bluuebunny/crossref_metadata_2025 huggingface dataset:
Neighborhood Food Environment and Children’s BMI: A New Framework with Structural Equation Modeling
Authors: Abdumijit Tursunay , Zhao Dong , Zhang Ronghua
Published: November 2022

The relationship between neighborhood food environment and childhood obesity is complex and not yet well defined by current research in China, especially when considering the integrated effects with other relative factors. The main purpose of this article is to introduce a framework of children’s weight status, based on their neighborhood food environment, and to identify the impact of food environment on the children’s BMI and potential pathways. The participants of this cross-sectional study were students aged 8–16.5 years old and their parents. Two conceptual frameworks were tested using the structural equation modeling method, and two models were extracted. Model B added the neighborhood food environment based on model A. By comparing the two models, the neighborhood environment was potentially correlated with the children’s BMI directly and may have a positive impact on unhealthy-food eating behaviors, which were positively associated with the children’s BMI. The results suggest that the focus should be placed on the integrated effects of the potential risk factors of childhood obesity, based on considering the neighborhood food environment, which may relate to children’s unhealthy-food eating behaviors and weight status.
Device-Controlled Microcondensation for Spatially Confined On-Tissue Digests in MALDI Imaging of N-Glycans
Authors: Fülöp Annabelle , Marsching Christian , Barka Frederik , Ucal Yasemin , Pfänder Pauline , Opitz Christiane A. , Barka Günes , Hopf Carsten
Published: November 2022

On-tissue enzymatic digestion is a prerequisite for MALDI mass spectrometry imaging (MSI) and spatialomic analysis of tissue proteins and their N-glycan conjugates. Despite the more widely accepted importance of N-glycans as diagnostic and prognostic biomarkers of many diseases and their potential as pharmacodynamic markers, the crucial sample preparation step, namely on-tissue digestion with enzymes like PNGaseF, is currently mainly carried out by specialized laboratories using home-built incubation arrangements, e.g., petri dishes placed in an incubator. Standardized spatially confined enzyme digests, however, require precise control and possible regulation of humidity and temperature, as high humidity increases the risk of analyte dislocation and low humidity compromises enzyme function. Here, a digestion device that controls humidity by cyclic ventilation and heating of the slide holder and the chamber lid was designed to enable controlled micro-condensation on the slide and to stabilize and monitor the digestion process. The device presented here may help with standardization in MSI. Using sagittal mouse brain sections and xenografted human U87 glioblastoma cells in CD1 nu/nu mouse brain, a device-controlled workflow for MALDI MSI of N-glycans was developed.
Molecular Dynamics Simulation of Drug Solubilization Behavior in Surfactant and Cosolvent Injections
Authors: He Meiqi , Zheng Wenwen , Wang Nannan , Gao Hanlu , Ouyang Defang , Huang Zunnan
Published: November 2022

Surfactants and cosolvents are often combined to solubilize insoluble drugs in commercially available intravenous formulations to achieve better solubilization. In this study, six marketed parenteral formulations with surfactants and cosolvents were investigated on the aggregation processes of micelles, the structural characterization of micelles, and the properties of solvent using molecular dynamics simulations. The addition of cosolvents resulted in better hydration of the core and palisade regions of micelles and an increase in both radius of gyration (Rg) and the solvent accessible surface area (SASA), causing a rise in critical micelle concentration (CMC), which hindered the phase separation of micelles. At the same time, the presence of cosolvents disrupted the hydrogen bonding structure of water in solution, increasing the solubility of insoluble medicines. Therefore, the solubilization mechanism of the cosolvent and surfactant mixtures was successfully analyzed by molecular dynamics simulation, which will benefit future formulation development for drug delivery.
Development of Azo Dye Immobilized Poly (Glycidyl Methacrylate-Co-Methyl Methacrylate) Polymers Composites as Novel Adsorbents for Water Treatment Applications: Methylene Blue-Polymers Composites
Authors: El-Aassar Mohamed R. , Tamer Tamer M. , El-Sayed Mohamed Y. , Omer Ahmed M. , Althobaiti Ibrahim O. , Youssef Mohamed E. , Alolaimi Rawan F. , El-Agammy Emam F. , Alruwaili Manar S. , Mohy-Eldin Mohamed S.
Published: November 2022

Methylene blue azo dye (MB) immobilized onto Poly (glycidyl methacrylate-Co-methyl methacrylate), (PGMA-co-PMMA), and sulphonated Poly (glycidyl methacrylate-Co-methyl methacrylate), (SPGMA-co-PMMA), polymers composites have been developed as novel adsorbents for water treatment applications. The effect of copolymer composition and sulphonation on the MB content has been studied. Maximum MB content was correlated to the Polyglycidyl methacrylate content for both native and sulphonated copolymers. Furthermore, the effect of the adsorption conditions on the MB content was studied. Sulfonated Poly (glycidyl methacrylate; SPGMA) was the most efficient formed composite with the highest MB content. The developed composites’ chemical structure and morphology were characterized using characterization tools such as particle size, FTIR, TGA, and SEM analyses. The developed MB-SPGMA composite adsorbent (27 mg/g), for the first time, was tested for the removal of Cr (VI) ions and Mn (VII) metal ions from dichromate and permanganate contaminated waters under mild adsorption conditions, opening a new field of multiuse of the same adsorbent in the removal of more than one contaminants.
New Approach for Extrusion Additive Manufacturing of Soft and Elastic Articles from Liquid-PVC-Based Consumable Materials
Authors: Savchenko Bohdan , Sova Nadiya , Beloshenko Victor , Debeluy Bohdan , Slieptsov Aleksander , Vozniak Iurii
Published: November 2022

The article deals with the experimental development of a novel additive manufacturing (AM) process using a liquid consumable based on polyvinyl chloride plastisol. A conventional additive manufacturing system designed for deposition of melt filaments was converted to deposition of liquid material. Additive manufacturing with liquid plastisol enables the production of parts with low Shore A hardness and high ductility, surpassing the performance of the conventional filament process. The novel AM process enables the production of articles with a Shore A hardness of 5 to 60, and the mechanical properties of the additively manufactured articles are similar to those produced in the mold. This was achieved by varying the parameters of the AM process as well as the composition of the plastisol composition, including those filled with an inorganic filler. The application of different material distribution patterns also has a significant effect on the mechanical properties of the samples. A potential application of the investigated AM method was proposed and practically evaluated.
Development of Hybrid Composite Utilizing Micro-Cellulose Fibers Extracted from Date Palm Rachis in the Najran Region
Authors: Alshahrani Hassan , Alshammari Basheer A. , Shah Ahmer Hussain , Dayo Abdul Qadeer
Published: November 2022

Environmental effects can be reduced by using renewable resources in various applications. The date palm fibers (DPF) used in this study were extracted from waste date ranches of the Najran region by retting and manual peeling processes. The biocomposites were developed by reinforcing the silane-treated DPF (SDPF) at different wt.% in eugenol phthalonitrile (EPN) and difunctional benzoxazine (BA-a) copolymer. The impact strength, tensile, flexural, and dynamic mechanical properties and thermogravimetric analysis were evaluated to understand the mechanical, thermomechanical, and thermal properties. Results confirmed that 30 wt.% SDPF-reinforced poly (EPN/BA-a) composites produced the highest mechanical and thermomechanical properties, and were considered optimized SDPF reinforcement. Furthermore, hybrid composites with 30 wt.% SDPF and 15 wt.% silane-treated glass fibers (SGF) reinforcement having different lamination sequences were also studied. The lamination sequences showed a significant impact on the mechanical and thermomechanical properties, as properties were further enhanced by adding a core layer of SGF in hybrid composites. However, the thermal properties of SDPF/SGF laminates were higher than SDPF biocomposites, but the SGF lamination sequence did not produce any impact. According to the limiting oxygen and heat resistance indexes, the developed SDPF/SGF laminates are self-extinguishing materials and can be used in temperature-tolerant applications up to 230 °C.
Biodegradable Dissolved Organic Carbon (BDOC) Removal from Micro-Polluted Water Source Using Ultrafiltration: Comparison with Conventional Processes, Operation Conditions and Membrane Fouling Control
Authors: Chen Ming , Shen Shuhuai , Zhang Fan , Zhang Cong , Xiong Jianglei
Published: November 2022

The biodegradable dissolved organic carbon (BDOC) in micro-polluted water sources affects the drinking water quality and safety in the urban water supply. The conventional technology of “coagulation-sedimentation-filtration” in a water plant located in the lower reaches of the Yangtze River removed dissolved organic carbon (DOC) with a molecular weight (MW) > 30 kDa effectively, but the BDOC elimination only ranged 27.4–58.1%, due to their predominant smaller MW (<1 kDa), leading to a high residual BDOC of 0.22–0.33 mg/L. To ensure the biological stability of drinking water, i.e., the inability to support microbial growth (BDOC < 0.2 mg/L), a pilot-scale ultrafiltration process (UF, made of aromatic polyamide with MW cut-off of 1 kDa) was operated to remove BDOC as an advanced treatment after sand-filtration. Results showed the membrane flux decreased with the increase in the influent BDOC concentration and decrease in operating pressure. With an operating pressure of 0.25 MPa, the BDOC removal by UF reached 80.7%, leading to a biologically stable BDOC concentration of 0.08 mg/L. The fouling of the membrane was mainly caused by organic pollution. The H2O2–HCl immersion washing method effectively cleaned the membrane surface fouling, with a recovery of membrane flux of 98%.
The 2022 Mw 6.1 Pasaman Barat, Indonesia Earthquake, Confirmed the Existence of the Talamau Segment Fault Based on Teleseismic and Satellite Gravity Data
Authors: Dewanto Bondan Galih , Priadi Ramadhan , Heliani Leni Sophia , Natul Al Shida , Yanis Muhammad , Suhendro Indranova , Julius Admiral Musa
Published: November 2022

A Mw 6.1 earthquake on 25 February 2022, at around 8:39 a.m. local time, struck Pasaman Barat Regency, West Sumatra, Indonesia, and was felt in Singapore and Malaysia. The hypocenter of this earthquake was 12 km deep and preceded by an Mw 4.9 foreshock a few minutes earlier. The earthquakes originated on a blind fault and triggered a landslide at Mount Talamau. Herein, the slip distribution and asperities along the plane fault during the earthquake were examined by teleseismic inversion and the fault location was identified by Global Gravity Model plus (GGMPlus) satellite gravity data. The slip distribution was calculated from the source parameters (strike: 136°; dip: 70°; rake: 174°) by inversion techniques based on teleseismic data. Based on the slip distribution, the earthquake was generated by stress from the Sianok fault that spread to the north and reached the uncertain fault segment in the Talamau area. In addition, the results of the First Horizontal Derivative and Second Vertical Derivative from the GGMplus data revealed a straight Simple Bouguer Anomaly pattern, confirming the existence of the uncertain Talamau fault as part of the Great Sumatra Fault. This work shows the potential application of the combination of teleseismic and gravity observation for delineating the fault structure that caused the 2022 Mw 6.1 Pasaman earthquake, which can also be applied to other locations of similar geological backgrounds.
Hindu Deities in the Flesh: “Hot” Emotions, Sensual Interactions, and (Syn)aesthetic Blends
Authors: Lange Gerrit
Published: November 2022

In Hindu practices and narratives, otherworldly and nonhuman beings appear with nonhuman and otherworldly bodies and feelings. In this article, I draw from ethnographic fieldwork as well as from philology to outline the widespread perception of divine presence or emotion as “heat”. This embodied idea or multi-sensual “aesthetic blend”, as I propose to call it, can be found in very diverse cultural and historical traditions of South Asia. It is more than a concept, a “mapping” or a metaphor, insofar as it informs how people not only think of, but sensually encounter the bodies of goddesses and gods. By adding this new term to the vocabulary of the Study of Religions, I intend to build upon the focus on embodied, enacted and situated religion, as it has become prominent within the discipline, to see the seemingly disembodied texts and stories in a new light. Does, in the end, the way divine bodies are imagined feed back into how humans conceptualize and feel their own bodies?
Is Theism Incompatible with the Pauline Principle?
Authors: Flannagan Matthew
Published: November 2022

This paper criticises James Sterba’s use of the Pauline principle to formulate a logical version of the problem of evil. Sterba’s argument contains a crucial premise: If human agents are always prohibited from doing some action, God is also prohibited from doing that action. This implies that the Pauline principle applies to both Divine and human agents. I argue that any Theist who affirms a divine command theory of ethics can consistently and coherently deny this premise and its implication. If a divine command theory is coherent, a theist can affirm that the Pauline principle governs human agents’ actions but not God’s actions. I will also criticise Sterba’s criticisms of a divine command theory and argue that they fail.
Multi-Branch Adaptive Hard Region Mining Network for Urban Scene Parsing of High-Resolution Remote-Sensing Images
Authors: Bai Haiwei , Cheng Jian , Su Yanzhou , Wang Qi , Han Haoran , Zhang Yijie
Published: November 2022

Scene parsing of high-resolution remote-sensing images (HRRSIs) refers to parsing different semantic regions from the images, which is an important fundamental task in image understanding. However, due to the inherent complexity of urban scenes, HRRSIs contain numerous object classes. These objects present large-scale variation and irregular morphological structures. Furthermore, their spatial distribution is uneven and contains substantial spatial details. All these features make it difficult to parse urban scenes accurately. To deal with these dilemmas, in this paper, we propose a multi-branch adaptive hard region mining network (MBANet) for urban scene parsing of HRRSIs. MBANet consists of three branches, namely, a multi-scale semantic branch, an adaptive hard region mining (AHRM) branch, and an edge branch. First, the multi-scale semantic branch is constructed based on a feature pyramid network (FPN). To reduce the memory footprint, ResNet50 is chosen as the backbone, which, combined with the atrous spatial pyramid pooling module, can extract rich multi-scale contextual information effectively, thereby enhancing object representation at various scales. Second, an AHRM branch is proposed to enhance feature representation of hard regions with a complex distribution, which would be difficult to parse otherwise. Third, the edge-extraction branch is introduced to supervise boundary perception training so that the contours of objects can be better captured. In our experiments, the three branches complemented each other in feature extraction and demonstrated state-of-the-art performance for urban scene parsing of HRRSIs. We also performed ablation studies on two HRRSI datasets from ISPRS and compared them with other methods.
Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites
Authors: Zhuravlev Ruslan , Dara Andrey , Santos André Luís Diniz dos , Demidov Oleg , Burba George
Published: November 2022

Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.
Spatial and Temporal Characteristics of NDVI in the Weihe River Basin and Its Correlation with Terrestrial Water Storage
Authors: Wei Zhenzhen , Wan Xiaoyun
Published: November 2022

The Weihe River Basin (WRB) of China is located in an arid and water-scarce semi-arid region with a fragile ecological environment, and it is meaningful to study the spatial and temporal changes in vegetation and terrestrial water storage changes in a small-scale inland basin. This study uses normalized difference vegetation index (NDVI) data and Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) time-variable gravity field models to derive changes in vegetation cover and water storage in the WRB from 2002 to 2020. Firstly, taking NDVI as the breakthrough point, the temporal and spatial characteristics of vegetation were analyzed by trend analysis method and F-test. Then, GRACE and GRACE-FO were used to derive water storage variations. Finally, the correlation between NDVI and water storage variations is discussed using the Pearson correlation analysis. The results show that the overall trend of NDVI is increasing, and the increasing trend is more evident before 2014, and after that, there is a significant fluctuation. The spatial distribution shows a large spatial variability, but the growing area still accounts for the majority, and the change varies by vegetation type, among which the cultivated vegetation is more influential. The overall change in terrestrial water storage showed a decreasing trend of −0.09 cm/yr, and also reflected a solid intra-annual regular cycle, i.e., reaching a trough from October to November and a peak from May to June each year. The correlation is 0.6 on the time scale, and there was a 3-month lag between NDVI and TWS. On the spatial scale, the percentage of areas with a negative correlation was about 95.4%, probably due to increased water consumption and evapotranspiration. The study’s results can help to understand the relationship between vegetation and water storage in the WRB and provide scientific support for local environmental management.
Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images
Authors: Li Yuyang , Fu Bolin , Sun Xidong , Fan Donglin , Wang Yeqiao , He Hongchang , Gao Ertao , He Wen , Yao Yuefeng
Published: November 2022

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%.
Incidence Angle Normalization of Dual-Polarized Sentinel-1 Backscatter Data on Greenland Ice Sheet
Authors: Chen Xiao , Li Gang , Chen Zhuoqi , Ju Qi , Cheng Xiao
Published: November 2022

The backscatter coefficients of Synthetic Aperture Radar (SAR) images that observe the Greenland Ice Sheet (GrIS) are incidence angle dependent, which impedes subsequent applications, such as monitoring its surface melting. Therefore, backscatter intensities with varying incidence angles should be normalized. This study proposes an incidence angle normalization method for dual-polarized Sentinel-1 images for GrIS. A multiple linear regression model is trained using the ratio between the backscatter coefficient differences and the incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include the geographical position and elevation. The precision evaluation to the ascending and descending images suggests better normalization results than the widely used cosine-square correction method for horizontal transmit and horizontal receive (HH) images and a slight improvement for horizontal transmit and vertical receive (HV) images. Another dataset of GrIS Sentinel-1 mosaics in four 6-day repeating periods in 2020 is also tested to evaluate the proposed method and yields similar results. For HH images, the proposed method performs better than the cosine-square method, reducing 0.34 dB RMSE on average. The overall accuracy of our proposed method is 0.77 and 0.75 dB for HH and HV images, respectively. The proposed incidence angle normalization method can benefit the application of wide-swath SAR images to the study of large-scale and long-period observation on GrIS.
A Spatio-Temporal Graph Convolutional Network Model for Internet of Medical Things (IoMT)
Authors: Ghosh Dipon Kumar , Chakrabarty Amitabha , Moon Hyeonjoon , Piran M. Jalil
Published: November 2022

In order to provide intelligent and efficient healthcare services in the Internet of Medical Things (IoMT), human action recognition (HAR) can play a crucial role. As a result of their stringent requirements, such as high computational complexity and memory efficiency, classical HAR techniques are not applicable to modern and intelligent healthcare services, e.g., IoMT. To address these issues, we present in this paper a novel HAR technique for healthcare services in IoMT. This model, referred to as the spatio-temporal graph convolutional network (STGCN), primarily aims at skeleton-based human–machine interfaces. By independently extracting spatial and temporal features, STGCN significantly reduces information loss. Spatio-temporal information is extracted independently of the exact spatial and temporal point, ensuring the extraction of useful features for HAR. Using only joint data and fewer parameters, we demonstrate that our proposed STGCN achieved 92.2% accuracy on the skeleton dataset. Unlike multi-channel methods, which use a combination of joint and bone data and have a large number of parameters, multi-channel methods use both joint and bone data. As a result, STGCN offers a good balance between accuracy, memory consumption, and processing time, making it suitable for detecting medical conditions.
Uncovering the Deviation of Farmers’ Green Manure Planting Willingness and Behavior
Authors: Ren Jing , Li Fuduo , Yin Changbin , Zhang Jiudong
Published: November 2022

Planting green manure is an effective way to improve the agricultural environment and the quality of cultivated land in China. However, deviation from green manure planting willingness and behavior (DWB) becomes a serious obstacle to the promotion of green manure planting technology. For economic farmers, whether to plant green manure is a rational choice made after weighing up family endowments. In addition, ecological cognition plays a moderating role in the “willingness-behavior” transformation process of farmers’ green manure planting on the basis of family endowments. We selected four counties in which to conduct a questionnaire survey in Gansu and carried out interviews with 375 farmers. Based on the survey data, our study identified determinants that influence farmers’ DWB and examined the moderating effect of ecological cognition. In our paper, results show that the probability of farmers’ DWB is 41.87%. Farmers’ DWB is not only negatively affected by social network information and ecological compensation (eco-compensation) and positively influenced by the scale of cultivated land, but also restricted by human capital endowment characteristics such as age and education. In addition, ecological cognition played a significant moderating effect on farmers’ DWB. Farmers with high ecological cognition were more aware and capable of promoting green manure planting intentions into practice. Furthermore, different groups of farmers had different characteristics of DWB. The findings are useful and helpful in better understand the influencing factors of farmers’ DWB for policy makers and managers and can provide some effective support for policies designed to encourage farmers to adopt more sustainable green manure.
Correction: Calisto Friant et al. Transition to a Sustainable Circular Plastics Economy in The Netherlands: Discourse and Policy Analysis. Sustainability 2022, 14, 190
Authors: Calisto Friant Martin , Lakerveld Dirkjan , Vermeulen Walter J. V. , Salomone Roberta
Published: November 2022

The authors would like to make the following corrections to the published paper [...]
Research on SMEs’ Reputation Mechanism and Default Risk Based on Investors’ Financial Participation
Authors: Li Xin , Tian Xiujuan
Published: November 2022

Small and micro enterprises (SMEs) play a significant role in the market economy. While online lending has brought financial inclusion for SMEs’ borrowers, it has also increased the default risk, which restricts the normative development of online lending. To explore the impact of the reputation mechanism on borrowers’ default behavior, this paper provides a theoretical model of asymmetric information dynamic games under the online lending mechanism and an empirical study, which takes the number of bidders that reflects the investors’ participation as a proxy variable for the reputation effect factor. The theoretical model showed the borrowers’ default behavior is effectively restrained by the increase in the reputation effect factor in the reputation mechanism, and the empirical study found that an increase in the number of bidders can significantly reduce the risk of borrowers’ default, which verifies the conclusion of the theoretical model.
A Cluster-Based Approach for Analysis of Injury Severity in Interstate Crashes Involving Large Trucks
Authors: Tahfim Syed As-Sadeq , Chen Yan
Published: November 2022

The significance of large trucks for the expansion and well-being of the economy is a well-established fact. However, crashes involving large trucks significantly threaten the overall safety on the roads. Moreover, a significant proportion of fatal crashes involving large trucks occurs on interstate roadways in the United States. However, not many studies have focused on the heterogeneous effects of the contributory factors on injury outcomes of interstate crashes involving large trucks. The current study explores the application of a k-prototypes clustering-based mixed logit model to identify and analyze the heterogeneous effects of contributory factors on injury outcomes in different scenarios of interstate crashes involving large trucks. Data from six years of crashes involving large trucks that occurred on interstate roadways in the state of Pennsylvania, US, were used in this study. The list of contributory factors included the following: drivers’ demographics and behaviors; crash characteristics; vehicle-related factors; location and roadway attributes; and environmental factors. The results indicated that some of the contributory factors were significant for all scenarios of interstate crashes involving large trucks. However, the magnitude of those factors’ effects varied across scenarios. Moreover, some of the contributory factors were exclusive to certain scenarios of interstate crashes involving large trucks. Lastly, the identification of random parameters in the cluster-based models indicated that a cluster-based mixed logit model is a more effective approach for accurately estimating the effects of contributory factors on injury outcomes in large-truck interstate crashes. The empirical findings of this study can be used to develop more robust traffic laws and safety measures to reduce the frequency and severity of injury in different scenarios of interstate crashes involving large trucks.
Does Gender Diversity and Experience Moderate the Impact of Tax Aggressiveness on Corporate Social Responsibility: A Study of UAE Listed Companies
Authors: Elouaer Nadia , Waheed Rida , Sarwar Suleman , Aziz Ghazala
Published: November 2022

The purpose of this paper is to explore the moderating role of gender diversity in corporate board and CEO experience in terms of the relationship between tax aggressiveness and corporate social responsibility of UAE-listed companies. By applying correlation and regression analysis on a data set of 55 firms from 2014 to 2020, it is found that board gender diversity does not moderate the relationship between tax aggressiveness and CSR. However, a female CEO positively moderates this relationship. It is also found that CEO experience negatively moderates the relationship between tax aggressiveness and CSR. It is recommended that policymakers improve checks and balances so that male dominance can be reduced to give women opportunities to be involved in decision-making.
Postgraduate Trends in the Training of Human Talent for Sustainable Development
Authors: Acevedo-Duque Ángel , Prado-Sabido Tohtli , García-Salirrosas Elizabeth Emperatriz , Fernández Mantilla Mirtha Mercedes , Vera Calmet Velia Graciela , Valle Palomino Nicolás , Aguilar Armas Haydee Mercedes
Published: November 2022

A sustainable university must educate its academic collaborators and its interest groups, focusing on the key dimensions, such as economic growth, respect for the environment and carrying out energy saving measures, and efficiency and reuse of resources. The aim of this research is to show the tendencies of postgraduate courses in the training of human talent for “Sustainable Development” based on grounded theory. The methodology is approached from the qualitative naturalistic perspective, where the hermeneutic analysis procedure is structured in five phases with data from nine countries obtained from 20 interviews conducted in the year 2021. For this, a dialogical intervention has been used through the complementary experiences of rectors, deans and specialists with doctoral degrees who are immersed in postgraduate programmes in Latin American and Spanish universities, safeguarding the identity under ethical criteria. The main findings show that this human capital that forms part of the universities through their postgraduate programmes is currently considering redesigning their educational programmes to contribute to sustainable development, not only academically but also through their educational model. The main evidence shows that today more than ever there is a need for change, a need for a new human resource that responds and aims for a triple balance: environmental, economic and social.
Does Stronger Protection of Intellectual Property Improve Sustainable Development? Evidence from City Data in China
Authors: Mao Ke , Failler Pierre
Published: November 2022

Achieving sustainable development goals is a challenge for countries. The core way is to enhance the green total factor productivity. While the literature has examined the various external institutional factors, there is a lack of research on the impact of intellectual property protection (IPP), which is an important external institution. This study adopts the differences-in-differences (DID) model and propensity scores matching (PSM) using the Chinese intellectual property model city policy (IPMP), as a quasi-natural experiment, and Chinese cities’ panel data from 2005 to 2019 to investigate the effect of IPP on sustainable development. The findings demonstrate that: (1) The IPMP significantly increases urban GTFP. (2) Mediation mechanism analyses show that the IPMP can support urban GTFP by fostering technological advancement, boosting human capital, luring foreign direct investment, and modernizing industrial structure. (3) Heterogeneity analysis shows that the Chinese central region, the eastern region, and the region with more fiscal transparency are where the IPMP has the greatest promotion effect on GTFP. Lastly, this study provides several recommendations for the improvement of sustainability in China.
Comprehensive Review regarding the Profile of the Microplastic Pollution in the Coastal Area of the Black Sea
Authors: Savuca Alexandra , Nicoara Mircea Nicusor , Faggio Caterina
Published: November 2022

Lately, the interest in researching microplastics in the Black Sea has increased, highlighting areas of accumulation in which the amounts of microplastics are alarming, such as seafood consumed by the population. The Black Sea has special characteristics in terms of currents and wave dynamics that create opportunities for the accumulation of microplastics in hotspot points, such as in the context of large rivers discharge that contains large amounts of pollutants and new sources of contamination. This article offers a literature-based profile on plastic pollution in the Black Sea—pollution that originates in the discharge of large rivers, transportation, and other economic activities, even the COVID-19 pandemic—in order to highlight “microplastic hotspots” before the current political crisis that directly involves the Black Sea worsens.
A Data-Driven Approach to Analyze Mobility Patterns and the Built Environment: Evidence from Brescia, Catania, and Salerno (Italy)
Authors: De Vincentis Rosita , Karagulian Federico , Liberto Carlo , Nigro Marialisa , Rosati Vincenza , Valenti Gaetano
Published: November 2022

Investigating the correlation between urban mobility patterns and the built environment is crucial to support an integrated approach to transportation and land-use planning in modern cities. In this study, we aim to conduct a data-driven analysis of these two interrelated parts of the urban environment through the estimation of a set of metrics to assist city planners in making well-informed strategic decisions. Metrics are computed by aggregating and correlating different types of data sources. Floating Car Data (FCD) are used to compute metrics on mobility demand and traffic patterns. The built environment metrics are mainly derived from population and housing census data, as well as by investigating the topology and the functional classification adopted in the OpenStreetMap Repository to describe the importance and the role of each street in the overall network. Thanks to this set of metrics, accessibility indexes are then estimated to capture and explain the interaction between traffic patterns and the built environment in three Italian cities: Brescia, Catania, and Salerno. The results confirm that the proposed data-driven approach can extract valuable information to support decisions leading to more sustainable urban mobility volumes and patterns. More specifically, the application results show how the physical shape of each city and the related street network characteristics affect the accessibility profiles of different city zones and, consequently, the associated traffic patterns and travel delays. In particular, the combined analysis of city layouts, street network distributions, and floating car profiles suggests that cities such as Brescia, which is characterized by a homogeneously distributed radial street system, exhibit a more balanced spread of activities and efficient mobility behaviors.
Different Interspecies Demographic Histories within the Same Locality: A Case Study of Sea Cucumbers, Cuttlefish and Clams in Greek Waters
Authors: Feidantsis Konstantinos , Gkafas Georgios A. , Exadactylos Athanasios , Michaelidis Basile , Staikou Alexandra , Hatziioannou Marianthi , Apostologamvrou Chrysoula , Sarantopoulou Joanne , Vafidis Dimitris
Published: November 2022

Coalescent methods in population genetics aim to detect biodiversity patterns, evolutionary mechanisms, and signatures of historical changes in effective population sizes with respect to the species fidelity. Restriction site-associated DNA sequencing (RADseq) was used to evaluate the population dynamics of invertebrate species within the same localities. New sequencing technologies, such as the ones employed by population genetics, could be used to improve the management and sustainability of marine and aquaculture resources. Sea cucumbers (Holothuria tubolosa) showed genetic differentiation patterns favoring limited gene flow between studied areas. Similar results for clams (Venus verrucosa) suggest local adaptation and low-dispersal abilities for sessile organisms. On the contrary, cuttlefish (Sepia officinalis) exhibited a panmictic pattern, resulting in a single genetic stock in the area. The larvae settlement duration may be responsible for such interspecies variations. Interspecies demographic modeling revealed different environmental pressures of historical events’ signatures with respect to the three invertebrates. Sea cucumbers favor a post-glacial bottleneck event followed by a more recent recovery, whereas cuttlefish favor an expansion before the late glacial maximum. Lastly, clams showed a constant effective population size in the area. The results of historical demographic changes in natural populations provide opportunities for critical evaluation and management in terms of the conservation of the species in the area.
Detection Method of Crushing Mouth Loose Material Blockage Based on SSD Algorithm
Authors: Yao Jiang , Wang Zhiqiang , Liu Chunhui , Huang Guichen , Yuan Qingbo , Xu Kai , Zhang Wenhui
Published: November 2022

With the advancement of smart mines technology, unmanned and Shojinka have received widespread attention, among which unattended crushing station is one of the research directions. To realize unattended crushing station, first of all, it is necessary to detect loose material blockage at the crushing mouth. Based on deep learning (DL) and machine vision (MV) technology, an on-line detection method is studied to trace the blockage in a swift and accurate manner, so that the corresponding detection system can be designed accordingly. The charge coupled device (CCD) industrial camera set above the crushing mouth is used to collect images and input them to the edge computing equipment. The original Single Shot MultiBox Detector (SSD) preprocessing model is trained and optimized before it is combined with the MV technology to detect and then the MV technology is combined to detect whether the crushing mouth is covered. In Ansteel Group GUANBAOSHAN mine, the accuracy of recognition and detection system with human observation was examined for one month, and the tested accuracy is 95%. The experimental results show that the proposed method can detect the crushing mouth blockage in real time, which would solve the problem that the blockage can only be identified by human eyes in traditional method, and provides basic support for the unattended crushing station.
A ShK-like Domain from Steinernema carpocapsae with Bioinsecticidal Potential
Authors: Frias Jorge , Toubarro Duarte , Bjerga Gro Elin Kjæreng , Puntervoll Pål , Vicente João B. , Reis Rui L. , Simões Nelson
Published: November 2022

Entomopathogenic nematodes are used as biological control agents against a broad range of insect pests. We ascribed the pathogenicity of these organisms to the excretory/secretory products (ESP) released by the infective nematode. Our group characterized different virulence factors produced by Steinernema carpocapsae that underlie its success as an insect pathogen. A novel ShK-like peptide (ScK1) from this nematode that presents high sequence similarity with the ShK peptide from a sea anemone was successfully produced recombinantly in Escherichia coli. The secondary structure of ScK1 appeared redox-sensitive, exhibiting a far-UV circular dichroism spectrum consistent with an alpha-helical secondary structure. Thermal denaturation of the ScK1 allowed estimating the melting temperature to 59.2 ± 0.1 °C. The results from toxicity assays using Drosophila melanogaster as a model show that injection of this peptide can kill insects in a dose-dependent manner with an LD50 of 16.9 µM per adult within 24 h. Oral administration of the fusion protein significantly reduced the locomotor activity of insects after 48 h (p < 0.05, Tukey’s test). These data show that this nematode expresses insecticidal peptides with potential as next-generation insecticides.
Canine Coronavirus Activates Aryl Hydrocarbon Receptor during In Vitro Infection
Authors: Cerracchio Claudia , Serra Francesco , Amoroso Maria Grazia , Fiorito Filomena
Published: November 2022

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that interacts with substrates, including microbial metabolites. Recent advances reveal that AhR is involved in the host response to coronaviruses (CoVs) infection. Particularly, AhR antagonists decrease the expression of angiotensin-converting enzyme 2 (ACE2) via AhR up-regulation, resulting in suppression of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in mammalian cells. Herein, we report that AhR is expressed in canine fibrosarcoma (A72) cells, where it is considerably activated by infection with genotype II of canine coronavirus (CCoV-II). The pharmacological inhibition of AhR, by CH223191, suppressed cell death signs and increased cell viability. Furthermore, the AhR antagonist induced a meaningful decline in virus yield, accompanied by the inhibition of the expression of viral nuclear protein (NP). Fascinatingly, during CCoV infection, a novel co-expression of NP and AhR expression was found. Taken together, our preliminary findings show that infection with CCoV activates AhR, and pharmacologic AhR inhibition reduces CCoV replication, identifying AhR as a possible candidate target for CCoV antiviral therapy.
Review of the Distribution and Influence of Antibiotic Resistance Genes in Ballast Water
Authors: Guo Jiaqi , Jiang Bo , Sumita  , Wu Chengzhang , Zhang Yunshu , Li Cong
Published: November 2022

The misuse of antibiotics causes antibiotic resistance genes (ARGs) in bacteria to be gradually enriched by environmental selection, resulting in increased tolerance and resistance in bacteria to antibiotics. Ballast water is a mobile carrier for the global transfer of bacteria and genes, thus posing a certain risk of ARGs spreading into the global ocean. Therefore, it is important to investigate the current status of ARGs in ballast water, as well as control the abundance of ARGs. Herein, we attempt to comprehensively summarize the distribution and abundance of ARGs in ballast water from different sea areas and analyze the influencing factors (such as physical factors, chemical factors, temperature, pH, etc.) on the distribution of ARGs. Furthermore, we seek to review the changes in ARGs after differential disinfection technology treatment in ballast water (including chlorination, ultraviolet, ozone, and free radical technology), especially the enhancing effect of subinhibitory concentrations of disinfectants on ARGs transfer. Overall, we believe this review can serve as a guide for future researchers to establish a more reasonable standard of ballast water discharge that considers the pollution of ARGs and provide new insight into the risk of vertical and horizontal ARG transfer in ballast water after disinfection.
Pore Size Distribution Derived from Soil–Water Retention Characteristic Curve as Affected by Tillage Intensity
Authors: Jabro Jalal David , Stevens William Bart
Published: November 2022

Tillage practices can influence the pore size distribution (PSD) of the soil, affecting soil physical and hydraulic properties as well as processes that are essential for plant growth, soil hydrology, environmental studies and modeling. A study was conducted to assess the effect of no-tillage (NT) and conventional tillage (CT) on PSD derived from soil–water retention curves (SWRCs) using the van Genuchten’s equation (vG) at 0–15 cm and 15–30 cm depths in a sandy loam soil. Values of PSD or slopes (C(h)) were calculated from the SWRCs by differentiating the vG equation. Soil water retention curves under both tillage systems and within two depths were determined using the evaporation HYPROP method. The vG equation was well fitted to measured soil water retention data. The diameter (D) of soil pores retaining water at various matric suctions (|h|) of water in soils was calculated by the capillary equation. A significant effect of tillage on soil PSD was observed in the macro-pore (D > 1000 μm, at |h| < 3 hPa) and meso-pore (D between 10 and 1000 μm, at |h| between 300 and 3 hPa) size classes, while the micro-pores size class (D < 10 μm, at |h| > 300 hPa) was unaffected at the 0–15 and 15–30 cm depths. Larger values of C(h) or PSD in CT were associated with greater soil loosening induced by the CT operations and greater proportion of large pores (structural porosity) occurred in soils under CT compared to soils under NT. Macro-pore and meso-pore proportions were significantly greater in soils under CT than in soils under NT within both soil depths. The hydraulic parameters of the vG equation and its derivative function can be used to compare soil–water retention curves and pore size distributions between soils under untilled and tilled conditions.