bluuebunny/crossref_metadata_2025 huggingface dataset:
A Priori Ratemaking Selection Using Multivariate Regression Models Allowing Different Coverages in Auto Insurance
Authors: Gómez-Déniz Emilio , Calderín-Ojeda Enrique
Published: July 2021

A comprehensive auto insurance policy usually provides the broadest protection for the most common events for which the policyholder would file a claim. On the other hand, some insurers offer extended third-party car insurance to adapt to the personal needs of every policyholder. The extra coverage includes cover against fire, natural hazards, theft, windscreen repair, and legal expenses, among some other coverages that apply to specific events that may cause damage to the insured’s vehicle. In this paper, a multivariate distribution, based on a conditional specification, is proposed to account for different numbers of claims for different coverages. Then, the premium is computed for each type of coverage separately rather than for the total claims number. Closed-form expressions are given for moments and cross-moments, parameter estimates, and for a priori premiums when different premiums principles are considered. In addition, the severity of claims can be incorporated into this multivariate model to derive multivariate claims’ severity distributions. The model is extended by developing a zero-inflated version. Regression models for both multivariate families are derived. These models are used to fit a real auto insurance portfolio that includes five types of coverage. Our findings show that some specific covariates are statistically significant in some coverages, yet they are not so for others.
Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
Authors: Wang Yu , Ali Md. Arfan , Bilal Muhammad , Qiu Zhongfeng , Ke Song , Almazroui Mansour , Islam Md. Monirul , Zhang Yuanzhi
Published: July 2021

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD < 0.4). Seasonally, higher AOD was seen in summer (>0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (<0.4) for all the cities. Frequencies of 0.3 ≤ AOD < 0.4 and 0.4 ≤ AOD < 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD.
Sensing Organophosphorus Compounds with SWCNT Films
Authors: Sahlman Mika , Lundström Mari , Janas Dawid
Published: July 2021

Promising electrical properties of single-walled carbon nanotubes (SWCNTs) open a spectrum of applications for this material. As the SWCNT electronic characteristics respond well to the presence of various analytes, this makes them highly sensitive sensors. In this contribution, selected organophosphorus compounds were detected by studying their impact on the electronic properties of the nanocarbon network. The goal was to untangle the n-doping mechanism behind the beneficial effect of organic phosphine derivatives on the electrical conductivity of SWCNT networks. The highest sensitivity was obtained in the case of the application of 1,6-Bis(diphenylphoshpino)hexane. Consequently, free-standing SWCNT films experienced a four-fold improvement to the electrical conductivity from 272 ± 21 to 1010 ± 44 S/cm and an order of magnitude increase in the power factor. This was ascribed to the beneficial action of electron-rich phenyl moieties linked with a long alkyl chain, making the dopant interact well with SWCNTs.
Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron
Authors: Gondal Ali Usman , Sadiq Muhammad Imran , Ali Tariq , Irfan Muhammad , Shaf Ahmad , Aamir Muhammad , Shoaib Muhammad , Glowacz Adam , Tadeusiewicz Ryszard , Kantoch Eliasz
Published: July 2021

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.
Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors
Authors: Bąk Beata , Wilk Jakub , Artiemjew Piotr , Wilde Jerzy
Published: July 2021

American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, and agar) was tested using a prototype of a multi-sensor recorder of the MCA-8 sensor signal with a matrix of six semiconductors: TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from Figaro. Two twin prototypes of the MCA-8 measurement device, M1 and M2, were used in the study. Each prototype was attached to two laboratory test chambers: a wooden one and a polystyrene one. For the experiment, the strain used was P. l. larvae ATCC 9545, ERIC I. On MYPGP medium, often used for laboratory diagnosis of American foulbrood, this bacterium produces small, transparent, smooth, and shiny colonies. Gas samples from over culture media of one- and two-day-old foulbrood P. l. larvae (with no colonies visible to the naked eye) and from over culture media older than 2 days (with visible bacterial colonies) were examined. In addition, the air from empty chambers was tested. The measurement time was 20 min, including a 10-min testing exposure phase and a 10-min sensor regeneration phase. The results were analyzed in two variants: without baseline correction and with baseline correction. We tested 14 classifiers and found that a prototype of a multi-sensor recorder of the MCA-8 sensor signal was capable of detecting colonies of P. l. larvae on MYPGP substrate with a 97% efficiency and could distinguish between MYPGP substrates with 1–2 days of culture, and substrates with older cultures. The efficacy of copies of the prototypes M1 and M2 was shown to differ slightly. The weighted method with Canberra metrics (Canberra.811) and kNN with Canberra and Manhattan metrics (Canberra. 1nn and manhattan.1nn) proved to be the most effective classifiers.
A Cu(II)-MOF Based on a Propargyl Carbamate-Functionalized Isophthalate Ligand as Nitrite Electrochemical Sensor
Authors: Cassani Maria Cristina , Castagnoli Riccardo , Gambassi Francesca , Nanni Daniele , Ragazzini Ilaria , Masciocchi Norberto , Boanini Elisa , Ballarin Barbara
Published: July 2021

This paper investigates the electrochemical properties of a new Cu(II)-based metal-organic framework (MOF). Noted as Cu-YBDC, it is built upon a linker containing the propargyl carbamate functionality and immobilized on a glassy carbon electrode by drop-casting (GC/Cu-YBDC). Afterward, GC/Cu-YBDC was treated with HAuCl4 and the direct electro-deposition of Au nanoparticles was carried at 0.05 V for 600 s (GC/Au/Cu-YBDC). The performance of both electrodes towards nitrite oxidation was tested and it was found that GC/Au/Cu-YBDC exhibited a better electrocatalytic behavior toward the oxidation of nitrite than GC/Cu-YBDC with enhanced catalytic currents and a reduced nitrite overpotential from 1.20 to 0.90 V. Additionally GC/Au/Cu-YBDC showed a low limit of detection (5.0 μM), an ultrafast response time (<2 s), and a wide linear range of up to 8 mM in neutral pH.
Evaluation of Shear Horizontal Surface Acoustic Wave Biosensors Using “Layer Parameter” Obtained from Sensor Responses during Immunoreaction
Authors: Kano Koji , Yatsuda Hiromi , Kondoh Jun
Published: July 2021

Shear horizontal surface acoustic wave (SH-SAW) biosensors measure the reaction of capture antibodies immobilized on the sensing surface to capture test molecules (antigens) by using the change in SH-SAW propagation characteristics. SH-SAW displacement exists not only on the SH-SAW propagating surface, but also partially penetrates the specimen liquid to a certain depth, which is determined by the liquid properties of the specimen and the operating frequency of the SH-SAW. This phenomenon is called viscosity penetration. In previous studies, the effect of viscosity penetration was not considered in the measurement of SH-SAW biosensors, and the mass or viscosity change caused by the specific binding of capture antibodies to the target antigen was mainly used for the measurement. However, by considering the effect of viscosity penetration, it was found that the antigen–antibody reaction could be measured and the detection characteristics of the biosensor could be improved. Therefore, this study aims to evaluate the detection properties of SH-SAW biosensors in the surface height direction by investigating the relationship between molecular dimensions and SH-SAW propagation characteristics, which are pseudo-changed by varying the diameter of gold nanoparticles. For the evaluation, we introduced a layer parameter defined by the ratio of the SH-SAW amplitude change to the SH-SAW velocity change caused by the antigen–antibody reaction. We found a correlation between the layer parameter and pseudo-varied molecular dimensions. The results suggest that SH-SAW does not only measure the mass and viscosity but can also measure the size of the molecule to be detected. This shows that SH-SAW biosensors can be used for advanced functionality.
Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models
Authors: Muñoz-Organero Mario , Queipo-Álvarez Paula , García Gutiérrez Boni
Published: July 2021

Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in range (considering the calories taken and the physical exercise carried out). In recent years, machine learning models have been developed in order to help type 1 diabetes patients with their blood glucose control. These models tend to receive the insulin units used and the carbohydrate taken as inputs and generate optimal estimations for future blood glucose levels over a prediction horizon. The body glucose kinetics is a complex user-dependent process, and learning patient-specific blood glucose patterns from insulin units and carbohydrate content is a difficult task even for deep learning-based models. This paper proposes a novel mechanism to increase the accuracy of blood glucose predictions from deep learning models based on the estimation of carbohydrate digestion and insulin absorption curves for a particular patient. This manuscript proposes a method to estimate absorption curves by using a simplified model with two parameters which are fitted to each patient by using a genetic algorithm. Using simulated data, the results show the ability of the proposed model to estimate absorption curves with mean absolute errors below 0.1 for normalized fast insulin curves having a maximum value of 1 unit.
Deep-Learning-Based Multimodal Emotion Classification for Music Videos
Authors: Pandeya Yagya Raj , Bhattarai Bhuwan , Lee Joonwhoan
Published: July 2021

Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving efficient affective computing. This paper presents an affective computing system that relies on music, video, and facial expression cues, making it useful for emotional analysis. We applied the audio–video information exchange and boosting methods to regularize the training process and reduced the computational costs by using a separable convolution strategy. In sum, our empirical findings are as follows: (1) Multimodal representations efficiently capture all acoustic and visual emotional clues included in each music video, (2) the computational cost of each neural network is significantly reduced by factorizing the standard 2D/3D convolution into separate channels and spatiotemporal interactions, and (3) information-sharing methods incorporated into multimodal representations are helpful in guiding individual information flow and boosting overall performance. We tested our findings across several unimodal and multimodal networks against various evaluation metrics and visual analyzers. Our best classifier attained 74% accuracy, an f1-score of 0.73, and an area under the curve score of 0.926.
F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes
Authors: Attaullah Hasina , Anjum Adeel , Kanwal Tehsin , Malik Saif Ur Rehman , Asheralieva Alia , Malik Hassan , Zoha Ahmed , Arshad Kamran , Imran Muhammad Ali
Published: July 2021

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.
Wearable Robotic Gait Training in Persons with Multiple Sclerosis: A Satisfaction Study
Authors: Fernández-Vázquez Diego , Cano-de-la-Cuerda Roberto , Gor-García-Fogeda María Dolores , Molina-Rueda Francisco
Published: July 2021

Wearable exoskeletons have showed improvements in levels of disability and quality of life in people with neurological disorders. However, it is important to understand users’ perspectives. The aim of this study was to explore the patients’ and physiotherapists’ satisfaction from gait training with the EKSO GT® exoskeleton in people with multiple sclerosis (MS). A cross-sectional study with 54 participants was conducted. Clinical data and self-administered scales data were registered from all patients who performed sessions with EKSO GT®. To evaluate patients’ satisfaction the Quebec User Evaluation with Assistive Technology and Client Satisfaction Questionnaire were used. A high level of satisfaction was reported for patients and for physiotherapists. A moderate correlation was found between the number of sessions and the patients’ satisfaction score (rho = 0.532; p < 0.001), and an excellent correlation between the physiotherapists’ time of experience in neurology rehabilitation and the satisfaction with the possibility of combining the device with other gait trainings approaches (rho = 0.723; p = 0.003). This study demonstrates a good degree of satisfaction for people with MS (31.3 ± 5.70 out of 40) and physiotherapists (38.50 ± 3.67 out of 45 points) with the EKSO GT®. Effectiveness, safety and impact on the patients’ gait were the most highly rated characteristics of EKSO GT®. Features such as comfort or weight of the device should be improved from the patients’ perspectives.
MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks
Authors: Anjum Naveed , Latif Zohaib , Lee Choonhwa , Shoukat Ijaz Ali , Iqbal Umer
Published: July 2021

In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major challenges in current Network Intrusion Detection Systems (NIDS). The data fusion technique is an emerging technology that merges data from multiple sources to form more certain, precise, informative, and accurate data. Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. In this paper, we propose a multi-source data fusion scheme for intrusion detection in networks (MIND) , where data fusion is performed by the horizontal emergence of two datasets. For this purpose, the Hadoop MapReduce tool such as, Hive is used. In addition, a machine learning ensemble classifier is used for the fused dataset with fewer parameters. Finally, the proposed model is evaluated with a 10-fold-cross validation technique. The experiments show that the average accuracy, detection rate, false positive rate, true positive rate, and F-measure are 99.80%, 99.80%, 0.29%, 99.85%, and 99.82% respectively. Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods.
Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification
Authors: García Garví Antonio , Puchalt Joan Carles , Layana Castro Pablo E. , Navarro Moya Francisco , Sánchez-Salmerón Antonio-José
Published: July 2021

The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% ± 1.30% per plate) with respect to the manual curve, demonstrating its feasibility.
Classification of the Acoustics of Loose Gravel
Authors: Saeed Nausheen , Nyberg Roger G. , Alam Moudud , Dougherty Mark , Jooma Diala , Rebreyend Pascal
Published: July 2021

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.
Separation of Lanthanide Isotopes from Mixed Fission Product Samples
Authors: Arrigo Leah M. , Jiang Jun , Finch Zachary S. , Bowen James M. , Herman Staci M. , Greenwood Larry R. , Friese Judah I. , Seiner Brienne N.
Published: July 2021

The measurement of radioactive fission products from nuclear events has important implications for nuclear data production, environmental monitoring, and nuclear forensics. In a previous paper, the authors reported the optimization of an intra-group lanthanide separation using LN extraction resin from Eichrom Technologies®, Inc. and a nitric acid gradient. In this work, the method was demonstrated for the separation and quantification of multiple short-lived fission product lanthanide isotopes from a fission product sample produced from the thermal irradiation of highly enriched uranium. The separations were performed in parallel in quadruplicate with reproducible results and high decontamination factors for 153Sm, 156Eu, and 161Tb. Based on the results obtained here, the fission yields for 144Ce, 153Sm, 156Eu, and 161Tb are consistent with published fission yields. This work demonstrates the effectiveness of the separations for the intended application of short-lived lanthanide fission product analysis requiring high decontamination factors.
Predictive Role of Ex Ante Strategic Firm Characteristics for Sustainable Initial Public Offering (IPO) Survival
Authors: Ahmad Iftikhar , Ismail Izlin , Shaharuddin Shahrin Saaid
Published: July 2021

This study attempts to predict how long a newly listed corporation, usually termed initial public offering (IPO), will survive on the equity listing market. The three-fold contribution of this study comprises a hand-collected and substantially expanded dataset for listed IPOs (1990–2017) over a maximum tracking period of 31 years (1990–2020) to predict the IPO survival on emerging Malaysian capital market, the rationale and consequences for unifying the two listing boards (Main Board and Second Board) in 2009, and an investigation of the predictive role of ex ante strategic prospectus information as early warning signals for sustainable survival of Malaysian IPOs. We also make comparisons for the survival profile of IPOs listed on different listing equity boards. We use Cox proportional hazard (PH) model to estimate the empirical results because of the cohort research design of the study. Overall empirical results show that survival curves for IPOs listed on Main Board and Second Board were not statistically different. However, Second Board IPOs remained more vulnerable to hazard. The survival curves for IPOs listed on Main Market and ACE Market are statistically different. Empirical results reveal that high share premium, high listed capital, and longer firm age at listing date significantly increase the survival (reduce hazard) of IPOs listed on the Main Market and the Second Board. However, bigger firm size and elevated risk factors significantly reduce the survival (increase hazard) of the listed IPOs mentioned above. However, share premium is the only variable that has a negative and significant correlation with IPO survival on ACE Market. These results have implications for the regulators, prospective investors, and policymakers of emerging markets, where the IPO prospectus disclosures bridge the information asymmetry gap prevailing due to the nonexistence of public information prior to the IPO. Empirical findings of this study can be generalized to other developing and emerging markets where IPO prospectus substantially mitigates information asymmetry and ex ante strategic firm characteristics act as early warning signals in predicting IPO survival.
Sustainability of the Agricultural Systems of Indigenous People in Hidalgo, Mexico
Authors: Leyva Doris , De la Torre Mayra , Coronado Yaxk’in
Published: July 2021

Agricultural sustainability depends on complex relationships between environmental, economic, and social aspects, especially with small farm holders from indigenous communities. This work was centered on two municipalities of Hidalgo State in Mexico, Ixmiquilpan (mainly irrigated systems) and El Cardonal (rainfed systems). Our objective was to understand the relationships between the small farm holders and their agricultural systems. We evaluated the sustainability of their agricultural systems and made some recommendations. We applied the Framework for the Evaluation of Management Systems using Indicators (MESMIS, Spanish acronym); thirty-one indicators were identified, and quantitative indexes were established to assess the sustainability. The results showed that adaptability was a critical factor for irrigated and rainfed systems, and the main problem identified was youth migration. Additionally, the access to water and economic resources and the management of environmental resources are necessary in order to increase the yield of agricultural crops. Therefore, a holistic approach that considers the organization of small producers and synergy between indigenous knowledge and modern technologies is required for the territorial development of the communities.
Metagenomic Study of the Community Structure and Functional Potentials in Maize Rhizosphere Microbiome: Elucidation of Mechanisms behind the Improvement in Plants under Normal and Stress Conditions
Authors: Omotayo Oluwadara , Igiehon Ozede , Babalola Olubukola
Published: July 2021

The community of microbes in the rhizosphere region is diverse and contributes significantly to plant growth and crop production. Being an important staple and economic crop, the maize rhizosphere microbiota has been studied in the past using culture-dependent techniques. However, these limited culturing methods often do not help in understanding the complex community of microbes in the rhizosphere. Moreover, the vital biogeochemical processes carried out by these organisms are yet to be fully characterized. Herein, shotgun metagenomics, which enables the holistic study of several microbial environments, was employed to examine the community structure and functional potentials of microbes in the maize rhizosphere and to assess the influence of environmental variables on these. The dominant microbial phyla found in the soil environments include Actinobacteria, Microsporidia, Bacteroidetes, Thaumarchaeota, Proteobacteria and Firmicutes. Carbohydrate metabolism, protein metabolism and stress metabolism constitute the major functional categories in the environments. The beta diversity analysis indicated significant differences (p = 0.01) in the community structure and functional categories across the samples. A correlation was seen between the physical and chemical properties of the soil, and the structural and functional diversities. The canonical correspondence analysis carried out showed that phosphorus, N-NO3, potassium and organic matter were the soil properties that best influenced the structural and functional diversities of the soil microbes. It can be inferred from this study that the maize rhizosphere is a hotspot for microorganisms of agricultural and biotechnological importance which can be used as bioinoculants for sustainable agriculture.
Dancing as Moments of Belonging: A Phenomenological Study Exploring Dancing as a Relevant Activity for Social and Cultural Sustainability in Early Childhood Education
Authors: Grindheim Maria , Grindheim Liv Torunn
Published: July 2021

Individuals’ capacities to contribute to more sustainable living are deeply influenced by their early life experiences. Hence, there is a need to discover which experiences are relevant to young children’s contemporary and future contributions to more sustainable living. Perceiving children as aesthetically oriented to the world and their sense of belonging as a core experience for social and cultural sustainability, and using the example of dancing, we investigate how such a sense of belonging can be supported through aesthetic first-person experiences. This article is therefore structured around the following research question: How can adults’ experiences of themselves, others and their sense of belonging—when dancing—inform explorations of ways to foster embodied and aesthetic belonging for social and cultural sustainability in early childhood education (ECE)? Drawing on a phenomenological study, we analyse interviews with four dancers, who differ in age, gender and dance genre. Our analysis reveals their experiences when dancing as being in a meditative state, having a sense of freedom and feeling body and mind as one, described as an overall “different”, resilient way of being and belonging in a social context. Our findings indicate that facilitating moments of sensible and bodily awareness can support a non-verbal understanding of oneself and others, as well as arguments for promoting aesthetic experiences while dancing as relevant to sustainable practices in ECE.
When Land Meets Finance in Latin America: Some Intersections between Financialization and Land Grabbing in Argentina and Brazil
Authors: Garcia-Arias Jorge , Cibils Alan , Costantino Agostina , Fernandes Vitor B. , Fernández-Huerga Eduardo
Published: July 2021

Financialization is one of the most relevant processes embedded in the functioning and evolution of the contemporary capitalist model and presents differential characteristics in the peripheral economies of the world-system. In turn, land grabbing is also one of the most relevant phenomena taking place in the field of farmland and land use, with particular significance also within the Global South. After presenting an in-depth analysis of both phenomena for Latin America, we specifically study the case of the two Latin American countries (Argentina and Brazil) where land grabbing has a greater qualitative and quantitative importance. In our article, we analyze the main interrelationships between both processes and show how financialization has played a fundamental role (together with the policies designed and the de-regulations implemented by respective states, and the participation of other domestic actors) in the land grabbing process in both countries.
The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality
Authors: Lotfian Maryam , Ingensand Jens , Brovelli Maria Antonia
Published: July 2021

Advances in artificial intelligence (AI) and the extension of citizen science to various scientific areas, as well as the generation of big citizen science data, are resulting in AI and citizen science being good partners, and their combination benefits both fields. The integration of AI and citizen science has mostly been used in biodiversity projects, with the primary focus on using citizen science data to train machine learning (ML) algorithms for automatic species identification. In this article, we will look at how ML techniques can be used in citizen science and how they can influence volunteer engagement, data collection, and data validation. We reviewed several use cases from various domains and categorized them according to the ML technique used and the impact of ML on citizen science in each project. Furthermore, the benefits and risks of integrating ML in citizen science are explored, and some recommendations are provided on how to enhance the benefits while mitigating the risks of this integration. Finally, because this integration is still in its early phases, we have proposed some potential ideas and challenges that can be implemented in the future to leverage the power of the combination of citizen science and AI, with the key emphasis being on citizen science in this article.
An Assessment of Social Distancing Obedience Behavior during the COVID-19 Post-Epidemic Period in China: A Cross-Sectional Survey
Authors: Yuan Jinghan , Zou Hansong , Xie Kefan , Dulebenets Maxim A.
Published: July 2021

Social distancing plays a critical role in reducing the disease diffusion risk during the COVID-19 pandemic and post-pandemic period. In order to explore the social distancing obedience behavior, a comprehensive survey was conducted in this study by collecting data from 1064 Chinese residents in January 2021 by means of a questionnaire. Structural equation modeling (SEM) and hierarchical linear regression (HLR) analyses were employed to investigate the research hypotheses considered, testing the three influencing factors of social distancing obedience behavior: public guidance, risk perception, and regulation punishment. The reliability and validity of the measurements are demonstrated. The outcomes from the conducted analyses show that the public guidance significantly affects risk perception of individuals, while risk perception imposes a positive impact on social distancing obedience behavior. Moreover, risk perception serves a mediating role in the relationship between the public guidance and social distancing obedience behavior. In addition, regulation punishment positively predicts social distancing obedience behavior and could even have a greater effect by enhancing risk perception. Hence, this study suggests that the relevant authorities and agencies implement strong social distancing policies during the COVID-19 post-pandemic period from the perspective of promoting the public guidance, risk perception, and regulation punishment.
Optimization Research on Vehicle Routing for Fresh Agricultural Products Based on the Investment of Freshness-Keeping Cost in the Distribution Process
Authors: Zhu Shenjun , Fu Hongming , Li Yanhui
Published: July 2021

In cold chain logistics, fresh agricultural products are susceptible to deteriorate due to the passage of time in the distribution process. To reduce the loss of cargo, this research integrates the traditional refrigeration cost into the freshness-keeping cost invested in the process of transportation and unloading goods. We rely on the investment of freshness-keeping cost to reduce the cargo damage cost caused by the distribution process and then propose a new vehicle routing problem (VRP). According to all relevant costs, this research builds a mathematical model with the goal of minimizing the total distribution cost. A hybrid ant colony optimization is designed to solve the problem, and the effectiveness of the model and algorithm are verified through two sets of comparative experiments. To determine which products should be invested in freshness-keeping cost to reduce the total distribution cost, we perform numerical analysis on the relevant parameters in the model. Results provide decision-making references for cold chain logistics distribution enterprises in the design of distribution routes.
Corporate Social Responsibility and Sustainable Employability: Empirical Evidence from Korea
Authors: Rhee Chang Seop , Woo Sohee , Yu So-Jin , Rhee Hyunjung
Published: July 2021

A firm’s corporate social responsibility (CSR) aids in social well-being, but it is costly. It is thus necessary to study whether a firm’s CSR activities are valuable in terms of costs and benefits for shareholders’ interest. Recent studies reported that firms’ CSR activities help to develop the corporate environment and improve financial performance. In addition, prior studies explained that a firm’s CSR activities can have a positive effect on financial performance by increasing employees’ commitment to their firm. The purpose of this study research is to examine the effect of CSR activities on sustainable employability through empirical analysis. We measured the sustainable employability using the percentage of regular employees and then examined the effect of CSR activities on sustainable employability using 3802 firm-year data for Korean listed firms. From the empirical results, we found that firms engaging in CSR activities improve more in terms of sustainable employability than do firms who are not engaging in CSR activities. We also found that the companies engaging in a high CSR index score showed greater sustainable employability than did those with a low CSR index score. The results of this study suggested a way to increase sustainability in terms of employment by supporting a rational basis for companies to adopt CSR. These findings are expected to contribute to academia and the capital market by providing empirical evidence that a company’s CSR activities have a positive impact on sustainable employability.
Sustaining the Effective Use of Materials in Language Classrooms: A Conceptual Understanding of Teacher Knowledge for Materials Use
Authors: Li Zhan , Xu Yueting
Published: July 2021

Materials use is a critical yet understudied domain of language teaching across jurisdictions and educational contexts. This study explored what knowledge constituents that English-as-a-foreign-language (EFL) teachers mobilize in their use of materials. Based on the social theory of human cognition in tool use and the conception of foreign language teachers’ pedagogical content knowledge, a qualitative multi-case study involving eight EFL teachers at one university in China was conducted. Data from interviews, lesson observations, and documents across three consecutive semesters revealed that teacher knowledge for materials use is multi-dimensional and developmental. The constituents of this professional knowledge are manifested in four domains, i.e., subject matter, pedagogical, curricular, and contextual domains. The study concluded that the professional knowledge needed for materials use is not merely an individual trait, but mediated by both human and nonhuman elements. Therefore, new forms of agency from both teachers and materials are required to foster the growth of this professional knowledge. Based on the findings, suggestions were made for teacher learning through materials use in language classrooms.
Analysis of Flame Suppression Capabilities Using Low-Frequency Acoustic Waves and Frequency Sweeping Techniques
Authors: Wilk-Jakubowski Jacek Łukasz
Published: July 2021

Symmetry plays a key role in the processing and analysis of not only visual but also acoustic signals in various multidisciplinary areas. New innovative and environmentally friendly methods for extinguishing flames are still being sought worldwide. One of these techniques appears to be the acoustic method. A laboratory stand was built for this purpose, which was coupled with the tested prototype of a high-power acoustic extinguisher, and then the original experiments and analyses of extinguishing effectiveness were carried out. For extinguishing, waveforms with specified parameters selected symmetrically around the frequency for which the extinguisher was designed were used. The aim of this article is to present and discuss selected measurement results concerning the possibility of flame extinguishing with the use of sinusoidal acoustic waves of low frequency (below 21 Hz), as well as with the use of frequency sweeping techniques with set parameters. Such an extinguisher can be equipped with an intelligent module so that the extinguisher may be activated automatically (without human intervention) when flames are detected. The benefits of this combination as well as the importance of image processing for flame detection are also presented in this paper. This solution, with its good fire detection and fast response, may be applicable for extinguishing firebreaks in particular.
Acetylation, Methylation and Allysine Modification Profile of Viral and Host Proteins during Influenza A Virus Infection
Authors: Ahmed Farjana , Kleffmann Torsten , Husain Matloob
Published: July 2021

Protein modifications dynamically occur and regulate biological processes in all organisms. Towards understanding the significance of protein modifications in influenza virus infection, we performed a global mass spectrometry screen followed by bioinformatics analyses of acetylation, methylation and allysine modification in human lung epithelial cells in response to influenza A virus infection. We discovered 8 out of 10 major viral proteins and 245 out of 2280 host proteins detected to be differentially modified by three modifications in infected cells. Some of the identified proteins were modified on multiple amino acids residues and by more than one modification; the latter occurred either on different or same residues. Most of the modified residues in viral proteins were conserved across >40 subtypes of influenza A virus, and influenza B or C viruses and located on the protein surface. Importantly, many of those residues have already been determined to be critical for the influenza A virus. Similarly, many modified residues in host proteins were conserved across influenza A virus hosts like humans, birds, and pigs. Finally, host proteins undergoing the three modifications clustered in common functional networks of metabolic, cytoskeletal, and RNA processes, all of which are known to be exploited by the influenza A virus.
Identification, Genetic Characterization and Validation of Highly Diverse HIV-1 Viruses for Reference Panel Development
Authors: Zhao Jiangqin , Huang Hanxia , Lee Sherwin , Ragupathy Viswanath , Biswas Santanu , Mbondji-wonje Christelle , Wang Xue , Jiang Alex , Hewlett Indira
Published: July 2021

The continued diversification of HIV poses potentially significant challenges to HIV diagnostics and therapeutics. The dynamic evolution of emerging variants is highlighted in countries such as Cameroon in West Central Africa, where all known subtypes and circulating recombinant forms (CRFs) have been shown to be prevalent. We obtained several hundred HIV-positive plasma and viruses from this region for characterization and identification of highly divergent HIV strains. A total of 163 viral strains were cultured to high titers and high volumes using donor peripheral blood mononuclear cells (PBMCs). Initially, 101 viruses representing 59 strains were well characterized and categorized. Results showed that the viral load (VL) range was 0.36–398.9 × 107 copies/mL, p24 values was 0.2–1134 ng/mL. Phylogenetic analysis of thirty-six near full-length HIV-1 genomic sequences demonstrated that most recombinants were highly diverse CRF02 containing unique recombinant forms (URFs). There were seven viral isolates identified as pure subtype/sub-subtypes (F2, A1, G, and D), six as CRFs (CRF06, CRF18, and CRF22), and ten as URFs. These extensively characterized reagents reflect the current dynamic and complex HIV epidemic in Cameroon and provide valuable insights into the potential phylogenetic evolutionary trend of global HIV molecular epidemiology in the future. These materials may be useful for development of HIV validation and reference panels to evaluate the performance of serologic antigen and nucleic acid assays for their ability to detect and quantitate highly divergent HIV strains.
Simulations of the Soil Evaporation and Crop Transpiration Beneath a Maize Crop Canopy in a Humid Area
Authors: Guo Tianting , Liu Chunwei , Xiang Ying , Zhang Pei , Wang Ranghui
Published: July 2021

Soil evaporation (Es) and crop transpiration (Tc) are important components of water balance in cropping systems. Comparing the accurate calculation by crop models of Es and Tc to the measured evaporation and transpiration has significant advances to the optimal configuration of water resource and evaluation of the accuracy of crop models in estimating water consumption. To evaluate the adaptation of APSIM (Agricultural Production Systems simulator) in calculating the Es and Tc in Nanjing, APSIM model parameters, including the meteorological and soil parameters, were measured from a two-year field experiment. The results showed that: (1) The simulated evaporation was basically consistent with the measured Es, and the regulated model can effectively present the field evaporation in the whole maize growth period (R2 = 0.85, D = 0.96, p < 0.001); and (2) The trend of the simulated Tc can present the actual Tc variation, but the accuracy was not as high as the evaporation (R2 = 0.74, D = 0.87, p < 0.001), therefore, the simulation of water balance process by APSIM will be helpful in calculating Es and Tc in a humid area of Nanjing, and its application also could predict the production of maize fields in Nanjing.
Chemical and Biochemical Processes of Watershed Ecosystems and Their Impacts on Water Quality
Authors: Haraguchi Akira
Published: July 2021

This Special Issue of Water focuses on the natural and anthropogenic impacts on water quality in watershed ecosystems [...]