuanhui , Zhang Chao , Qi Fuju , Zhang Hao , Gao Yifan , Xu Shiai
Published: July 2022

In order to recover lithium from brine with a high Mg2+/Li+ ratio, a positively charged nanofiltration (NF) membrane was prepared by depositing polydopamine (PDA)-coated graphitic carbon nitride (g-C3N4) as the interlayer (PDA-g-C3N4) and the interfacial polymerization (IP) of polyethyleneimine (PEI) and trimesoyl chloride (TMC) was carried out. Under optimal conditions, the water contact angle of the composite membrane is only 55.5° and the isoelectric point (IEP) is 6.01. The final positively charged NF membrane (M5) exhibits high permeance (10.19 L·m−2·h−1·bar−1) and high rejection of Mg2+ (98.20%) but low rejection of Li+ (13.33%). The separation factor (SF) is up to 48.08, and the Mg2+/Li+ ratio of the permeate is 0.036 in the simulated brine. In conclusion, the M5 membrane shows a good separation performance for salt lake brine (SF = 12.79 and Mg2+/Li+ ratio of the permeate = 1.43) and good fouling resistance. Therefore, the positively charged M5 membrane with PDA-g-C3N4 as the interlayer has the potential to be used for the recovery of lithium from brine.
Promotion and Restoration of Health with the Amazing Mediterranean Diet versus an Increase in Childhood Pathology Due to the Disastrous Diet of ‘Western Civilisation’
Authors: Calatayud-Sáez Fernando M.
Published: July 2022

‘Understand your honours that it is not so much that the traditional diet rooted in our homes has a miraculous character, as that the diet that modernity imposes on us is unhealthy and inadequate for the human intestines [...]
A Qualitative Exploration of a Biopsychosocial Profile for Experiencing Sexual Harassment and Abuse in Sports
Authors: van Voorthuizen Mercede , Faber Irene Renate , van de Bongardt Daphne , Schipper-van Veldhoven Nicolette
Published: July 2022

The purpose of this study was to explore a biopsychosocial profile for experiencing sexual harassment and abuse in sports. A qualitative approach was used; data were collected from semi-structured in-depth interviews covering seven cases of sexual harassment and abuse in sports in the Netherlands. The interview transcripts were analysed and aligned with the biopsychosocial model. The results reveal biological (i.e., aged under 18, sex, and sexual orientation), psychological (i.e., high degree of naivety, altruism and agreeableness, low self-esteem, perfectionism, emotional or disorders) and social factors (i.e., poor or negative relationship with parents, social pressure to perform, incest at home, social isolation, elite sports and too much power of a single trainer/coach) that can contribute to the risk of experiencing sexual harassment and abuse in sports. These findings provide important directions for prevention and recognition in sports practice and future research.
Why Do(n’t) We Buy Second-Hand Luxury Products?
Authors: Stolz Katharina
Published: July 2022

Global sales of second-hand luxury products are steadily increasing. To better understand key drivers for purchasing second-hand luxury products, a survey was conducted including 469 respondents. The study not only includes existing customers, but also non-customers and thus potential future clientele. Based on the theory of planned behavior, the components of attitude, subjective norms, perceived behavioral control, and individual motivators (creative, economic, ethical, nostalgic, sustainable) were investigated. Using structural equation modeling, the results support the influence of attitude, past purchase experience, perceived behavioral control, and subjective norms on purchase intention. Attitude, in turn, is mainly impacted by economics and ethics as individual motivators. Further, subjects were asked to state their own definition of luxury and to name individual reasons for and against second-hand luxury products. Thereby, quality turned out to be double-edged: on the one hand, second-hand products were able to prove their quality, on the other hand, the fear of counterfeits and unhygienic products was cited. Overall, consumers of second-hand luxury goods are heterogeneous and have different buying experiences with one or more of the three categories: new luxury goods, second-hand luxury goods, and second-hand products. The study thus expands the understanding of the transformation within the (second-hand) luxury industry.
Waterborne Polyurethane/Acrylic Adhesive Blends from Physaria fendleri Oil for Food Packaging Applications
Authors: Mort Rebecca , Olson Emily , Thurber Henry , Jiang Shan , Vorst Keith , Curtzwiler Greg
Published: July 2022

Environmental concerns and the diminishing acceptability of using petrochemical polymers require innovative synthetic approaches to materials for essential polymeric technologies such as adhesives. Biobased plant oils have been suggested as replacements for petrochemical monomers in polyurethane formulations. A variety of seed oil extracts from plants contain naturally occurring functional groups such as hydroxyl and glycidyl ether, which can be utilized in polyurethane synthesis. Most studies of bioderived polyurethane adhesives occur in solventborne systems and with chemically modified oils. However, rising concerns and manufacturing limitations of volatile organic compounds in solventborne systems warrant investigation into more sustainable and alternatives that are easier to handle. In this work, we synthesized waterborne polyurethanes comprised of oil derived from Physaria fendleri seed (naturally occurring hydroxyl functionality), hexamethylene diisocyanate, toluene diisocyanate, and dimethyl propionic acid. Acrylate copolymers were synthesized via emulsion polymerization comprised of different butyl and methylmethacrylate monomer ratios. These polymers were formulated into waterborne polyurethane/acrylic adhesive blends. The resulting formulations possess a commercially comparable peel strength of >6 N and are suggested for use in resealable food packaging applications. This study demonstrates the utility of oil derived from Physaria fendleri seeds in waterborne adhesive applications, adding value with bioderived materials and increasing sustainability of polyurethane adhesives.
Research on Industrial Ecological Efficiency Evaluation and Improvement Countermeasures Based on Data-Driven Evaluations from 30 Provinces and Cities in China
Authors: Liu Fan , Zhou Shuling , Yang Yaliu , Liu Conghu
Published: July 2022

Improving industrial ecological efficiency is important in promoting the industry’s sustainable development. However, the economy, resources, the environment, and other factors should be considered. This paper proposes a data-driven evaluation and promotion method for improving industrial ecological efficiency. Based on industrial input and output data, the super-efficiency slack-based model containing an unexpected output was used to measure industrial ecological efficiency. The kernel density estimation method was employed to analyze the time-series characteristics of industrial ecological efficiency. Using data from 30 provinces and cities in China, this study demonstrated the implementation of a data-driven method. The results show that China’s overall industrial ecological efficiency is increasing, and industrial ecological efficiency in the western region is rapidly improving. Differences exist between provinces and cities; the characteristics of polarization are significant, and there are short boards in the eastern, central, and western regions. Based on this, suggestions are made to improve the industrial ecological efficiency of the central region, narrow the gaps between the regions, and promote each region to develop its strengths and mitigate its weaknesses. This provides a basis for formulating policies related to ecological environment protection and industrial pollution control.
Biodiversity and Economy but Not Social Factors Predict Human Population Dynamics in South Africa
Authors: Phogole Bopaki , Yessoufou Kowiyou
Published: July 2022

The ongoing exponential growth of human population poses a risk to sustainable development goals (SDGs). Unless we understand the drivers of this growth and inform policy development accordingly, SDGs would remain a dream. One of the old theories of population growth known as the Malthusian theory predicts that resource availability drives population growth until a certain time when population growth outrun resource availability, leading to all sort of crises summarized as Malthusian crisis. Although the link between economic growth and population has been widely investigated while testing the theory, little is known about environmental and social factors potentially driving population growth. Here, because of various crises of our time recalling the Malthusian crisis, we revisited the theory by fitting structural equation models to environmental, social and economic data collected over 30-year period in South Africa. None of the social variables tested predicts population growth. Instead, we found that biodiversity (species protection index) correlates positively with population growth. Biodiversity provides various resources through ecosystem goods and services to human, thus supporting population growth as predicted in the Malthusian theory. However, we also found that this population growth may lead to conservation conflict as we found that biodiversity habitat (wetland area) correlates negatively with population growth, thus raising the compromising effect of population growth on life on earth. What’s more, we found a significant link between economic growth measured as GDP and population growth, further supporting the Malthusian prediction. Overall, our study re-affirms the value of biodiversity to human and suggests that the Malthusian theory should continuously be tested with predictors other than economic.
Overcoming the Challenges of Sustainable Family Agriculture in Southern Brazil: Contributions to the 2030 Agenda
Authors: Dal Moro Leila , Mazutti Janaina , Brandli Luciana Londero , Casagranda Yasmin Gomes , Mores Giana de Vargas
Published: July 2022

Concerning sustainability and food production, efforts are made by social, political, and environmental actors to enact changes in production and consumption patterns and adopt new management models and instruments. Hunger, waste, poor distribution of food, and low diversity affect many countries, and small family farmers are abandoning their activities due to the challenges which minimize the possibilities of contemplating SDG 12. Local production and SDG 12 favor access to safe and cheaper food, thereby encouraging food security and minimizing food waste. Focus groups and interviews were conducted with the participation of local actors in 16 municipalities in southern Brazil. The study took a qualitative research approach and content analysis was used as a mode of investigation. It was then possible to provide assistance to the decision-making process of local and regional public authorities in terms of development to support sustainable production and consumption. This study highlights the contribution of family agriculture to the UN’s Sustainable Development Goals (SDG), specifically SDG 12. The study presents guidelines and means of implementation for a more sustainable family agriculture, as well as attributes to the challenges of the sector. With this approach, it was possible to connect the problem with the solutions, classifying them by thematic axes.
Definition, Role, and Functions of Soil Related to the Knowledge Society and the Someș-Tisa Hydrographic Area (Romania)
Authors: Cioruța Bogdan-Vasile , Coman Mirela
Published: July 2022

In Romania, under the aegis of the knowledge society, we naturally wondered if the soil still had a role, through the functions it fulfills, at the level of local communities. In this context, we set out, starting from the definition of soil as a cosmic–telluric–biotic product with notable valences in the transition of communities from hunting to knowledge-based societies, to make some scientific observations on the importance of soil resources. The research methodology was outlined around the importance and functions of soil in society by presenting the current state of land use in the Someș-Tisa region. As reference points, graphs on the hydrometeorological conditions made using the MeteoBlue application and an archive of the meteorological station in Baia Mare were used, as provided (per an open-access regime) by the Someș-Tisa Water Basin Administration and the Maramures County Council, respectively. Moreover, the research was completed with studies conducted by specialists, most of which were included in the Application Guide of the XVIII National Conference of Pedology held in 2006 in Cluj-Napoca. The research results showed that soil resources were still of vital importance for the communities in the Someș-Tisa area, where agricultural and agrotourism practices predominate. At the same time, it was noticed that the soil resources required increased attention as a result of the pollution resulting from the development of mining activities in large areas. Consequently, the soil resources in the Someș-Tisa area, which closely define the evolution of local communities, should be carefully monitored and subject to good practice guidelines specific to the objectives of sustainable development.
Antecedents of Responsible Leadership: Proactive and Passive Responsible Leadership Behavior
Authors: Zhang Xinyue , Li Dian , Guo Xintong
Published: July 2022

Due to the massive global impact of the current COVID-19 pandemic, the call for social and environment responsibility has grown stronger, and discussion regarding responsible leadership has been aroused. Agreement on the antecedents of responsible leadership has not yet been reached, however, and most studies are theoretical and focus on the personal characteristics of leaders. In this paper, we explore the antecedents of responsible leadership behavior at the organizational and situational level, through case studies of companies in China. Based on the organizational strategy, organizational ownership, mass media, crisis, local community, and other influencing factors, we determine two types of responsible leadership behaviors: Proactive responsible leadership behavior, which involves leaders showing initiative in dealing with relationships with stakeholders both inside and outside the organization, as well as voluntarily striving to assume relevant responsibilities in order to achieve economic and/or social development goals; and passive responsible leadership behavior, which involves acts of being responsible to some stakeholders under pressure from external circumstances or when unexpected events occur, even though this behavior may go against the actor’s original intention. We further explore the influence mechanism of these factors on responsible leadership and discuss the development trend from passive to proactive responsible leadership behavior.
Study on Fire Ventilation Control of Subway Tunnel: A Case Study for Dalian Subway
Authors: Dong Sihui , Zhang Xinyu , Wang Kang
Published: July 2022

During the actual operation of a subway company, only one ventilation-control scheme is considered in the emergency plan, without considering the specific location difference of the fire source. However, in the case of an actual tunnel fire, the best ventilation-control scheme and personnel-evacuation scheme are very different given the potential different locations of the fire source. We consider the use of a connecting channel for smoke exhaust or personnel evacuation and study the best ventilation-control scheme and personnel-evacuation scheme, when the fire source is at different positions relative to the train, and the train is at different positions relative to the connecting channel. Taking the tunnel between Yaojia Station and Nanguanling Station of Metro Line 1 in Dalian, China, as an example, a 1:1 full-scale numerical model is established to study dangerous fire-related conditions, such as carbon monoxide concentration, smoke visibility, and temperature. Nine typical working conditions of a tunnel-section fire are studied. The traditional and commonly used longitudinal-ventilation mode can ensure smoke control and personnel evacuation. For the working conditions of fire in the end of the train the ventilation-control scheme designed in this paper can ensure the safety of personnel. However, the working conditions of fire in the middle of a train are the most dangerous, and about 50% of personnel are affected by smoke during the escape. This paper analyzes the impact of the longitudinal-ventilation mode, transverse-ventilation mode, and semi-transverse-ventilation mode on personnel evacuation under such working conditions. It is found that with the semi-transverse-ventilation mode, personnel are least affected. Furthermore, semi-transverse ventilation requires a higher engineering investment, which is more than RMB 2000 per meter of tunnel. If the economic conditions are available, it is recommended to consider the semi-transverse-ventilation mode instead of the longitudinal-ventilation mode. The research results can provide guidance for the emergency-control scheme for subway-tunnel fire operation.
Impact of the COVID-19 Pandemic on Export Survival from Latin American Countries
Authors: Morales Luis Felipe Beltrán
Published: July 2022

This study analyzes the impact of mobility, as a proxy for social distancing measures, on exports to the United States of America (USA). A mobility index based on Google mobility indicators was constructed using Principal Component Analysis (PCA), and an Accelerated Failure Time (AFT) model was fitted to the data on export survival from a group of Latin American countries (LATAM). Higher mobility levels are associated with an acceleration of the risk of interruption of exports. On average, LATAM shows higher export survival levels compared to other countries. Higher innovation and market concentration favored export survival, while higher levels of Real Effective Exchange Rate (REER) are associated with a lower probability of survival. Differences in survival were found between export sectors with regard to machinery and transportation equipment.