bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Smallholder Maize Farms Using Multi-Temporal Sentinel-1 Data in Support of the Sustainable Development Goals
Authors: Mashaba-Munghemezulu Zinhle , Chirima George Johannes , Munghemezulu Cilence
Published: April 2021

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.
Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning
Authors: Dašić Dejan , Ilić Nemanja , Vučetić Miljan , Perić Miroslav , Beko Marko , Stanković Miloš S.
Published: April 2021

In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.
Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification
Authors: Dominguez-Morales Juan P. , Duran-Lopez Lourdes , Gutierrez-Galan Daniel , Rios-Navarro Antonio , Linares-Barranco Alejandro , Jimenez-Fernandez Angel
Published: April 2021

Monitoring animals’ behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt.
Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach
Authors: Simoni Laura , Scarton Alessandra , Macchi Claudio , Gori Federico , Pasquini Guido , Pogliaghi Silvia
Published: April 2021

Quantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., Graal), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., Optogait—the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel’s brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland–Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, p = 0.25, r2 = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, p = 0.55, r2 = 0.98). Bland–Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, p = 0.92, r2 = 0.00038; 0.21 ± 0.96, p = 0.87, r2 = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals.
Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review
Authors: Cobo Antonio , Villalba-Mora Elena , Pérez-Rodríguez Rodrigo , Ferre Xavier , Rodríguez-Mañas Leocadio
Published: April 2021

Ubiquity (devices becoming part of the context) and transparency (devices not interfering with daily activities) are very significant in healthcare monitoring applications for elders. The present study undertakes a scoping review to map the literature on sensor-based unobtrusive monitoring of older adults’ frailty. We aim to determine what types of devices comply with unobtrusiveness requirements, which frailty markers have been unobtrusively assessed, which unsupervised devices have been tested, the relationships between sensor outcomes and frailty markers, and which devices can assess multiple markers. SCOPUS, PUBMED, and Web of Science were used to identify papers published 2010–2020. We selected 67 documents involving non-hospitalized older adults (65+ y.o.) and assessing frailty level or some specific frailty-marker with some sensor. Among the nine types of body worn sensors, only inertial measurement units (IMUs) on the waist and wrist-worn sensors comply with ubiquity. The former can transparently assess all variables but weight loss. Wrist-worn devices have not been tested in unsupervised conditions. Unsupervised presence detectors can predict frailty, slowness, performance, and physical activity. Waist IMUs and presence detectors are the most promising candidates for unobtrusive and unsupervised monitoring of frailty. Further research is necessary to give specific predictions of frailty level with unsupervised waist IMUs.
SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks
Authors: Popoola Segun I. , Adebisi Bamidele , Ande Ruth , Hammoudeh Mohammad , Anoh Kelvin , Atayero Aderemi A.
Published: April 2021

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.
A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning
Authors: Qaddoura Raneem , M. Al-Zoubi Ala’ , Faris Hossam , Almomani Iman
Published: April 2021

The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.
Evolution of Small Strain Soil Stiffness during Freeze-Thaw Cycle: Transition from Capillarity to Cementation Examined Using Magnetic and Piezo Crystal Sensors
Authors: Park Junghee , Lee Jong-Sub , Won Jongmuk , Kim Jongchan
Published: April 2021

Freeze-thaw cycles caused by seasonal temperature fluctuations significantly affect the geotechnical engineering properties. This study investigated the crucial role of water distribution patterns in the characterization of elastic wave properties for the fine F-110 sand during a freeze-thaw cycle. Sand specimens with four different water distribution patterns were prepared, namely homogeneously-mixed, evaporation-driven, vertically-, and horizontally-layered specimens. The P- and S-wave signatures of the specimens were monitored using piezo crystal sensors. Results indicated the criticality of water distribution patterns in the determination of small-strain soil properties even though the specimens had identical global water saturation. The nuclear magnetic resonance-based water volume depth profiles indicated that the evaporation-driven specimens had more heterogeneous pore-invasive ice-bonding layers at a high water saturation region; by contrast, the drying process facilitated uniform meniscuses around the particle contacts near the air percolation threshold. Elastic wave measurements for laboratory-prepared specimens might over/underestimate the small-strain soil stiffness of sediments in nature, wherein the drying processes prevailed to control the water saturation. This study highlighted a clear transition from capillary-controlled to cementation-controlled elastic wave properties during temperature oscillations.
Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder
Authors: Minelli Annalisa , Tassetti Anna Nora , Hutton Briony , Pezzuti Cozzolino Gerardo N. , Jarvis Toby , Fabi Gianna
Published: April 2021

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.
Edge/Fog Computing Technologies for IoT Infrastructure
Authors: Kim Taehong , Yoo Seong-eun , Kim Youngsoo
Published: April 2021

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices [...]
The Fragility of Women’s Work Trajectories in Chile
Authors: Undurraga Rosario , Gunnarsson Jóna
Published: April 2021

How are the work trajectories of Chilean women? This qualitative study analyzes the female work trajectories through interviews and biograms in a sample of 50 Chilean women, professionals and non-professionals, between the ages of 24 and 88. The article proposes an original typology of female work trajectories and relates type of work trajectory with Piore’s theory of labor market segmentation. The paper discusses the challenges and weaknesses of the Chilean women’s labor outcome and presents recent data to extrapolate the impact of the COVID-19 pandemic on vulnerable work trajectories. It considers the type of State and possible actions to achieve greater welfare and social development regarding gender equality.
Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities
Authors: Nasralla Moustafa M.
Published: April 2021

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.
Does Circular Reuse of Chickpea Cooking Water to Produce Vegan Mayonnaise Reduce Environmental Impact Compared with Egg Mayonnaise?
Authors: Saget Sophie , Costa Marcela , Styles David , Williams Mike
Published: April 2021

Consumers are increasingly asking for foods that are healthier, more humane, and environmentally sustainable. Recently, chickpea cooking water—aquafaba—has gained popularity as a potential egg substitute that complies with these criteria. However, research on the environmental impact of this ingredient is lacking. We performed a comparative attributional life cycle assessment (LCA) of mayonnaise made with aquafaba as the emulsifying agent, and traditional mayonnaise made with egg yolk. The vegan mayonnaise was found not to be as environmentally sustainable as the egg-based product. The vegan mayonnaise had a significantly (p < 0.05) lower impact across 4 categories, but a significantly higher impact across 8 categories out of 16, including climate change and resource-use-energy-carriers. The majority of categories under which vegan mayonnaise underperformed were related to the electricity needed for aquafaba processing. These impacts can be mitigated with a “cleaner” electricity grid, or onsite renewable electricity generation. Substituting the Mexican grid, where the aquafaba is currently processed, for the Canadian grid, where the mayonnaise is produced, reduced the carbon footprint of the vegan mayonnaise by 37%, making it similar to the egg-based product. As sunflower oil production was linked to extensive environmental burdens, we performed additional sensitivity analyses around oil processing, sunflower production, and other vegetable oils. Our study shows that substituting egg yolk with aquafaba could cause an increase in the environmental footprint of mayonnaise due to high processing costs, illustrating that vegan options do not always have a smaller environmental footprint, and can represent a trade-off in their comparatively more humane and healthier offer.
Photocatalytic Degradation of Methylene Blue Using Zinc Oxide Nanorods Grown on Activated Carbon Fibers
Authors: Albiss Borhan , Abu-Dalo Muna
Published: April 2021

In this work, the synthesis, characterization, and photocatalytic performance of zinc oxide/activated carbon fiber nanocomposites prepared by hydrothermal method were investigated. Zinc oxide nanoparticles (ZnO-NP) were deposited as seeds on porous activated carbon fiber (ACF) substrates. Then, zinc oxide nanorods (ZnO-NR) were successfully grown on the seeds and assembled on the fibers’ surface in various patterns to form ZnO-NR/ACF nanocomposites. The nanocomposites were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectrometry, UV–vis diffuse reflectance spectra (DRS), and Brunauer–Emmett–Teller (BET) surface area analysis. SEM images showed that brush-like and flower-like ZnO-NR patterns were grown uniformly on the ACF surface with sizes depending on the ZnO-NP concentration, growth time, and temperature. The FTIR spectrum confirmed the presence of the major vibration bands, especially the absorption peaks representing the vibration modes of the COOH (C = O and C = C) functional group. Adsorption and photocatalytic activities of the synthesized catalytic adsorbents were compared using methylene blue (MB) as the model pollutant under UV irradiation. ZnO-NR/ACF nanocomposites showed excellent photocatalytic activity (~99% degradation of MB in 2 h) compared with that of bare ZnO-NR and ACF. Additionally, a recycling experiment demonstrated the stability of the catalyst; the catalytic degradation ratio of ZnO-NR/ACF reached more than 90% after five successive runs and possessed strong adsorption capacity and high photocatalytic ability. The enhanced photocatalytic activities may be related to the effects of the relatively high surface area, enhanced UV-light absorption, and decrease of charge carrier recombination resulting from the synergetic adsorption–photocatalytic degradation effect of ZnO and ACF.
Do Foldable Containers Enhance Efficient Empty Container Repositioning under Demand Fluctuation?—Case of the Pacific Region
Authors: Liang Zirui , Shibasaki Ryuichi , Hoshino Yuji
Published: April 2021

This study considers the empty container repositioning problem of shipping companies that use standard and 3-in-1 foldable containers with more advanced designs. A mathematical model is developed to compare the total management costs of container repositioning of various patterns in different cargo shipping demand scenarios. Numerous scenario analyses and simulations of empty container repositioning were conducted, focusing on a liner shipping service in the Pacific Islands where empty containers are likely to be present because of the imbalance between inbound and outbound flows of containers, including static analysis and consecutive analysis with demand fluctuation in different approaches. Results show that with the introduction of foldable containers, depending on the growth rate of container cargo shipping demand, the total management costs of empty container repositioning can be reduced. However, introducing a large number of foldable containers may increase the total management costs of container repositioning. Moreover, the cost reduction effect of adding another containership increases in cases where future cargo shipping demand increases substantially. Furthermore, the introduction of foldable containers not only effectively reduces the management costs of empty containers, but also makes costs more stable and predictable.
The Effect of Ethnocentrism on Product Evaluation and Purchase Intention: The Case of Extra Virgin Olive Oil (EVOO)
Authors: Blazquez-Resino Juan Jose , Gutierrez-Broncano Santiago , Jimenez-Estevez Pedro , Perez-Jimenez Israel Roberto
Published: April 2021

This study is framed within the concept of sustainability of local foods such as extra virgin olive oil (EVOO) and considers the effects of country of origin (COO) and ethnocentrism as relevant factors in decision making about product choice. Our work contributes to the literature regarding the food industry with the main objective of investigating how consumer ethnocentrism may affect not only behavioral intention but also the perception of the quality of the EVOO. The authors of the present paper developed this line of research via a review of the existing literature, leading to the elaboration of the conceptual model proposed in this paper. The research was developed through a laboratory experiment and the modeling of consumer behavior, raising a series of hypotheses, which were contrasted following the different analyses conducted on the data. Results were obtained on factors such as the differences in evaluation according to label type, the effect of ethnocentrism on perception and purchase intention, and structural knowledge of the weight of the different variables that influence this decision making. Several guidelines and conclusions are derived from these results, which refer to the use of COO information as well as the satisfaction of consumer ethnocentrism. Understanding the role played by consumer ethnocentrism in the evaluation of food products in accordance with their origin may yield useful information for local food producers.
Battery Electric Bus Network: Efficient Design and Cost Comparison of Different Powertrains
Authors: Barraza Orlando , Estrada Miquel
Published: April 2021

Recent advances in the electromobility of bus fleets improve transit service sustainability but require the development of vehicle recharging facilities. The paper defines a methodology to design an efficient transit network operated by battery electric buses in cities with grid-shaped road network, based on continuous approximations. An analytical model defines the optimal network configuration that minimizes the agency cost, the monetization of emissions and the travel time of transit users. The analytical model allows the comparison of total cost, emissions and bus performance to other fuel powertrains. The methodology is tested in Guadalajara (Mexico) to propose an alternative bus configuration, outperforming the current bus service and reducing the agency cost and environmental impact. The analytical model justifies the network rationalization in fewer routes to reduce the total cost of the system. The deployment of standard battery electric buses with opportunity charging scheme obtains the lowest total cost of the system.
Recommendations for Sustainable Brand Personalities: An Empirical Study
Authors: Paetz Friederike
Published: April 2021

Sustainability marketing has emerged as an important trend both in practice and academic literature. The relevant literature has heavily focused on determinations of sustainable consumer behavior, and practitioners have used these results to derive short-term marketing decisions, e.g., adequate pricing of sustainable products. However, no study has scrutinized derivations of sustainable brand personalities or provided important long-term, strategic, managerial implications for marketing managers of sustainable brands. This study aims to contribute to this underrepresented research field and makes recommendations for preferred brand personality dimensions for sustainable brands. First, the personality structure of sustainable consumers by using a preference-based two-step segmentation approach is investigated, and subsequent profiling of the sustainable consumer segment is conducted. The research relies on the results of an empirical discrete choice experiment and a personality test, including the data of a representative German consumer sample. Sustainable consumers were found to be highly agreeable and open. Second, the personality results of sustainable consumers are linked to consumers’ personality-specific preferred brand personalities. Third, recommendations for harmonic brand personality dimensions for sustainable brands, e.g., competence, excitement, and sincerity, are derived, and therefore, long-term, strategic, managerial implications are provided.
Unfreezing the Discursive Hegemonies Underpinning Current Versions of “Social Sustainability” in ECE Policies in Anglo–Celtic, Nordic and Continental Contexts
Authors: Sadownik Alicja R. , Bakken Yvonne , Gabi Josephine , Višnjić-Jevtić Adrijana , Koutoulas Jennifer
Published: April 2021

Social sustainability is linked to finding new ways of living together and strengthening social capital and participation, as well as to social justice and equity in societies, and it is becoming increasingly important for diverse multicultural societies. In this article, we trace understandings of social sustainability as established in Early Childhood Education (ECE) policy documents by following the chains of meaning connected to sense of belonging, local place and cultural diversity and through ECE collaboration with children’s parents/caregivers. Critical discourse analysis has been applied to trace the chains of meaning attached to these concepts in ECE steering documents in Australia, Croatia, Denmark, Norway, Poland, Serbia, Slovenia, Sweden and the UK (England, Scotland, Wales and Northern Ireland). Such analysis shows different ways in which the ECE polices indirectly work with social sustainability, as well as create critical distance from the sets of meanings established in each country (by proving a chain of meaning established in the policy documents of another country). In conclusion, we do not advocate in favour of any of the chains of meaning but argue for continual reflection and reflexivity, and we see research to be a particularly significant arena in which to unfreeze the taken for granted and sustainable notion.
Sustainable Mobility Driven Prioritization of New Vehicle Technologies, Based on a New Decision-Aiding Methodology
Authors: Anastasiadou Konstantina
Published: April 2021

In an era of environmental and socio-economic crisis, sustainable transport planning is vital as ever, especially given that the transport sector is responsible for the greatest part of total air pollution and greenhouse gas emissions. New vehicle technologies, such as autonomous and electric vehicles, emerge as promising alternatives, creating, however, both opportunities and challenges and raising questions relating to their performance. Can these new vehicle technologies really perform better than conventional ones in terms of sustainable mobility? Which one of them constitutes the optimum solution? How does each alternative perform with regard to different evaluation criteria, such as air pollution or road safety? In order to answer such questions, and to select the optimum solution, a comparison between autonomous, electric, autonomous electric and conventional vehicles is executed, based on a set of social, economic and environmental criteria. For this purpose, a new decision-aiding methodology, allowing for a holistic evaluation of the alternatives through a comprehensive literature review and experts’ participation, is applied. It is mainly based on the combined application of two hybrid multi-criteria analysis models, creating a more solid background towards optimum decision-making, thus constituting an important decision support tool for project appraisal and funding within the framework of sustainability in any sector.
Integration Versus Fragmentation, the Role of Minor Rural Networks in Rural Cultural Landscapes. A Study-Case in Spain
Authors: Tabasco Julio Plaza , Martínez Sánchez-Mateos Héctor S.
Published: April 2021

This work deals with the dichotomy between integration and fragmentation caused by artificial elements in the cultural landscapes, especially minor rural roads. In Europe, the rural matrix dominates the configuration of landscapes, and the agents of fragmentation can be analysed from different perspectives. For this purpose, the Land Parcel Information System, designed for the Common Agriculture Policy (CAP) is used as a data source to feed the indicators, allowing a high detail analysis, down to the parcel unit. It is applied to a case-study in Spain: the province of Ciudad Real. Here we find different landscape units with different rural and agrarian profiles to test the hypothesis. We use three indicators that allow us to explore the configuration of different cultural landscapes under the fragmentation perspective, using minor rural roads and other elements of the rural matrix that can only be observed at large scale. Then we calculate a composite indicator summarizing the fragmentation results of each unit. Results reveal a significative variability of fragmentation results regarding the land use and spatial patterns of the different cultural landscapes dominated by agrarian and rural factors, with a strong correspondence with the minor rural network underneath. Therefore, fragmentation can be interpreted as a dual process in cultural landscapes where the different land uses have different relations with the infrastructure network.
The Theoretical-Conceptual Model of Churning in Human Resources: The Importance of Its Operationalization
Authors: Pirrolas Olga Alexandra Chinita , Correia Pedro Miguel Alves Ribeiro
Published: April 2021

Given the current socio-economic context in which the labor market is set in, if we were to consider both employment opportunities in specific economic periods and the individual expectations workers have regarding one’s working conditions, it should be a matter of one’s individual right of choice to decide whether or not to stay or leave and change companies. The paper we present before you took into account the phenomenon of churning as a cyclical process. Our main goal was to understand the main causes leading to it in the context of human resources and, ultimately, what were the consequences emerging from it. In order to carry out this analysis, we put forward a conceptual-theoretical model of the phenomenon of churning, made possible through the analysis of both the currently available literature and the empirical studies and conclusions stemming from the latter. The aforementioned analysis allowed us to trace all main causes responsible for the origin of churning and also, to understand how it could be implemented further on, in order to minimize the costs associated with hiring new personnel and retaining qualified employees by resorting beforehand to the implementation of strategic measures of human resources retention. By applying the methodology based on grounded theory, this study allowed us to further contribute to the already available, yet limited, literature and definition of this multifaceted and greatly complex subject that is the phenomenon of churning.
Soil Respiration Characteristics and Influencing Factors for Apple Orchards in Different Regions on the Loess Plateau of Shaanxi Province
Authors: Hou Tingting , Wang Yanping , Guo Fuxing , Jia Qiong , Wu Xinnan , Wang Enguang , Hong Jingbo
Published: April 2021

To explore the characteristics of the spatial and temporal variation in soil respiration (SR) in orchard ecosystems in different regions of the Loess Plateau of Shaanxi Province and to distinguish the controlling factors, representative orchards were studied from April to October 2019. We conducted SR measurements in five locations, including Mizhi dangta (MZ), Ansai Liuzhuang (AS), Luochuan houzitou (LC), Fuping meijiaping (FP), and Yangling Wuquan (YL). The results indicated that the SR of each orchard showed clear seasonal variation. The SR increased with the distance from the trunk at the tree scale, while gradually increasing from north to south on the regional scale (p < 0.05). The soil temperature and soil moisture were the main factors controlling the seasonal changes in SR in the orchards. On the tree scale, the fine root biomass was the main factor causing the tree-scale spatial variability. At the regional scale, SR was mainly influenced by the differences in the soil temperature, soil moisture, soil organic carbon, soil bulk density and pH. In agricultural management practices, the cumulative soil respiration was higher with irrigated treatment than with non-irrigated (NI) treatment (p < 0.05). In addition, traditional surface drip irrigation (SDI) and root injection irrigation (RII) showed great differences in soil respiration in the early and late stages of irrigation (p < 0.05), and the soil moisture was the main controlling factor. Compared with no tillage (NI), green cover (GC), deep tillage (DT), and shallow tillage (ST) increased the SR by 57%, 36% and 14%, respectively (p < 0.05). Due to the great temporal and spatial variation in the SR in our study area, we determined that the soil respiration in the orchards was affected not only by environmental factors but also by agricultural measures. Therefore, greater attention should be paid to human factors when exploring SR to ensure that orchard management can promote the economic benefits of the orchards without greatly impacting the environment.
Applying Interactive Teaching Experience and Technology Action Puzzles in Disaster Prevention Education
Authors: Shyr Wen-Jye , Chen I-Min , Lee Jing-Chuan , Su Te-Jen
Published: April 2021

This study incorporated technology action puzzle and obstacle challenge activities in the course design. Using the 921 Earthquake in Taiwan as the theme, this study integrated the content of various subjects and course modules and applied information technology to present the humanistic care elements. The subjects of this study were Grade 9 students of a public middle school in central Taiwan. After the interactive operation introduction and theme film viewing, the students were divided into groups to participate in the technology action puzzle and obstacle challenge activities. Students’ learning performance using smart technological tools and overall course feedback were evaluated from the aspects of building structure safety knowledge, disaster prevention and mitigation, integrated interdisciplinary thinking, and problem-solving abilities through the course planning and quasi-experimental design. The results show that (1) in terms of the learning achievement scale, the pre-test and post-test of paired samples reached statistical significance; (2) in terms of the learning response scale, the mean of the Likert five-point scale reached above 4.0; (3) the results of mediating regression analysis show that, compared with the traditional classroom teaching mode, the interactive teaching experience and technology action puzzle have a mediating effect on learning performance and overall course feedback.
Establishment of a Sustainability Assessment System for Bridges
Authors: Liu Tai-Yi , Liu Guan-Ting , Chen Po-Han , Chou Nelson N. S. , Ho Shih-Ping
Published: April 2021

In this paper, with the motivation of “green” and “sustainability” for bridge projects, ten key indicators were selected to establish a reliable and applicable sustainability assessment system. It is also expecting the civil infrastructure to be developed more ecologically, environmentally, and friendly by applying the proposed assessment system. The data collection method in this research is questionnaire-based expert interviews. The questionnaire results were well analyzed and calculated for the key indicators’ weights by adopting the top two boxes theory (TTBT) and multiple attribute value theory (MAVT). Consequently, the sustainability assessment system for green civil infrastructure (SASGCI) was established to evaluate infrastructure projects’ sustainability achievements. In this study, the authors conclude the weights of ten key indicators for bridges as follows: risk mitigation and reliability: 15.3%, durability: 15.1%, landscape: 11.5%, ecology: 10.6%, benefit and function: 9.7%, environmental protection and carbon emissions reduction: 9.5%, waste reduction: 8.5%, energy-saving: 7.1%, creativity: 7.0%, and humanities and culture reservation: 5.6%. An example of a scenic bridge in Taiwan is presented to demonstrate the applicability of the established SASGCI for sustainability evaluation. The result indicates that the evaluation system is successful.
Using Fine-Grained Sediment and Wave Attenuation as a New Measure for Evaluating the Efficacy of Offshore Breakwaters in Stabilizing an Eroded Muddy Coast: Insights from Ca Mau, the Mekong Delta of Vietnam
Authors: Luom Thai Thanh , Phong Nguyen Tan , Anh Nguyen Tuan , Tung Nguyen Thanh , Tu Le Xuan , Duong Tran Anh
Published: April 2021

Offshore breakwaters can be effective in reducing the energy of incident waves through dissipation, refraction or reflection. Breakwaters are increasingly constructed to stabilize eroded muddy coasts, particularly in developing countries. Accumulation of fine-grained sediment and wave attenuation are two attributes of a stable muddy coast. Effective interventions in stabilizing eroded muddy coasts include two important elements: accumulation of fine-grained sediment and wave reduction. The efficacy of offshore breakwaters in stabilizing eroded muddy coasts is, however, not yet adequately understood. A crucial question needing attention is whether accumulation of fine-grained sediment and wave attenuation should be used in evaluating the efficacy of these offshore breakwaters in stabilizing eroded muddy coasts. To address this issue, a pile-rock offshore breakwater in Huong Mai, Tieu Dua of Ca Mau, Vietnam was selected as an appropriate example in this regard. Accumulation of fine-grained sediment and wave attenuation were tested as means to investigate the efficacy of the Huong Mai structure in stabilizing the eroded muddy coast. The study was undertaken using field-based measurements and semi-structured interviews in three stages between October 2016 and December 2020. We found that this structure has had limited efficacy in stabilizing the eroded muddy coast. The structure was effective in dissipating the energy of incident waves, but we found no evidence of fine-grained sediment accumulation due to an inappropriate structural design. There was also no monitoring system in place, leading to difficulties in evaluating its efficacy in terms of wave attenuation and accumulation of fine-grained sediment. The gaps between the shoreline and the structure have not been adequately explained, resulting in substantial challenges in replicating the structure elsewhere. The Huong Mai structure should be strengthened using supplementary measures and granulometric tests in order to improve the efficacy in stabilizing eroded muddy coasts. The methods in this study provide new insights in this regard.
Evaluation of Fourteen Bread Wheat (Triticum aestivum L.) Genotypes by Observing Gas Exchange Parameters, Relative Water and Chlorophyll Content, and Yield Attributes under Drought Stress
Authors: Wasaya Allah , Manzoor Sobia , Yasir Tauqeer Ahmad , Sarwar Naeem , Mubeen Khuram , Ismail Ismail A. , Raza Ali , Rehman Abdul , Hossain Akbar , EL Sabagh Ayman
Published: April 2021

Water scarceness is a major threat to wheat productivity under changing climate scenarios, especially in arid and semi-arid regions. However, growing drought-tolerant wheat genotypes could be a sustainable option to enhance wheat productivity under drought stress conditions. The aim of this study was to evaluate the effect of mild to severe drought stress on gas exchange parameters, relative water content, SPAD-chlorophyll value, and yield-related parameters of 14 wheat genotypes being cultivated in arid to semi-arid areas on large scale. The genotypes were grown in earthen pots under three drought levels, namely (1) control-well watered, (2) mild water stress, i.e., 60% water holding capacity, and (3) severe water stress, i.e., 40% water holding capacity. The drought was imposed from the jointing stage to physiological maturity. Drought significantly decreased net photosynthesis, stomatal conductance, relative water contents, 100-grain weight, and grain yield in all genotypes. However, the reduction percentage was different in different genotypes under drought stress compared with well-watered conditions. The highest relative water content (65.2%) was maintained by the genotype Galaxy-2013, followed by AAS-2011 (64.6%) and Johar-2016 (62.3%) under severe drought conditions. Likewise, Galaxy-2013 showed the highest net photosynthesis and stomatal conductance under severe drought conditions. The highest grain yield per plant (6.2 g) and 100-grain weight (3.3 g) was also recorded in Galaxy-2013 under severe drought conditions, while the highest grain yield under well-watered conditions was recorded in Johar-2016, followed by Galaxy-2013. These results suggest that wheat variety Galaxy-2013 could be cultivated extensively to obtain good wheat yield under limited water conditions.
Effects of Deoxynivalenol and Mycotoxin Adsorbent Agents on Mitogen-Activated Protein Kinase Signaling Pathways and Inflammation-Associated Gene Expression in Porcine Intestinal Epithelial Cells
Authors: Yu Yu-Hsiang , Lai Yi-Han , Hsiao Felix Shih-Hsiang , Cheng Yeong-Hsiang
Published: April 2021

Deoxynivalenol (DON) is the most prevalent mycotoxin in swine feedstuffs. The intestinal epithelial cells represent the first target for the DON. Here, we studied the effects of DON and mycotoxin adsorbent agents on mitogen-activated protein kinase (MAPK) signaling pathways and inflammation-associated gene expression in porcine intestinal epithelial cells (IPEC-J2). Results showed that phosphorylation of MAPK signaling pathways (p38, ERK, and JNK) was increased after treatment of DON or lipopolysaccharide (LPS) in IPEC-J2 cells. The phosphorylation of p38, ERK, and JNK was not further enhanced after co-treatment with DON and LPS. The inos and cox-2 mRNA expression were significantly induced at 6 h after treatment of DON. DON treatment significantly increased the claudin 3 and occludin mRNA expression at 12 h. DON in combination with LPS treatment did not further increase the inflammation and tight junction-associated gene expression. The DON-induced phosphorylation of MAPK signaling pathways was impaired by mycotoxin adsorbent agent (nanoscale silicate platelets and the mixture of montmorillonites and yeast cell walls) treatment, thereby decreasing inflammation and tight junction-associated gene expression. Taken together, these findings demonstrate that DON triggers the inflammation in IPEC-J2 cells by phosphorylation of MAPK signaling pathways and LPS does not further augment the DON-induced inflammatory responses. Mycotoxin adsorbent agents can attenuate DON-induced inflammatory responses in IPEC-J2 cells through modulation of the phosphorylation of p38, ERK, and JNK.