bluuebunny/crossref_metadata_2025 huggingface dataset:
In Situ Fenton Triggered PDA Coating Copper Mesh with Underwater Superoleophobic Property for Oily Wastewater Pretreatment
Authors: Xu Ying , Wang Wei , Zhu Zhigao , Xu Bin
Published: September 2021

The issue of oily wastewater treatment has become a worldwide challenge due to increasing industrial oily wastewater and frequent oil spill accidents. As an integral part of practical sewage treatment, pretreatment is conducted to remove inorganic particles, floating oil, and some emulsified oil, and to pave the way for post-treatment. Here, we report a facile fabricated, hydrostable, and rapid underwater-formed superoleophobic copper mesh with polydopamine (PDA) coating for efficient oily wastewater pre-treatment. Unlike with traditional technologies, using the interface phenomenon to solve the problem of oil/water mixture separation provided a new approach for the low energy input pretreatment process. The PDA coating formed by the in situ Fenton method not only rapidly constructs a protection layer for the etched hierarchical micro-size particles on mesh and results in enhanced hydrophilicity, but also exhibits high uniformity and enhanced stability in acid/alkali medium. Benefiting from the above processes, a very high flux of 25 L m−2 s−1 and high separation efficiency of 99.0% toward various oil/water mixtures were achieved, revealing excellent prospects for practical usage. Therefore, this new approach offered insight into the development of a cost-effective and functional method for efficient pretreatment of oily wastewater.
Biomolecular Modifications Linked to Oxidative Stress in Amyotrophic Lateral Sclerosis: Determining Promising Biomarkers Related to Oxidative Stress
Authors: Hosaka Takashi , Tsuji Hiroshi , Tamaoka Akira
Published: September 2021

Reduction–oxidation reactions are essential to cellular homeostasis. Oxidative stress transcends physiological antioxidative system damage to biomolecules, including nucleic acids and proteins, and modifies their structures. Amyotrophic lateral sclerosis (ALS) is the most common adult-onset motor neuron disease. The cells present in the central nervous system, including motor neurons, are vulnerable to oxidative stress. Neurodegeneration has been demonstrated to be caused by oxidative biomolecular modifications. Oxidative stress has been suggested to be involved in the pathogenesis of ALS. Recent progress in research on the underlying mechanisms of oxidative stress in ALS has led to the development of disease-modifying therapies, including edaravone. However, the clinical effects of edaravone remain limited, and ALS is a heretofore incurable disease. The reason for the lack of reliable biomarkers and the precise underlying mechanisms between oxidative stress and ALS remain unclear. As extracellular proteins and RNAs present in body fluids and represent intracellular pathological neurodegenerative processes, extracellular proteins and/or RNAs are predicted to promise diagnosis, prediction of disease course, and therapeutic biomarkers for ALS. Therefore, we aimed to elucidate the underlying mechanisms between oxidative stress and ALS, and promising biomarkers indicating the mechanism to determine whether therapy targeting oxidative stress can be fundamental for ALS.
Monasticism in the British Isles: A Comparative Overview
Authors: Stöber Karen
Published: September 2021

The medieval British Isles were marked by a lively monastic presence throughout the entire period. Groups of monks, nuns, regular canons and canonesses, and friars established communities even in the furthermost reaches of the territory, and by doing so they came to play an important part in the life, culture, economy, and politics of the region. This paper will provide an overview of the arrival and spread of the different religious orders in England, Ireland, Wales, and Scotland, and by doing so, it will provide some comparative study of the different parts of the British Isles and examine how and when the spread and settlement of the various religious groups manifested itself across the islands, and what their impact was upon their localities and the society around them.
Finding Wholes in the Metaverse: Posthuman Mystics as Agents of Evolutionary Contextualization
Authors: Bolger Ryan K.
Published: September 2021

The Metaverse is a pervasive expression of technological culture whose impact will be global. First, through knowledge, then through social, and now through geo-spatial, AI (the foundation of the Metaverse) will connect all entities on Earth through digital means thereby creating a three-dimensional informational and experiential layer across the world dubbed the Metaverse. The Metaverse has four characteristics: augmented reality, lifelogging, mirror worlds, and virtual reality. From the standpoint of Christian cultural engagement, a contextual theology has yet to be developed. In the work that follows, the Metaverse is engaged through a combination of contextualization and wholemaking from the standpoint of posthumanism and mysticism. The study focuses on evolutionary wholemaking as identified by Teilhard/Delio, while being guided by Bevans’ five (early) models of contextualization. The method of contextual wholemaking enables new ways of seeing, embracing, communing, complexifying, and creating within the four spheres of the Metaverse. After exploring the nature of the Metaverse in the first half of the paper, insights were gathered from the dialogue between contextual theology and culture and discussed in the second half of the paper.
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands
Authors: Martínez Prentice Ricardo , Villoslada Peciña Miguel , Ward Raymond D. , Bergamo Thaisa F. , Joyce Chris B. , Sepp Kalev
Published: September 2021

High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.
A Novel Fast Multiple-Scattering Approximate Model for Oceanographic Lidar
Authors: Zhang Zhenhua , Chen Peng , Mao Zhihua , Yuan Dapeng
Published: September 2021

An effective lidar simulator is vital for its system design and processing algorithms. However, laser transmission is a complex process due to the effects of sea surface and various interactions in seawater such as absorption, scattering, and so on. It is sophisticated and difficult for multiple scattering to accurately simulate. In this study, a multiple-scattering lidar model based on multiple-forward-scattering-single-backscattering approximation for oceanic lidar was proposed. Compared with previous analytic models, this model can work without assuming a homogeneous water and fixed scattering phase function. Besides, it takes consideration of lidar system and environmental parameters including receiver field of view, different scattering phase functions, particulate sizes, stratified water, and rough sea surface. One should note that because the scattering phase function is difficult to determine accurately, the simulation accuracy may be reduced in a complex oceanic environment. The Cox–Munk model used in our method simulates capillarity waves but ignores gravity waves, and the pulse stretching is not included. The wide-angle scattering occurs in the dense subsurface phytoplankton, which sometimes makes it hard to use this model. In this study, we firstly derived this method based on an analytical solution by convolving Gaussians of the forward-scattering contribution of layer dr and the energy density at R in the small-angle-scattering approximation. Then, the effects of multiple scattering and water optical properties were analyzed using the model. Meanwhile, the validation with Monte Carlo model was implemented. Their coefficient of determination is beyond 0.9, the RMSE is within 0.02, the MAD is within 0.02, and the MAPD is within 8%, which indicates that our model is efficient for oceanographic lidar simulation. Finally, we studied the effects of FOV, SPF, rough sea surface, stratified water, and particle size. These results can provide reference for the design of the oceanic lidar system and contribute to the processing of lidar echo signals.
Emerging Sensor Platforms Allow for Seagrass Extent Mapping in a Turbid Estuary and from the Meadow to Ecosystem Scale
Authors: Krause Johannes R. , Hinojosa-Corona Alejandro , Gray Andrew B. , Burke Watson Elizabeth
Published: September 2021

Seagrass meadows are globally important habitats, protecting shorelines, providing nursery areas for fish, and sequestering carbon. However, both anthropogenic and natural environmental stressors have led to a worldwide reduction seagrass habitats. For purposes of management and restoration, it is essential to produce accurate maps of seagrass meadows over a variety of spatial scales, resolutions, and at temporal frequencies ranging from months to years. Satellite remote sensing has been successfully employed to produce maps of seagrass in the past, but turbid waters and difficulty in obtaining low-tide scenes pose persistent challenges. This study builds on an increased availability of affordable high temporal frequency imaging platforms, using seasonal unmanned aerial vehicle (UAV) surveys of seagrass extent at the meadow scale, to inform machine learning classifications of satellite imagery of a 40 km2 bay. We find that object-based image analysis is suitable to detect seasonal trends in seagrass extent from UAV imagery and find that trends vary between individual meadows at our study site Bahía de San Quintín, Baja California, México, during our study period in 2019. We further suggest that compositing multiple satellite imagery classifications into a seagrass probability map allows for an estimation of seagrass extent in turbid waters and report that in 2019, seagrass covered 2324 ha of Bahía de San Quintín, indicating a recovery from losses reported for previous decades.
Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain
Authors: Sobrino José Antonio , Souza da Rocha Nájila , Skoković Drazen , Suélen Käfer Pâmela , López-Urrea Ramón , Jiménez-Muñoz Juan Carlos , Alves Rolim Silvia Beatriz
Published: September 2021

Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm/h and 0.09 mm/h and 1.11 mm/day and 0.63 mm/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes.
Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods
Authors: Li Yingjun , Zhang Wenpeng , Tian Biao , Lin Wenhao , Liu Yongxiang
Published: September 2021

RCS reconstruction is an important way to reduce the measurement time in anechoic chambers and expand the radar original data, which can solve the problems of data scarcity and a high measurement cost. The greedy pursuit, convex relaxation, and sparse Bayesian learning-based sparse recovery methods can be used for parameter estimation. However, these sparse recovery methods either have problems in solving accuracy or selecting auxiliary parameters, or need to determine the probability distribution of noise in advance. To solve these problems, a non-parametric Sparse Iterative Covariance Estimation (SPICE) algorithm with global convergence property based on the sparse Geometrical Theory of Diffraction (GTD) model (GTD–SPICE) is employed for the first time for RCS reconstruction. Furthermore, an improved coarse-to-fine two-stage SPICE method (DE–GTD–SPICE) based on the Damped Exponential (DE) model and the GTD model (DE–GTD) is proposed to reduce the computational cost. Experimental results show that both the GTD–SPICE method and the DE–GTD–SPICE method are reliable and effective for RCS reconstruction. Specifically, the DE–GTD–SPICE method has a shorter computational time.
Retrieval of Daily Mean Top-of-Atmosphere Reflected Solar Flux Using the Advanced Very High Resolution Radiometer (AVHRR) Instruments
Authors: Akkermans Tom , Clerbaux Nicolas
Published: September 2021

The records of the Advanced Very High Resolution Radiometer (AVHRR) instrument observations can resolve the current lack of a long global climate data record of Reflected Solar Flux (RSF), by transforming these measurements into broadband flux at the top-of-atmosphere. This paper presents a methodology for obtaining daily mean RSF (Wm−2) from AVHRR. First, the narrowband reflectances are converted to broadband reflectance using empirical regressions with the Clouds and the Earth’s Radiant Energy System (CERES) observations. Second, the anisotropy is corrected by applying Angular Distribution Models (ADMs), which convert directional reflectance into a hemispherical albedo. Third, the instantaneous albedos are temporally interpolated by a flexible diurnal cycle model, capable of ingesting any number of observations at any time of day, making it suitable for any orbital configuration of NOAA and MetOp satellites. Finally, the twilight conditions prevailing near sunrise and sunset are simulated with an empirical model. The entire day is then integrated into a single daily mean RSF. This paper furthermore demonstrates the methodology by validating a full year (2008) of RSF daily means with the CERES SYN1deg data record, both on daily and subdaily scale. Several configurations are tested, each excluding particular satellites from the constellation in order to mimic orbital changes (e.g., orbital drift), and to assess their relative importance to the daily mean RSF. The best performance is obtained by the combination of at least one mid-morning (NOAA-17 or MetOp-A) and one early afternoon (NOAA-18) orbit. In this case, the RMS difference with CERES is about 7 Wm−2. Removing NOAA-18 degrades the performance to an RMS difference of 12 Wm−2, thereby providing an estimate of the impact of NOAA-19’s orbital drift between 2016 and 2020. Very early or late observations (NOAA-15, NOAA-16) provide little added value, and both mid-morning orbits turn out to be almost interchangeable given their close temporal proximity.
Design and Implementation of an Atmospheric Anion Monitoring System Based on Beidou Positioning
Authors: Wang Jinhu , Xie Binze , Cai Jiahan , Wang Yuhao , Chen Jiang , Abro Muhammad Ilyas
Published: September 2021

Atmospheric oxygen anions play an important role in medical health, clinical medicine, environmental health, and the ecological environment. Therefore, the concentration of atmospheric anions is an important index for measuring air quality. This paper proposes a monitoring system for atmospheric oxygen anions based on Beidou positioning and unmanned vehicles. This approach combines Beidou positioning technology, 4G pass-through, the unmanned capacitance suction method, electromagnetic field theory, and atmospheric detection technology. The proposed instrument can monitor the overall negative oxygen ion concentration, temperature, and humidity in a certain region over time and provide data visualization for the concentration of negative oxygen ions.
Fabrication of Humidity-Resistant Optical Fiber Sensor for Ammonia Sensing Using Diazo Resin-Photocrosslinked Films with a Porphyrin-Polystyrene Binary Mixture
Authors: Ahmed Soad , Park Yeawon , Okuda Hirofumi , Ono Shoichiro , Korposh Sergiy , Lee Seung-Woo
Published: September 2021

Ammonia gas sensors were fabricated via layer-by-layer (LbL) deposition of diazo resin (DAR) and a binary mixture of tetrakis(4-sulfophenyl)porphine (TSPP) and poly(styrene sulfonate) (PSS) onto the core of a multimode U-bent optical fiber. The penetration of light transferred into the evanescent field was enhanced by stripping the polymer cladding and coating the fiber core. The electrostatic interaction between the diazonium ion in DAR and the sulfonate residues in TSPP and PSS was converted into covalent bonds using UV irradiation. The photoreaction between the layers was confirmed by UV-vis and Fourier transform infrared spectroscopy. The sensitivity of the optical fiber sensors to ammonia was linear when exposed to ammonia gases generated from aqueous ammonia solutions at a concentration of approximately 17 parts per million (ppm). This linearity extended up to 50 ppm when the exposure time (30 s) was shortened. The response and recovery times were reduced to 30 s with a 5-cycle DAR/TSPP+PSS (as a mixture of 1 mM TSPP and 0.025 wt% PSS in water) film sensor. The limit of detection (LOD) of the optimized sensor was estimated to be 0.31 ppm for ammonia in solution, corresponding to approximately 0.03 ppm of ammonia gas. It is hypothesized that the presence of the hydrophobic moiety of PSS in the matrix suppressed the effects of humidity on the sensor response. The sensor response was stable and reproducible over seven days. The PSS-containing U-bent fiber sensor also showed superior sensitivity to ammonia when examined alongside amine and non-amine analytes.
Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation
Authors: Jiang Yun , Yao Huixia , Tao Shengxin , Liang Jing
Published: September 2021

Segmentation of retinal vessels is a critical step for the diagnosis of some fundus diseases. Methods: To further enhance the performance of vessel segmentation, we propose a method based on a gated skip-connection network with adaptive upsampling (GSAU-Net). In GSAU-Net, a novel skip-connection with gating is first utilized in the extension path, which facilitates the flow of information from the encoder to the decoder. Specifically, we used the gated skip-connection between the encoder and decoder to gate the lower-level information from the encoder. In the decoding phase, we used an adaptive upsampling to replace the bilinear interpolation, which recovers feature maps from the decoder to obtain the pixelwise prediction. Finally, we validated our method on the DRIVE, CHASE, and STARE datasets. Results: The experimental results showed that our proposed method outperformed some existing methods, such as DeepVessel, AG-Net, and IterNet, in terms of accuracy, F-measure, and AUCROC. The proposed method achieved a vessel segmentation F-measure of 83.13%, 81.40%, and 84.84% on the DRIVE, CHASE, and STARE datasets, respectively.
Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers
Authors: Carvalho Inês Alves , Silva Nuno Azevedo , Rosa Carla C. , Coelho Luís C. C. , Jorge Pedro A. S.
Published: September 2021

The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.
A Method of Implementing a 4 × 4 Correlation Matrix for Evaluating the Uplink Channel Properties of MIMO Over-the-Air Apparatus
Authors: Honda Kazuhiro
Published: September 2021

This paper presents a method of implementing a 4 × 4 correlation matrix for evaluating the uplink channel properties of multiple-input multiple-output (MIMO) antennas using an over-the-air measurement system. First, the implementation model used to determine the correlation coefficients between the signals received at the base station (BS) antennas via the uplink channel is described. Then, a methodology is introduced to achieve a 4 × 4 correlation matrix for a BS MIMO antenna based on Jakes’ model by setting the initial phases of the secondary wave sources in the two-dimensional channel model. The performance of the uplink channel for a four-element MIMO terminal array antenna is evaluated using a two-dimensional bidirectional fading emulator. The results show that the measured correlation coefficients between the signals received via the uplink channel at the BS antennas using the proposed method are in good agreement with the BS correlation characteristics calculated using Monte Carlo simulation and the theoretical formula, thereby confirming the effectiveness of the proposed method.
Application of Chemical Sensors and Olfactometry Method in Ecological Audits of Degraded Areas
Authors: Kulig Andrzej , Szyłak-Szydłowski Mirosław , Wiśniewska Marta
Published: September 2021

Mineral excavation is a common process throughout the world. The open pits remaining after the closure of a mine require well-considered and meticulous reclamation activities aimed at restoring the environmental properties of a given area. The inspections carried out in Poland indicate numerous irregularities in implementing the reclamation process. The research in this study was conducted in six measurement series and includes both chemical and olfactometry determinations by devices: multisensor portable gas detector and field olfactometer. Statistical analysis of the results obtained show high concentrations in ambient air of both chemical compounds (NH3, VOCs, H2S, CH3SH) and odour, excluding the possibility of occurrence in the pit of only waste types contained in the administrative decision on reclamation. In addition to the unpleasant odour, the listed compounds can have dangerous effects on the health and life of living organisms. This paper presents a suitable method of control and detection of irregularities in the conducted processes. The main advantage is the relatively low cost of purchasing sensors and field olfactometers compared to other devices, and the possibility to test the polluted air in situ, without the risk of chemical processes occurring during transport of gas samples to the laboratory.
Design and Implementation of Morphed Multi-Rotor Vehicles with Real-Time Obstacle Detection and Sensing System
Authors: Shiferaw Aleligne Yohannes , Esakki Balasubramanian , Pari Tamilarasan , Elumalai Elangovan , Mobayen Saleh , Bartoszewicz Andrzej
Published: September 2021

Multirotor unmanned aerial vehicles (MUAVs) are becoming more prominent for diverse real-world applications due to their inherent hovering ability, swift manoeuvring and vertical take-off landing capabilities. Nonetheless, to be entirely applicable for various obstacle prone environments, the conventional MUAVs may not be able to change their configuration depending on the available space and perform designated missions. It necessitates the morphing phenomenon of MUAVS, wherein it can alter their geometric structure autonomously. This article presents the development of a morphed MUAV based on a simple rotary actuation mechanism capable of driving each arm’s smoothly and satisfying the necessary reduction in workspace volume to navigate in the obstacle prone regions. The mathematical modelling for the folding mechanism was formulated, and corresponding kinematic analysis was performed to understand the synchronous motion characteristics of the arms during the folding of arms. Experiments were conducted by precisely actuating the servo motors based on the proximity ultrasonic sensor data to avoid the obstacle for achieving effective morphing of MUAV. The flight tests were conducted to estimate the endurance and attain a change in morphology of MUAV from “X-Configuration” to “H-Configuration” with the four arms actuated synchronously without time delay.
Effects of Ethanol on the Supercritical Carbon Dioxide Extraction of Cannabinoids from Near Equimolar (THC and CBD Balanced) Cannabis Flower
Authors: Qamar Sadia , Torres Yady , Parekh Harendra , Falconer James
Published: September 2021

In this study, supercritical carbon dioxide (scCO2) extractions of cannabinoids were conducted at four different densities (231, 590, 818, and 911 kg/m3) using ethanol (5% w/v) as a co-solvent. The chemical profiles of these cannabinoids were analysed via reverse-phase high-performance liquid chromatography (RP-HPLC). It was determined that scCO2, at low density (231 kg/m3), produced an extract yield of 6.1% w/v. At high scCO2 density (~818 kg/m3), the yield was 16.1% w/v. More specifically, the amounts of tetrahydrocannabinol (THC) and cannabidiol (CBD) in the scCO2 extract at 818 kg/m3 were 10.8 and 15.6% w/v, respectively. It was also found that the use of 5% w/v ethanol increased scCO2 extract yields at both low and high densities (7.6% w/v and 18.2% w/v, respectively). Additionally, the use of co-solvent increased this yield further under both low- and high-density conditions, to 13.7 and 19.1% w/v, respectively. Interestingly, higher scCO2 density (911 kg/m3) with and without ethanol did not improve the scCO2 extract yield or the amount of cannabinoids. Although this study provides new insights into the correlation between scCO2 density and ethanol co-extraction of CBD and THC, more studies are needed to determine how different scCO2 densities and co-solvents influence the extraction of cannabinoids.
Characteristics of Heavy Metals in Seawater and Sediments from Daya Bay (South China): Environmental Fates, Source Apportionment and Ecological Risks
Authors: Tao Wei , Li Haidong , Peng Xiaojuan , Zhang Wanping , Lou Quansheng , Gong Jian , Ye Jianjun
Published: September 2021

In this study, the spatiotemporal distributions, potential sources, and ecological risks of Hg, Cr, and As in seawater, and Hg, As, Zn, Cd, Pb, and Cu in sediments from Daya Bay were investigated. The five-year average concentrations of Hg, Cr, and As in seawater were 0.020 μg/L, 0.79 μg/L, and 2.08 μg/L, respectively. The five-year average concentrations of Hg, As, Zn, Cd, Pb, and Cu in surface sediments were 0.04 mg/kg, 7.34 mg/kg, 63.81 mg/kg, 0.23 mg/kg, 25.60 mg/kg, and 11.78 mg/kg, respectively. Annual variations in Hg, Cr, and As in seawater exhibited different trends. HMs in sediments, such as As, Zn, Pb, and Cu, exhibited similar annual variations, whereas Hg and Cd exhibited different annual variations. The spatial distribution of metal species in seawater and sediments showed significant variability, and the concentrations decreased gradually from the coast to the open sea. The comprehensive potential ecological hazard index (RI) of HMs in sediments indicated a relatively high risk, especially for Hg and Cd contamination. The geoaccumulation indices (Igeo) of As, Zn, Pb, and Cu suggested that these metals did not pollute Daya Bay, whereas those of Cd and Hg indicated mild and moderate pollution. The environmental fates of HMs were discussed based on Pearson correlation analysis, revealing that concentrations of HMs were greatly affected by parameters, such as pH, salinity, dissolved oxygen (DO), and total organic carbon (TOC). Principal component and factor analyses indicated that Hg, Cr, As, and dissolved inorganic nitrogen (DIN) in water originated from similar sources, including domestic sewage and wastewater from fishing ports, runoffs, and outlets. For sediments, it was proposed that Cu, Zn, As, Pb, and TOC exhibited similar sources, including cage culture and waste discharge from outlets. Meanwhile, Hg and Cd originated from other point sources, such as a harbor. The study suggests that sustainable management and economic development be integrated to control pollutant emissions in Daya Bay.
Bio-Cementation for Improving Soil Thermal Conductivity
Authors: Cheng Liang , Afur Natalia , Shahin Mohamed A
Published: September 2021

A promising technology for renewable energy is energy piles used to heat and cool buildings. In this research, the effects of bio-cementation via microbially induced calcite precipitation (MICP) using mixed calcium and magnesium sources and the addition of fibres on the thermal conductivity of soil were investigated. Firstly, silica sand specimens were treated with cementation solutions containing different ratios of calcium chloride and magnesium chloride to achieve maximum thermal conductivity improvement. Three treatment cycles were provided, and the corresponding thermal conductivity was measured after each cycle. It was found that using 100% calcium chloride resulted in the highest thermal conductivity. This cementation solution was then used to treat bio-cemented soil samples containing fibres, including polyethylene, steel and glass fibres. The fibre contents used included 0.5%, 1.0% and 1.5% of the dry sand mass. The results show that the glass fibre samples yielded the highest thermal conductivity after three treatment cycles, and SEM imaging was used to support the findings. This research suggests that using MICP as a soil improvement technique can also improve the thermal conductivity of soil surrounding energy piles, which has high potential to effectively improve the efficiency of energy piles.
Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale
Authors: Farhangi Farbod , Sadeghi-Niaraki Abolghasem , Razavi-Termeh Seyed Vahid , Choi Soo-Mi
Published: September 2021

Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.
Innovation as a Determinant of Growth in Outperforming Emerging Markets: An Analysis of South Korea
Authors: Hintringer Tina Maria , Bobek Vito , Milost Franko , Horvat Tatjana
Published: September 2021

Emerging economies and their speed of growth, competitiveness, and resilience are of great interest globally due to the high potential investors see in them. Innovation is one of the factors recognized to be the common ground of significantly outperforming economies. Therefore, identifying innovation benchmarks and how they impact economic success is relevant for a more straightforward evaluation of innovation in a country. This research focuses on the quantitative parts of innovation. Firstly, governmental interference, knowledge flows and networks, cultural and societal preconditions, and openness towards change are identified as notably relevant innovation enhancing factors in South Korea through case study analyses. Then, an analysis of the impact of quantitative innovation factors on the GDP in South Korea is conducted. The impact of quantifiable innovation factors, identified through literature review, on the GDP as the benchmark for economic growth is tested from 1995 until 2018 through a linear, multiple least squares regression to identify significant relationships between the chosen variables. Two out of five selected quantitative innovation factors have a statistically significant impact on the economic growth in the used model. The number of researchers per million people and the patent grants of residents is identified to be impactful innovation benchmarks.
Re-Commerce to Ensure Circular Economy from Consumer Perspective
Authors: Arman Saleh Md. , Mark-Herbert Cecilia
Published: September 2021

Responsible consumption practice is crucial to minimizing waste as part of sustainable development (SDG 12). This can be achieved by extending product life in a cradle-to-cradle system as part of a circular economy. However, are consumers willing to extend product life by reusing materials? The phenomenon of re-commerce, where consumers sell products to each other (C2C), takes place in physical as well as web-based markets. This project focuses on enabling factors for re-commerce practices on Facebook among consumers in Bangladesh. A review of existing literature provided grounds for an empirical focus group study of Bangladeshi consumers. Using a social practice theory perspective in a thematic analysis shows that enabling factors in terms of technical competence, context-bound conditions influencing meanings such as socially accepted procedures, and practical practices relating to materials such as payment forms and logistics support, all serve as enabling or hindering factors. The study contributes to the understanding of conditions for re-commerce practices as part of a circular economy system where consumers are encouraged to engage in responsible consumption by extending product life cycles.
Social Bonding and Public Trust/Distrust in COVID-19 Vaccines
Authors: Ramkissoon Haywantee
Published: September 2021

COVID-19 vaccine hesitancy has been a growing concern. The pandemic has proved to be very complicated with the mutated virus. The Delta variant is contributing to a surge of cases across the globe. Vaccine hesitancy can be socially contagious, requiring more stringent efforts from policy makers and health professionals in promoting vaccine uptake. Some evidence shows that vaccine acceptance appears to have played an integral role in successfully controlling the pandemic. Vaccination acceptance, however, demands that the public has a good understanding of the vaccine’s benefits in promoting healthier societies and people’s quality of life. Unclear COVID-19 vaccine information can lead to distrust in vaccines and vaccine hesitancy. It is of paramount importance to communicate clear and unbiased vaccine information to the public to encourage vaccine uptake. Word of mouth communication remains important to further promote COVID-19 vaccine uptake in the community. This short paper discusses the role of social bonds and public trust/distrust and word of mouth communication in vaccine decision making.
How Would We Cycle Today If We Had the Weather of Tomorrow? An Analysis of the Impact of Climate Change on Bicycle Traffic
Authors: Galich Anton , Nieland Simon , Lenz Barbara , Blechschmidt Jan
Published: September 2021

Bicycle usage is significantly affected by weather conditions. Climate change is, therefore, expected to have an impact on the volume of bicycle traffic, which is an important factor in the planning and design of bicycle infrastructures. To predict bicycle traffic in a changed climate in the city of Berlin, this paper compares a traditional statistical approach to three machine learning models. For this purpose, a cross-validation procedure is developed that evaluates model performance on the basis of prediction accuracy. XGBoost showed the best performance and is used for the prediction of bicycle counts. Our results indicate that we can expect an overall annual increase in bicycle traffic of 1–4% in the city of Berlin due to the changes in local weather conditions caused by global climate change. The biggest changes are expected to occur in the winter season with increases of 11–14% due to rising temperatures and only slight increases in precipitation.
Estimating the Circularity Performance of an Emerging Industrial Symbiosis Network: The Case of Recycled Plastic Fibers in Reinforced Concrete
Authors: Marinelli Simona , Butturi Maria Angela , Rimini Bianca , Gamberini Rita , Sellitto Miguel Afonso
Published: September 2021

In recent times, the construction industry has been handling circular economy strategies in order to face the most important challenges in the sector, namely the lack of raw materials and the environmental impacts derived from all the processes linked to the entire supply chain. The industrial symbiosis approach represents an effective strategy to improve the circularity of the construction industry. This study analyses the circularity performance of an emerging industrial symbiosis network derived from the production of a cement mortar reinforced with recycled synthetic fibers coming from artificial turf carpets. From the collection of artificial turf carpets at the end-of-life stage it is possible to recover several materials, leading to potential unusual interactions between industries belonging to different sectors. A suitable indicator, retrieved from the literature, the Industrial Symbiosis Indicator (ISI), has been used to estimate the level of industrial symbiosis associated with increasing materials recirculation inside the network. Four scenarios—ranging from perfect linearity to perfect circularity—representing growing circularity were tested. Findings demonstrate that the development of an effective industrial symbiosis network can contribute to improving the circular approach within the construction sector, reducing environmental and economic pressures.
Potential of Utilization of Renewable Energy Technologies in Gulf Countries
Authors: Basha J. Sadhik , Jafary Tahereh , Vasudevan Ranjit , Bahadur Jahanzeb Khan , Ajmi Muna Al , Neyadi Aadil Al , Soudagar Manzoore Elahi M. , Mujtaba MA , Hussain Abrar , Ahmed Waqar , Shahapurkar Kiran , Rahman S. M. Ashrafur , Fattah I. M. Rizwanul
Published: September 2021

This critical review report highlights the enormous potentiality and availability of renewable energy sources in the Gulf region. The earth suffers from extreme air pollution, climate changes, and extreme problems due to the enormous usage of underground carbon resources applications materialized in industrial, transport, and domestic sectors. The countries under Gulf Cooperation Council, i.e., Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, mainly explore those underground carbon resources for crude oil extraction and natural gas production. As a nonrenewable resource, these are bound to be exhausted in the near future. Hence, this review discusses the importance and feasibility of renewable sources in the Gulf region to persuade the scientific community to launch and explore renewable sources to obtain the maximum benefit in electric power generation. In most parts of the Gulf region, solar and wind energy sources are abundantly available. However, attempts to harness those resources are very limited. Furthermore, in this review report, innovative areas of advanced research (such as bioenergy, biomass) were proposed for the Gulf region to extract those resources at a higher magnitude to generate surplus power generation. Overall, this report clearly depicts the current scenario, current power demand, currently installed capacities, and the future strategies of power production from renewable power sources (viz., solar, wind, tidal, biomass, and bioenergy) in each and every part of the Gulf region.
Power Brand Defense Up, My Friend! Stimulating Brand Defense through Digital Content Marketing
Authors: Sawaftah Dima , Aljarah Ahmad , Lahuerta-Otero Eva
Published: September 2021

Digital content marketing that increases consumers’ favorable behavior is of increasing interest to marketers. However, there is a lack of studies that examine the relative effect of digital content marketing on brand defense. Building on the theoretical lens of elaboration likelihood model, attachment theory, and source credibility theory, this experimental study examines the relative effect of two types of digital content marketing on brand defense, taking into consideration the mediation effect of behavioral engagement and the moderation effect of age generation. Based on 237 participants collected from a United States sample, the findings of this study revealed that user-generated content is a stronger predictor of brand defense and behavioral engagement compared to firm-generated content. Further, behavioral engagement served as a mediator variable between the digital content marketing types and brand defense. Significant evidence has additionally been found between behavioral engagement and brand defense. Moreover, the findings of the moderation analysis illustrated that Generation Z is the most influenced by user-generated content, followed by Generations X and Y. Generation Y is the most influenced by firm-generated content, followed by Generations Z and X. This study adds empirical relevance to the growing literature of the importance of digital content marketing, behavioral engagement, and generation as well validates the effects of those constructs on brand defense.
Climate-Smart Agriculture: Sleeping Beauty of the Hungarian Agribusiness
Authors: Biró Kinga , Szalmáné Csete Mária , Németh Bálint
Published: September 2021

Climate change and agriculture interact with each other in many ways and have a complex impact. The agriculture sector needs to be prepared to adapt to the expected effects of climate change, and emphasis must be placed on prevention to achieve emission reduction targets. The research explores the current situation in the agricultural sector, the risks of climate change, as well as the Climate-Smart Agriculture (CSA) solutions that appear at Hungarian agribusiness. Examining the climate innovation activities of farms shows a coherence of the practical application. The research is based on a questionnaire survey of Hungarian agribusiness and the country directorates of the National Chamber of Agriculture. The investigation confirmed that although farmers are susceptible to Agri-Innovation, they only partly exploit the potential of digitalization. For agriculture, the spread of agricultural digitalization and technology transfer is an excellent opportunity to increase agricultural production, maintain and improve its market position, and increase its digital maturity, which can also enhance the integration of climate innovation and sustainability aspects supporting the green economic development.
Effect of Recycled Shell Waste as a Modifier on the High- and Low-Temperature Rheological Properties of Asphalt
Authors: Guo Yuchen , Wang Xuancang , Ji Guanyu , Zhang Yi , Su Hao , Luo Yaolu
Published: September 2021

The deteriorating ecological environment and the concept of sustainable development have highlighted the importance of waste reuse. This article investigates the performance changes resulting from the incorporation of shellac into asphalt binders. Seashell powder-modified asphalt was prepared with 5%, 10%, and 15% admixture using the high-speed shear method. The microstructure of the seashell powder was observed by scanning electron microscope test (SEM); the physical-phase analysis of the seashell powder was carried out using an X-ray diffraction (XRD) test; the surface characteristics and pore structure of shellac were analyzed by the specific surface area Brunauer-Emmett-Teller (BET) test; and Fourier infrared spectroscopy (FTIR) qualitatively analyzed the composition and changes of functional groups of seashell powder-modified asphalt. The conventional performance index of seashell powder asphalt was analyzed by penetration, softening point, and ductility (5 °C) tests; the effect of seashell powder on asphalt binder was studied using a dynamic shear rheometer (DSR) and bending beam rheometer (BBR) at high and low temperatures, respectively. The results indicate the following: seashell powder is a coarse, porous, and angular CaCO3 bio-material; seashell powder and the asphalt binder represent a stable physical mixture of modified properties; seashell powder improves the consistency, hardness, and high-temperature performance of the asphalt binder but weakens the low-temperature performance of it; seashell powder enhances the elasticity, recovery performance, and permanent deformation resistance of asphalt binders and improves high-temperature rheological properties; finally, seashell powder has a minimal effect on the crack resistance of asphalt binders at very low temperatures. In summary, the use of waste seashells for recycling as bio-modifiers for asphalt binders is a practical approach.