bluuebunny/crossref_metadata_2025 huggingface dataset:
Bulgarian Medicinal Extracts as Natural Inhibitors with Antiviral and Antibacterial Activity
Authors: Nikolova Ivanka , Paunova-Krasteva Tsvetelina , Petrova Zdravka , Grozdanov Petar , Nikolova Nadya , Tsonev Georgi , Triantafyllidis Alexandros , Andreev Stoyan , Trepechova Madlena , Milkova Viktoria , Vilhelmova-Ilieva Neli
Published: June 2022

Background: Bulgaria is a country with a wide range of medicinal plants, with uses in traditional medicine dating back for centuries. Methods: Disc diffusion assay was used to evaluate the antimicrobial activity of the plant extracts. A cytopathic effect inhibition test was used for the assessment of the antiviral activity of the extracts. The virucidal activity of the extracts, their influence on the stage of viral adsorption, and their protective effect on uninfected cells were reported using the end-point dilution method, and Δlgs was determined as compared to the untreated controls. Results: The results of the study reveal that the antibacterial potential of G. glabra and H. perforatum extracts in Gram-positive bacteria is more effective than in Gram-negative bacteria. When applied during the replication of HSV-1 and HCov-OC-43, only some of the extracts showed weak activity, with SI between 2 to 8.5. Almost all tested extracts inhibited the extracellular virions of the studied enveloped viruses (HSV-1 and HCov-OC-43) to a greater extent than of the non-enveloped viruses (PV-1 and HAdV-5). They inhibited the stage of viral adsorption (HSV-1) in the host cell (MDBK) to varying degrees and showed a protective effect on healthy cells (MDBK) before they were subjected to viral invasion (HSV-1). Conclusion: The antipathogenic potential of extracts of H. perforatum and G. glabra suggests their effectiveness as antimicrobial agents. All 13 extracts of the Bulgarian medicinal plants studied can be used to reduce viral yield in a wide range of viral infections.
Phytochemical Profiling and Antibacterial Activity of Methanol Leaf Extract of Skimmia anquetilia
Authors: Nabi Masarat , Zargar Mohammed Iqbal , Tabassum Nahida , Ganai Bashir Ahmad , Wani Shahid Ud Din , Alshehri Sultan , Alam Prawez , Shakeel Faiyaz
Published: June 2022

Skimmia anquetilia is a plant species native to the Western Himalaya region that has tremendous potential for phytochemical activities. This study aimed to identify bioactive compounds and assess the antibacterial activity of S. anquetilia. To determine the major bioactive chemicals in the methanol leaf extract of S. anquetilia, we used a gas chromatography–mass spectrometer (GC-MS). The presence of 35 distinct phytoconstituents was discovered using GC-MS, which could contribute to the therapeutic capabilities of this plant species. The most predominant compound was 2R-acetoxymethyl-1,3,3-trimethyl-4t-(3-methyl-2-buten-1-yl)-1t-cyclohexanol (23.9%). Further, the leaf extract was evaluated for antibacterial activity against Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Salmonella typhi, and Staphylococcus aureus. The extract showed the highest zone of inhibition against E. coli (19 mm) followed by P. aeruginosa (18 mm) and K. Pneumoniae (17 mm) at 160 mg mL−1. The minimum inhibitory concentration (MIC) of methanol extract against the strain of P. aeruginosa (2 mg mL−1) demonstrated significant antibacterial activity. The findings of the present study highlight the potential of S. anquetilia for the development of herbal medicines for the treatment of various pathogenic infections.
Mechanical Characterization of Graphene—Hexagonal Boron Nitride-Based Kevlar–Carbon Hybrid Fabric Nanocomposites
Authors: Madarvoni Srivatsava , Sreekanth Rama P. S.
Published: June 2022

Polymer nanocomposites have been gaining attention in recent years. The addition of a low content of nanomaterials into the matrix improves mechanical, wear, thermal, electrical, and flame-retardant properties. The present work aimed to investigate the effect of graphene and hexagonal boron nitride nanoparticles on Kevlar and hybrid fiber-reinforced composites (FRP). Composites are fabricated with different filler concentrations of 0, 0.1, 0.3, and 0.5 wt.% by using a hand layup process. Characterizations like tensile, flexural, hardness, and impact strength were evaluated separately, heat deflection and viscosity properties of the epoxy composites. The dynamic viscosity findings indicated that a higher concentration of filler material resulted in nano-particle agglomeration. Graphene filler showed superior properties when compared to hexagonal boron nitride filler. Graphene showed optimum mechanical properties at 0.3 wt.%, whereas the hBN filler showed optimum properties at 0.5 wt.%. As compared to Kevlar composites, hybrid (carbon–Kevlar) composites significantly improved properties. As compared to graphene-filled composites, hexagonal boron nitride-filled composites increased scratch resistance. Digimat simulations were performed to validate experimental results, and it was observed that hybrid fabric composites exhibited better results when compared to Kevlar composites. The error percentage of all composites are within 10%, and it was concluded that hybrid–graphene fiber composites exhibited superior properties compared to Kevlar composites.
Influence of Electrical Heating Metal Mesh and Power Density on Resistance Welding of Carbon Fiber/PEEK Composite
Authors: Wei Donglu , Gu Yizhuo , Zhu Hanrui , Li Min , Wang Shaokai
Published: June 2022

An experimental investigation on the resistance welding of carbon-fiber-reinforced polyetheretherketone (PEEK) composite laminate using three types of stainless steel (SS) meshes with different sizes and electrical resistances as heating elements is reported. The objective of this study is to determine the influence of the metal mesh on the welding process and performance at different power densities ranging from 29 to 82 kW/m2. Resistance welding equipment is used to monitor the temperature and displacement along the thickness of the laminate. The results show that the power density determines the welding time and heat concentration. A large power density results in a short welding time, but also increases the temperature gradient at the joining interface (almost 50 °C) and causes an obvious deformation of a contraction of more than 0.1 mm along the thickness of the laminate. A SS mesh with low resistance has a strong welding capability, i.e., a high welding efficiency under low power density. A lap shear strength of approximately 35 MPa can be obtained with the appropriate power density. The shear strength is affected by the bonding between the metal mesh and polymer, the metal mesh load bearing, and the metal mesh size.
Optimization Design for the Centrifugal Pump under Non-Uniform Elbow Inflow Based on Orthogonal Test and GA_PSO
Authors: Yuan Ye , Jin Rong , Tang Lingdi , Lin Yanhua
Published: June 2022

The non-uniform inflow caused by the elbow inlet is one of the main reasons for the low actual operation performance of a centrifugal pump. Orthogonal experiment and GA_PSO algorithm are used to improve the head and efficiency of a centrifugal pump with an elbow inlet based on the method combining numerical simulation and prototype experiment in this paper. The effects of the design parameters, including elbow inlet radius ratio, blade inlet angle, blade number, blade wrap angle, blade outlet angle, impeller outlet diameter, blade outlet width and flow area ratio, on the pump head and efficiency are studied in the orthogonal experiment. The blade inlet angle is the major factor to match the non-uniform inflow and reduce the flow loss in the impeller inlet to contribute to enhancing the pump performance and cavitation characteristics. The particle swarm optimization (PSO) algorithm is optimized by integrating the genetic algorithm (GA), which ensures that the PSO-calculation result avoids falling into the local optimization and the global optimal solution is obtained as quickly as possible. The centrifugal pump with an elbow inlet is optimally designed by the GA_PSO algorithm. According to the performance test results, the efficiency of the optimized pump is 4.7% higher than that of the original pump.
Extraction of Water Body Information from Remote Sensing Imagery While Considering Greenness and Wetness Based on Tasseled Cap Transformation
Authors: Chen Chao , Chen Huixin , Liang Jintao , Huang Wenlang , Xu Wenxue , Li Bin , Wang Jianqiang
Published: June 2022

Water, as an important part of ecosystems, is also an important topic in the field of remote sensing. Shadows and dense vegetation negatively affect most traditional methods used to extract water body information from remotely sensed images. As a result, extracting water body information with high precision from a wide range of remote sensing images which contain complex ground-based objects has proved difficult. In the present study, a method used for extracting water body information from remote sensing imagery considers the greenness and wetness of ground-based objects. Ground objects with varied water content and vegetation coverage have different characteristics in their greenness and wetness components obtained by the Tasseled Cap transformation (TCT). Multispectral information can be output as brightness, greenness, and wetness by Tasseled Cap transformation, which is widely used in satellite remote sensing images. Hence, a model used to extract water body information was constructed to weaken the influence of shadows and dense vegetation. Jiangsu and Anhui provinces are located along the Yangtze River, China, and were selected as the research area. The experiment used the wide-field-of-view (WFV) sensor onboard the Gaofen-1 satellite to acquire remotely sensed photos. The results showed that the contours and spatial extent of the water bodies extracted by the proposed method are highly consistent, and the influence of shadow and buildings is minimized; the method has a high Kappa coefficient (0.89), overall accuracy (92.72%), and user accuracy (88.04%). Thus, the method is useful in updating a geographical database of water bodies and in water resource management.
Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season
Authors: Li Bingze , Ma Ming , Chen Shengbo , Li Xiaofeng , Chen Si , Zheng Xingming
Published: June 2022

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (R mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (RMSE) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the RMSE of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m2 to 4.24 kg/m2 (2.0% to 12.9% for mean absolute percentage error (MAPE)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (MAPE: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.
Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors
Authors: Yan Yang , Kayem Kader , Hao Ye , Shi Zhou , Zhang Chao , Peng Jie , Liu Weiyang , Zuo Qiang , Ji Wenjun , Li Baoguo
Published: June 2022

Accurate updating of soil salination and alkalization maps based on remote sensing images and machining learning methods plays an essential role in food security, biodiversity, and desertification. However, there is still a lack of research on using machine learning, especially one-dimensional convolutional neural networks (CNN)s, and soil-forming factors to classify the salinization and alkalization degree. As a case study, the study estimated the soil salination and alkalization by Random forests (RF) and CNN based on the 88 observations and 16 environmental covariates in Da’an city, China. The results show that: the RF model (accuracy = 0.67, precision = 0.67 for soil salination) with the synthetic minority oversampling technique performed better than CNN. Salinity and vegetation spectral indexes played the most crucial roles in soil salinization and alkalinization estimation in Songnen Plain. The spatial distribution derived from the RF model shows that from the 1980s to 2021, soil salinization and alkalization areas increased at an annual rate of 1.40% and 0.86%, respectively, and the size of very high salinization and alkalization was expanding. The degree and change rate of soil salinization and alkalization under various land-use types followed mash > salinate soil > grassland > dry land and forest. This study provides a reference for rapid mapping, evaluating, and managing soil salinization and alkalization in arid areas.
FIR, IIR and Wavelet Algorithms for the Rigorous Filtering of GOCE SGG Data to the GOCE MBW
Authors: Pitenis Eleftherios , Mamagiannou Elisavet , Natsiopoulos Dimitrios A. , Vergos Georgios S. , Tziavos Ilias N. , Grigoriadis Vassilios N. , Sideris Michael G.
Published: June 2022

Gravity field and steady-state Ocean Circulation Explorer (GOCE) data are strongly affected by noise and long-wavelength errors outside the satellite measurement bandwidth (MBW). One of the main goals in utilizing GOCE data for gravity field modeling is the application of filtering techniques that can remove gross errors and reduce low-frequency errors and high-frequency noise while preserving the original signal. This paper aims to present and analyze three filtering strategies used to de-noise the GOCE Level 2 data from long-wavelength correlated errors and noise. These strategies are Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and Wavelet Multi-resolution Analysis (WL), which have been applied to GOCE residual second order derivatives of the gravity potential. Several experiments were performed for each filtering scheme in order to identify the ideal filtering parameters. The outcomes indicate that all the suggested filtering strategies proved to be effective in removing low-frequency errors while preserving the signals in the GOCE MBW, with FIR filtering providing the overall best results.
Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine
Authors: Pech-May Fernando , Aquino-Santos Raúl , Rios-Toledo German , Posadas-Durán Juan Pablo Francisco
Published: June 2022

Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.
Deep Learning-Based Defect Prediction for Mobile Applications
Authors: Jorayeva Manzura , Akbulut Akhan , Catal Cagatay , Mishra Alok
Published: June 2022

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.
Effective Free-Driving Region Detection for Mobile Robots by Uncertainty Estimation Using RGB-D Data
Authors: Nguyen Toan-Khoa , Nguyen Phuc Thanh-Thien , Nguyen Dai-Dong , Kuo Chung-Hsien
Published: June 2022

Accurate segmentation of drivable areas and road obstacles is critical for autonomous mobile robots to navigate safely in indoor and outdoor environments. With the fast advancement of deep learning, mobile robots may now perform autonomous navigation based on what they learned in the learning phase. On the other hand, existing techniques often have low performance when confronted with complex situations since unfamiliar objects are not included in the training dataset. Additionally, the use of a large amount of labeled data is generally essential for training deep neural networks to achieve good performance, which is time-consuming and labor-intensive. Thus, this paper presents a solution to these issues by proposing a self-supervised learning method for the drivable areas and road anomaly segmentation. First, we propose the Automatic Generating Segmentation Label (AGSL) framework, which is an efficient system automatically generating segmentation labels for drivable areas and road anomalies by finding dissimilarities between the input and resynthesized image and localizing obstacles in the disparity map. Then, we train RGB-D datasets with a semantic segmentation network using self-generated ground truth labels derived from our method (AGSL labels) to get the pre-trained model. The results showed that our AGSL achieved high performance in labeling evaluation, and the pre-trained model also obtains certain confidence in real-time segmentation application on mobile robots.
Chemical Fingerprinting Profile and Targeted Quantitative Analysis of Phenolic Compounds from Rooibos Tea (Aspalathus linearis) and Dietary Supplements Using UHPLC-PDA-MS
Authors: Fantoukh Omer I. , Wang Yan-Hong , Parveen Abidah , Hawwal Mohammed F. , Ali Zulfiqar , Al-Hamoud Gadah A. , Chittiboyina Amar G. , Joubert Elizabeth , Viljoen Alvaro , Khan Ikhlas A.
Published: June 2022

Aspalathus linearis (Burm.f.) R. Dahlgren, commonly known as rooibos tea, was consumed traditionally by the indigenous South African inhabitants as an herbal remedy. Beside antioxidant properties, it displays antiallergic, antispasmodic, and hypoglycemic activities. An ultra-high-performance liquid chromatography method coupled with photodiode array and mass spectrometry detectors were developed for the determination of 14 phenolic constituents from leaves and stems of A. linearis. The efficient separation was performed within 30 min at a temperature of 30 °C by using C-18 column as the stationary phase and water/acetonitrile with 0.05% formic acid as the mobile phase. Method validation for linearity, repeatability, limits of detection, and limits of quantification was achieved. The limits of detection from 0.2–1 μg/mL were reported for the standard compounds. Their total content varied substantially (1.50–9.85 mg/100 mg sample) in 21 dietary supplements. The presence of regioisomers and diastereomers which co-elute on a variety of stationary phases make separation for quantification purposes challenging. This method was found to be efficient in providing low retention times and excellent resolution for this type of phytochemicals. The established method is suitable for chemical fingerprint analysis of A. linearis and cost-effective for quality control of rooibos tea products.
Current and Future Trends of Information Technology and Sustainability in Logistics Outsourcing
Authors: Mageto Joash
Published: June 2022

Sustainability awareness across various economic sectors requires firms to use logistics outsourcing to reduce logistics-related emissions with compliant logistics service providers (LSPs). LSPs apply advanced information technologies to help achieve high efficiency, effectiveness, and sustainability goals. While logistics outsourcing has received considerable attention from researchers, limited research has identified the elements of logistics outsourcing and established research trends regarding technology and sustainability aspects of logistics outsourcing. This study aims to establish trends in technology and sustainability in logistics outsourcing and identify the important elements of logistics outsourcing. A bibliometric analysis technique using the Biblioshiny package in R. A SCOPUS search resulted in 2019 documents. Research on technology and sustainability in logistics outsourcing is growing, especially in developed countries, with little or no research from developing countries. Sustainable supply chains and third-party logistics themes dominated the past research. Current research is on reverse logistics, circular economy, and green logistics; the latter is likely to dominate the future, focusing on risk management, pollution control, and innovation through advanced technologies such as artificial intelligence, machine learning, and big data. Essential elements of logistics outsourcing are identified as maintaining a good relationship based on trust and cooperation, LSP multi-selection criteria that include sustainability and technology capabilities, proper contract management, and an appropriate in-house versus outsourcing balance for competitiveness. LSP managers are advised to develop sustainability and technology capabilities, including reverse logistics. The paper contributes to logistics management theory by identifying the elements of logistics outsourcing and presenting a bibliometric result to guide future research on sustainability and technology capabilities in logistics outsourcing.
Role of Reverse Logistics Activities in the Recycling of Used Plastic Bottled Water Waste Management
Authors: Abdissa Gemechu , Ayalew Abebe , Dunay Anna , Illés Csaba Bálint
Published: June 2022

The purpose of this study is to investigate the role of reverse logistics in the recycling of used plastic bottles and waste management in Ethiopia. To achieve the study’s goal, the authors used a descriptive research design where a combination of primary and secondary data sources was employed. Primary data was collected from 258 respondents, while secondary data was taken from office manuals, yearly reports, and brochures. The findings of our analysis show that incineration, landfilling, and dumping of used plastic bottles on the side of the road are all apparent in the environment, making the city and its environs filthy and unpleasant to live in. This study also discovered that reuse and remanufacturing have become less common and are rarely practiced. Even though plastic waste disposal guidelines have been created, our investigation revealed that there is a lack of community understanding regarding solid waste management, and some stakeholders actively disregard waste management proclamations. Thus, it is recommended that all stakeholders, including the government, business owners, plastic bottled water makers, and the public, pay close attention to waste disposal, particularly of non-biodegradable pollutants in Ethiopia’s urban and rural areas.
Research in Electronic and Mobile Payment Systems: A Bibliometric Analysis
Authors: Tounekti Oussama , Ruiz-Martínez Antonio , Skarmeta Gomez Antonio F.
Published: June 2022

Electronic (mobile) payment systems are an important aspect of e-commerce. However, few reviews highlight the most significant findings and challenges. In this article, we have prepared a bibliometric analysis to provide a statistical overview of previously published research papers on electronic (mobile) payment systems and user preferences, with a particular emphasis on their diverse techniques and analyses, as well as comprehensive and reliable directions to reveal evolutionary nuances and highlighting emerging areas in this specific research. This study reviewed 177 scientific papers published between 2001 and November 2021 in the Web of Science (WoS) database on the subjects of electronic payment systems (EPS) and mobile payment systems (MPS), payment interface (PI), technology acceptance model (TAM), payment frameworks (PF), and user preferences (UP). The amount of studies using the abovementioned topics (EPS, MPS, TAM, PF, and UP) increases annually. Spain, China, and Malaysia are the three main countries that actively participate, and their international academic partnership is relatively close. We have compiled a list of the most relevant publications, prolific authors, institutions, and leading research topics. The articles were used to evaluate bibliometric indicators, analyze research activity, investigate the subject’s evolution, and identify the most interconnected themes. The findings provide a comprehensive overview of existing thematic studies, with a notable rising trend showing the potential for future research in the field. The study provides a guideline for further research.
A Place under the Sun: Planning, Landscape and Participation in a Case of a Solar Powerplant in the Israeli Desert
Authors: Romov Ela , Teschner Na’ama
Published: June 2022

Studies demonstrate the lack of common interpretations and rigorous methods for landscape assessments (LA) during design and siting of renewable energy (RES) facilities. Research shows how perceived landscape impacts influence public willingness to accept changes in the landscape. The connection between the effectiveness of LA procedures vis-à-vis the inclusion of the public in decision-making related to RES siting has received less attention. We, therefore, examine the role of LAs in planning via the eyes of policymakers and experts, and evaluate the capacity of current tools to influence the process. Additionally, we analyze the role (or lack thereof) of the public in LAs. Our unique case—one of the largest in the world thermo-solar “tower” plant, located near a small desert village—exemplifies the place for landscape consideration in national-level mega-infrastructure. Based on documents analysis and semi-structured interviews, the findings demonstrate the struggle between competing goals such as financial and temporal efficiency, RES targets, landscape protection, and public participation. We conclude that first, despite independent efforts to promote the latter two, there may be little connection between the assessment of landscape effects and public participation because there is no mechanism for post-evaluation of a project’s impacts, and any debates on the actual effects remain theoretical. Second, that landscape impacts of large-scape infrastructure can mainly be avoided in the stage of site location, and at this stage, the room for public input remains limited.
A Study on Parametric Design Method for Optimization of Daylight in Commercial Building’s Atrium in Cold Regions
Authors: Xue Yibing , Liu Wenhan
Published: June 2022

With the development of urbanization, more and more commercial buildings are built in cities, which is resulting in a large amount of building energy consumption that threatens the ecological environment of the earth. Lighting energy in commercial buildings occupies a large proportion of consumption, and improving the quality of natural daylight in commercial atriums is of great significance for building energy efficiency as well as improving indoor comfort. This paper proposes a method for optimizing the daylight quality of commercial atriums. Starting from the perspective of parametric design, this paper investigates the current status and theoretical research on the natural daylight of commercial atriums in cold regions, taking Jinan, China, as an example. Dynamic daylight and glare simulations were performed using Rhino + Grasshopper and Ladybug + Honeybee for every design parameter in the system, followed by correlation analysis and multiple linear regression analysis using SPSS to determine the degree of influence of each design parameter on the daylight quality of the atrium. Based on the results of the above analysis, the multi-objective optimization plug-in Octopus is used to find the combination of design parameters that can achieve the best indoor daylight. The results show that among a total of fourteen atrium design parameters, seven of them are significantly correlated with atrium daylight, and after regression analysis, it is found that the atrium design parameters affect the atrium daylight and glare in the following order: Skylight VT, Skylight ratio, Atrium inclination, Fabric coverage, Fabric VT, Wall reflectivity, Roof reflectivity. The optimal design parameters for commercial atrium daylight quality are obtained according to the Pareto front solution set, which provides some reference and ideas for improving the optimization of commercial atrium daylight in cold regions of China.
Impact of Urban Spatial Transformation on the Mobility of Commuters with Different Transportation Modes in China: Evidence from Kunming 2011–2016
Authors: Zheng Hui , He Baohong , He Mingwei , Guo Jinghui
Published: June 2022

Many emerging economies, including China, are undergoing rapid and large-scale urban spatial transformation. Thus, the daily mobility of transportation-disadvantaged groups, especially non-car users, has received increased attention, as these people may experience significant restrictions in their daily activities. Such restrictions raise issues with respect to transport-related social exclusion, which are detrimental to the sustainability of urban transportation systems. Activity participation and time use have been used to measure the spatial barriers and inequalities that travelers face in their daily lives. However, limited research has been conducted on how the daily mobility of different transportation modes has evolved over a longer period relative to urban development. Therefore, this study aimed to investigate the activity participation and time use of car travelers in comparison with other transportation mode groups in Kunming from 2011 to 2016, a period of rapid growth in motorization. A three-layer activity structure was used to characterize the hierarchy of activity requirements. Propensity score matching was used to compare the mobility of commuters across different urban periods and transportation modes while controlling several confounding factors. Three conclusions were drawn from the results of the study: First, changes in urban form and transportation system cause residential suburbanization and a considerable increase in private-car and public transportation at the expense of non-motorized transportation modes. Second, the degree of impact of urban space transformation on personal mobility is ranked in descending order of public transit, cycling and walking, e-bike, and cars. Third, the traffic disadvantage of non-car users is obvious, and the mobility gap of commuters with different travel modes tends to widen over time. We discuss the consequences of transport-related social exclusion and highlight directions for future sustainable transportation planning research.
Causes of Variability in Groundwater Salinity of the Lower Jurassic Sediments in the Talinskoye Oilfield of West Siberia
Authors: Abdrashitova Rimma N. , Kadyrov Marsel A.
Published: June 2022

This study aimed to identify the nature of variable groundwater salinity from the standpoint of whether natural or man-caused factors have a dominant impact on the modern salinity range of the Lower Jurassic sediments in the Talinskoye oilfield of West Siberia in Russia. As of now, the salinity values of reservoir waters vary from 3.7 to 15.3 g/dm3, with an average of 8.9 g/dm3. Petroleum hydrocarbons are extracted at the oilfield from the Lower Jurassic sediments. The volume of overlying sediments’ waters injected into the pay zones to maintain the formation pressure was more than 8960.3 thousand m3 between 2014 and 2021. In this regard, it is necessary to establish whether anthropogenic factors are critical for the variability in groundwater salinity of the Lower Jurassic sediments, or the complex geologic and hydrogeologic conditions are determinant factors affecting the salinity variability. To achieve this objective, we evaluated the genetic coefficients of various types of waters (inclusive of injected waters) which could contribute to the variability in groundwaters with varying quantities and ratios. The resultant genetic coefficients were compared with those of reservoir waters of the Lower Jurassic hydrogeological complex. This allowed for the conclusion that the major factors currently affecting the variable salinity of the reservoir waters of the Lower Jurassic complex are natural ones. We also assessed the relationship between the groundwater salinity values of the complex under study and basic parameters of the geologic setting, such as porosity, formation pressure, modern temperature and paleotemperature of the basement, and basement depth of burial. A tight association was found between the salinity values and porosity of the sediments (R = 0.87), and a very tight connection between the salinity and formation pressure (R = 0.91), which, we believe, also evidences that natural factors have a dominant effect on the variability in groundwater salinity. The variability in the modern salinity values of the reservoir waters of the complex in question ensues from the continental conditions of the groundwater genesis, expelled-water exchange processes (ingress of the pore waters expelled from argillaceous deposits as geostatic pressure rises), and the intrusion of abyssal fluids along the basement fractures.
Internal Motivations, External Contexts, and Sustainable Consumption Behavior in China—Based on the TPB-ABC Integration Model
Authors: Qin Bowen , Song Ge
Published: June 2022

Population expansion and the depletion of the planet’s natural resources make it necessary to look at human consumption behavior in sustainable development. The purpose of this study is to investigate the influence factors, the influence paths, and the decision-making mechanisms of Chinese consumers’ sustainable consumption behavior through the TPB–ABC integration theory. Based on survey data from 534 consumers in Dongying, China, this study used the partial least squares structural equation model (PLS-SEM) to analyze the main factors that influence the three sections of sustainable consumption behaviors, which are green purchase behavior, green transportation behavior, and recycling and resource conservation behavior. Decision-making mechanisms are discussed concerning impact pathways. The results prove that three internal motivations and two external contexts are intimately linked to customers’ behavioral decisions, with external contexts indirectly shaping individual attitudes. Furthermore, the factors that influence various types of sustainable consumption practices differ. Specifically, green purchase behavior and green transportation behavior are mainly influenced by attitude variables, and negative contexts mainly influence recycling and resource conservation behavior. Finally, the study suggested corresponding policy recommendations to promote sustainable consumption.
The Prediction of the Potentially Suitable Distribution Area of Cinnamomum mairei H. Lév in China Based on the MaxEnt Model
Authors: Qi Shuai , Luo Wei , Chen Ke-Lin , Li Xin , Luo Huo-Lin , Yang Zai-Qiang , Yin Dong-Mei
Published: June 2022

Cinnamomum mairei H. Lév is a rare and valuable medicinal and timber species in China. It not only has a narrow distribution, but also has few resources, is an endangered species, and is a nationally protected plant. Climate change impacts the growth and development of plants; therefore, it is of great practical significance to predict the current and future distribution of C. mairei H. Lév in suitable areas of China and to protect these endangered plants. In this study, the MaxEnt model was used to predict the suitable growing areas for C. mairei H. Lév according to six environmental factors (the temperature seasonality, max. temperature in the warmest month, min. temperature in the coldest month, precipitation seasonality, precipitation in the coldest quarter and aspect), and three different climate models (SSP126, SSP245, and SSP585) were simulated for three periods (the 2030s, 2050s, and 2070s). In the present study, the suitable ecological environment for C. mairei H. Lév comprised the following: a min. temperature in the coldest month from −0.63 to 4.36 °C, temperature seasonality from 130.67 to 642.58, a max. temperature in the warmest month from 28.86 to 45.97 °C, and precipitation in the coldest quarter from 40.12 to 101.13 mm. Highly suitable habitats for C. mairei H. Lév are located in the Yunnan Province, Guizhou Province, Sichuan Province, and Chongqing City, China (southwestern part of China), and to a lesser extent in the Xizang Province and Shaanxi Province, China. The moderately suitable habitat district overlaps with the highly suitable habitat district, and a small number of suitable habitats are distributed in Guangxi Province, Hunan Province, Hubei Province, and Henan Province. In the future, the highly suitable areas for C. mairei H. Lév will increase slightly, and the gravity points will shift toward northeast China. Our simulations are helpful for understanding the geoecological characteristics of this species and provide a basis for regional projections of this species under current and future climate change scenarios in China. It is proposed to establish nature reserves for C. mairei H. Lév in the Chongqing, Yunnan, Sichuan and Guizhou provinces of China.
Fusion Proteins CLD and CLDmut Demonstrate Potent and Broad Neutralizing Activity against HIV-1
Authors: Fu Ming , Xiao Yingying , Du Tao , Hu Huimin , Ni Fengfeng , Hu Kai , Hu Qinxue
Published: June 2022

HIV-1 envelope glycoprotein (Env) interacts with cellular receptors and mediates virus entry into target cells. Blocking Env-receptor interactions represents an effective interventional strategy for developing HIV-1 entry inhibitors. We previously designed a panel of CD4-linker-DC-SIGN (CLD) constructs by fusing the extracellular CD4 and DC-SIGN domains with various linkers. Such CLDs produced by the prokaryotic system efficiently inhibited HIV-1 infection and dissemination in vitro and ex vivo. In this study, following the construction and identification of the most promising candidate with a linker of 8 Gly4Ser repeats (named CLD), we further designed an improved form (named CLDmut) by back mutating Cys to Ser at amino acid 60 of CD4. Both CLD and CLDmut were produced in mammalian (293F) cells for better protein translation and modification. The anti-HIV-1 activity of CLD and CLDmut was assessed against the infection of a range of HIV-1 isolates, including transmitted and founder (T/F) viruses. While both CLD and CLDmut efficiently neutralized the tested HIV-1 isolates, CLDmut demonstrated much higher neutralizing activity than CLD, with an IC50 up to one log lower. The neutralizing activity of CLDmut was close to or more potent than those of the highly effective HIV-1 broadly neutralizing antibodies (bNAbs) reported to date. Findings in this study indicate that mammalian cell-expressed CLDmut may have the potential to be used as prophylaxis or/and therapeutics against HIV-1 infection.
Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020
Authors: Punyapornwithaya Veerasak , Mishra Pradeep , Sansamur Chalutwan , Pfeiffer Dirk , Arjkumpa Orapun , Prakotcheo Rotchana , Damrongwatanapokin Thanis , Jampachaisri Katechan
Published: June 2022

Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state–space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.
A Newly Engineered A549 Cell Line Expressing ACE2 and TMPRSS2 Is Highly Permissive to SARS-CoV-2, Including the Delta and Omicron Variants
Authors: Chang Ching-Wen , Parsi Krishna , Somasundaran Mohan , Vanderleeden Emma , Liu Ping , Cruz John , Cousineau Alyssa , Finberg Robert , Kurt-Jones Evelyn
Published: June 2022

New variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to emerge, causing surges, breakthrough infections, and devastating losses—underscoring the importance of identifying SARS-CoV-2 antivirals. A simple, accessible human cell culture model permissive to SARS-CoV-2 variants is critical for identifying and assessing antivirals in a high-throughput manner. Although human alveolar A549 cells are a valuable model for studying respiratory virus infections, they lack two essential host factors for SARS-CoV-2 infection: angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). SARS-CoV-2 uses the ACE2 receptor for viral entry and TMPRSS2 to prime the SARS-CoV-2 spike protein, both of which are negligibly expressed in A549 cells. Here, we report the generation of a suitable human cell line for SARS-CoV-2 studies by transducing human ACE2 and TMPRSS2 into A549 cells. We show that subclones highly expressing ACE2 and TMPRSS2 (“ACE2plus” and the subclone “ACE2plusC3”) are susceptible to infection with SARS-CoV-2, including the delta and omicron variants. These subclones express more ACE2 and TMPRSS2 transcripts than existing commercial A549 cells engineered to express ACE2 and TMPRSS2. Additionally, the antiviral drugs EIDD-1931, remdesivir, nirmatrelvir, and nelfinavir strongly inhibit SARS-CoV-2 variants in our infection model. Our data show that ACE2plusC3 cells are highly permissive to SARS-CoV-2 infection and can be used to identify anti-SARS-CoV-2 drugs.
Antigenicity Alternations of Variant PEDV S Protein Disclosed by Linear B Cell Epitope Mapping
Authors: Yu Ruisong , Dong Shijuan , Chen Bingqing , Liu Yingjie , Li Fengping , Si Fusheng , Xie Chunfang , Li Zhen
Published: June 2022

The spike protein (S) plays a crucial role in porcine epidemic diarrhea virus (PEDV) infection and induces neutralizing antibodies. Mutations of the S protein are supposed to provide the main antigenic shift leading to the antigenic escape of PEDVs. It is therefore a significant question how much accumulation of antigenic shift could lead to the antigenic escape of the variant PEDV. To provide an answer in the study, B cell epitopes (BCEs) on the S protein of the PEDV vaccine strain CV777 (SCV777) and variant strain SD2014 (SSD2014) were mapped using biosynthetic peptides and rabbit anti-PEDV S serum. Seventy-nine and 68 linear BCEs were identified from SCV777 and SSD2014, respectively. While 66.2% of the BCEs of SSD2014 could be recognized by anti-SCV777 serum and 67.1% of SCV777 BCEs could be recognized by anti-SSD2014 serum, more than 40% of the BCEs identified using anti-SCV777 serum on SCV777 could not be recognized by anti-SSD2014 serum and vice versa. The completely shared BCEs took low percentages of 29.4% and 25.3% for SSD2014 and SCV777, respectively. These results indicate a low conservation of antigenicity of the S protein compared to a relatively high amino acid sequence similarity of 92.2% between the two strains. The study provided a BCE shift reference of PEDV antigenic escape and surveillance control.