bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform
Authors: Deng Xinping , Guo Shanxin , Sun Luyi , Chen Jinsong
Published: July 2020

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations.
Drought Impacts on Vegetation in Southeastern Europe
Authors: Páscoa Patrícia , Gouveia Célia , Russo Ana , Bojariu Roxana , Vicente-Serrano Sergio , Trigo Ricardo
Published: July 2020

We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation Evaporation Index (SPEI), at several time scales, and the Normalized Difference Vegetation Index (NDVI), as well as an assessment of the simultaneous occurrence of extremes in both indices. The analysis of the relationship between drought and vegetation was made for the growing season (from April to October of the entire period), and special attention was devoted to the severe drought event of 2000/2001, considered as the driest since 1961 for the study area. More than three quarters (77%) of the agricultural land exhibits a positive correlation between the two indices. The sensitivity of crop areas to drought is strong, as the impacts were detected from May to October, with a peak in July. On the other hand, forests were found to be less sensitive to drought, as the impacts were limited mostly to July and August. Moreover, vegetation of all land cover classes showed a dependence between the sign of the correlation and the elevation gradient. Roughly 60% (20%) of the study domain shows a concordance of anomalously low vegetation activity with dry conditions of at least 50% (80%) in August. By contrast, a lower value of concordance was observed over the Carpathian Mountains. During the severe drought event of 2000/2001, a decrease in vegetation activity was detected for most of the study area, showing a decrease lasting at least 4 months, between April and October, for more than two thirds (71%) of the study domain.
A New Method of De-Aliasing Large-Scale High-Frequency Barotropic Signals in the Mediterranean Sea
Authors: Hu Denghui , Xu Yongsheng
Published: July 2020

With the development of satellite observation technology, higher resolution and shorter return cycle have also placed higher demands on satellite data processing. The non-tide high-frequency barotropic oscillation in the marginal sea produces large aliasing errors in satellite altimeter observations. In previous studies, the satellite altimeter aliasing correction generally relied on a few bottom pressure data or the model data. Here, we employed the high-frequency tide gauge data to extract the altimeter non-tide aliasing correction in the west Mediterranean Sea. The spatial average method and EOF analysis method were adopted to track the high-frequency oscillation signals from 15 tide gauge records (TGs), and then were used to correct the aliasing errors in the Jason-1 and Envisat observations. The results showed that the EOF analysis method is better than the spatial average method in the altimeter data correction. After EOF correction, 90% of correlation (COR) between TG and sea level of Jason-1 has increased ~5%, and ~3% increase for the Envisat sea level; for the spatial average correction method, only ~70% of Jason-1 and Envisat data at the TGs location has about 2% increase in correlation. The EOF correction reduced the average percentage of error variance (PEL) by ~30%, while the spatial average correction increased the average percentage of PEL by ~20%. After correction by the EOF method, the altimeter observations are more consistent with the distribution of strong currents and eddies in the west Mediterranean Sea. The results prove that the proposed EOF method is more effective and accurate for the non-tide aliasing correction.
Hydrometeor Identification Using Multiple-Frequency Microwave Links: A Numerical Simulation
Authors: Pu Kang , Liu Xichuan , Hu Shuai , Gao Taichang
Published: July 2020

A method for identifying hydrometeor types (rain, graupel, and wet snow) based on a microwave link is proposed in this paper. The measured hydrometeor size distribution (HSD) data from the winters of 2014 to 2019 in Nanjing, China, were used to carry out simulation experiments to verify the performance of the model. Single-, dual-, and tri-frequency models (combinations of 15 GHz, 18 GHz, 25 GHz, 38 GHz, 50 GHz, 60 GHz, 70 GHz, and 80 GHz) were established with the extreme learning machine (ELM) algorithm. The results showed that the performance of the tri-frequency models was overall better than that of the dual-frequency models, for which the performance was better than that of the single-frequency models. The mean (maximum) test set accuracies of the single-frequency, dual-frequency, and tri-frequency models reached 75.8%, 80.7%, and 83.2% (83.0%, 84.4%, and 85.6%), respectively. For the dual-frequency and tri-frequency models, it was found that the accuracy increased with the overall frequency or the frequency difference. In addition, the influences of different noise levels on the model performance were also analyzed. Finally, the effects of position and length of link relative to precipitation cell were analyzed and are also discussed.
Long-Term Grass Biomass Estimation of Pastures from Satellite Data
Authors: Clementini Chiara , Pomente Andrea , Latini Daniele , Kanamaru Hideki , Vuolo Maria Raffaella , Heureux Ana , Fujisawa Mariko , Schiavon Giovanni , Del Frate Fabio
Published: July 2020

The general consensus on future climate projections poses new and increased concerns about climate change and its impacts. Droughts are primarily worrying, since they contribute to altering the composition, distribution, and abundance of species. Grasslands, for example, are the primary source for grazing mammals and modifications in climate determine variation in the available yields for cattle. To support the agriculture sector, international organizations such as the Food and Agriculture Organization (FAO) of the United Nations are promoting the development of dedicated monitoring initiatives, with particular attention for undeveloped and disadvantaged countries. The temporal scale is very important in this context, where long time series of data are required to compute consistent analyses. In this research, we discuss the results regarding long-term grass biomass estimation in an extended African region. The results are obtained by means of a procedure that is mostly automatic and replicable in other contexts. Zambia has been identified as a significant test area due to its vulnerability to the adverse impacts of climate change as a result of its geographic location, socioeconomic stresses, and low adaptive capacity. In fact, analysis and estimations were performed over a long time window (21 years) to identify correlations with climate variables, such as precipitation, to clarify sensitivity to climate change and possible effects already in place. From the analysis, decline in both grass quality and quantity was not currently evident in the study area. However, pastures in the considered area were found to be vulnerable to changing climate and, in particular, to the water shortages accompanying drought periods.
An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints
Authors: Ren Hang , Hu Taotao
Published: July 2020

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.
Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method
Authors: Moll Jochen , Schmidt Matthias , Käsgen Johannes , Mehldau Jörg , Bücker Marcel , Haupt Felix
Published: July 2020

This paper presents a proof of concept for simultaneous load and structural health monitoring of a hybrid carbon fiber rudder stock sample consisting of carbon fiber composite and metallic parts in order to demonstrate smart sensors in the context of maritime systems. Therefore, a strain gauge is used to assess bending loads during quasi-static laboratory testing. In addition, six piezoelectric transducers are placed around the circumference of the tubular structure for damage detection based on the electro-mechanical impedance (EMI) method. A damage indicator has been defined that exploits the real and imaginary parts of the admittance for the detection of pin failure in the rudder stock. In particular, higher frequencies in the EMI spectrum contain valuable information about damage. Finally, the information about damage and load are merged in a cluster analysis enabling damage detection under load.
Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case
Authors: Pisa Ivan , Morell Antoni , Vicario Jose Lopez , Vilanova Ramon
Published: July 2020

The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements—when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively.
Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain
Authors: Shahzad Aamir , Zhang Kaiwen , Gherbi Abdelouahed
Published: July 2020

Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.
Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning
Authors: Althobaiti Turke , Katsigiannis Stamos , Ramzan Naeem
Published: July 2020

The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named “ShimFall&ADL” will be publicly released to facilitate further research on the field.
Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data
Authors: Gómez-del-Río Nazaret , González-González Carina S. , Toledo-Delgado Pedro A. , Muñoz-Cruz Vanesa , García-Peñalvo Francisco
Published: July 2020

At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a considerable amount of tracked activity data. Consequently, our approach is based on the quantified-self model for recommending healthy activities. Gamification can also be used as a mechanism to enhance personalization, increasing user motivation. This paper describes the quantified-self model and its data sources, the activity recommender system, and the PROVITAO App user experience model. Furthermore, it presents the results of a gamified program applied for three years in children with obesity and the process of evaluating the quantified-self model with experts. Positive outcomes were obtained in children’s medical parameters and health habits.
Architecture for Trajectory-Based Fishing Ship Classification with AIS Data
Authors: Sánchez Pedroche David , Amigo Daniel , García Jesús , Molina José Manuel
Published: July 2020

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.
Foot-Mounted Pedestrian Navigation Method by Comparing ADR and Modified ZUPT Based on MEMS IMU Array
Authors: Xing Li , Tu Xiaowei , Chen Zhi
Published: July 2020

Using an MEMS Inertial Measurement Unit (MEMS IMU) array mounted on foot is a feasible approach to improve the pedestrian tracking accuracy for the pedestrian navigation system (PNS). Based on the in-house developed IMU array, the paper proposes a new integrated framework that combines adaptive deck reckoning (ADR) with the modified zero velocity update (ZUPT). In the proposed ADR algorithm, the IMUs with large drift errors on the array are selected and removed according to the step length and the track angle computed by each IMU on the array. Then, by using the step length and the track angle of each step computed by remaining IMUs, the foot position extracted from the traditional ZUPT model is estimated on the basis of least squares (LS) so as to improve the traveled distance calculation accuracy. Compared with the traditional IMU array fusion method based on a maximum likelihood estimator (MLE) when it is used in the PNS, which is approximately taking the mean value of array readings, the proposed method is equivalent to adaptively fusing the array readings and thus improves the pedestrian tracking accuracy. To compare the proposed method with MLE, two different types of walking tracks are designed. The 161 m straight line experiments show that the end position by ADR/modified ZUPT method is much closer to the one of the reference trajectory compared with the MLE in repeated walks, and the closed-loop tracks about 300 m show that the positioning error with respect to the total traveled distance is less than 0.6% (1σ), which is higher than 1% (1σ) of MLE.
Local Seeing Measurement for Increasing Astrophysical Observatory Quality Images Using an Autonomous Wireless Sensor Network
Authors: Parada Raúl , Rueda-Teruel Sergio , Monzo Carlos
Published: July 2020

Astrophysical observatories (AOs) are used to acquire high-quality images from the sky. However, AOs are amenable to distortion phenomena such as seeing. In this paper, we consider specifically the local seeing produced from indoor and outdoor temperature variations. Local seeing contributes to the captured image quality, therefore it must be measured. Local seeing has been considered, to the best of our knowledge, in observatories using ad hoc solutions, typically with high cost and complexity. This paper presents the complete development of an autonomous wireless sensor network (WSN) composed of temperature-measuring for real-time local seeing measurement. Therefore, a WSN is deployed using Zigbee as a data communication exchange. As a result, a long continuous-operating system is constructed and tested in a real AO infrastructure. Finally, we calculate a preliminary dome local seeing, from the acquired temperature data, achieving reasonable results.
Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines
Authors: Zhang Jian , Soangra Rahul , E. Lockhart Thurmon
Published: July 2020

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.
Process Controls of the Live Root Zone and Carbon Sequestration Capacity of the Sundarbans Mangrove Forest, Bangladesh
Authors: Bomer Edwin J. , Wilson Carol A. , Elsey-Quirk Tracy
Published: July 2020

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr−1. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.
Role of Ionic Liquids in Composites in Analytical Sample Preparation
Authors: Gutiérrez-Serpa Adrián , Napolitano-Tabares Patricia I. , Šulc Jakub , Pacheco-Fernández Idaira , Pino Verónica
Published: July 2020

Ionic liquids (ILs) are a group of non-conventional salts with melting points below 100 °C. Apart from their negligible vapor pressure at room temperature, high thermal stability, and impressive solvation properties, ILs are characterized by their tunability. Given such nearly infinite combinations of cations and anions, and the easy modification of their structures, ILs with specific properties can be synthesized. These characteristics have attracted attention regarding their use as extraction phases in analytical sample preparation methods, particularly in liquid-phase extraction methods. Given the liquid nature of most common ILs, their incorporation in analytical sample preparation methods using solid sorbents requires the preparation of solid derivatives, such as polymeric ILs, or the combination of ILs with other materials to prepare solid IL-based composites. In this sense, many solid composites based on ILs have been prepared with improved features, including magnetic particles, carbonaceous materials, polymers, silica materials, and metal-organic frameworks, as additional materials forming the composites. This review aims to give an overview on the preparation and applications of IL-based composites in analytical sample preparation in the period 2017–2020, paying attention to the role of the IL material in those composites to understand the effect of the individual components in the sorbent.
Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
Authors: Drachal Krzysztof
Published: July 2020

The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.
The Nexus between Market Needs and Value Attributes of Smart City Solutions towards Energy Transition. An Empirical Evidence of Two European Union (EU) Smart Cities, Evora and Alkmaar
Authors: Giourka Paraskevi , Apostolopoulos Vasilis , Angelakoglou Komninos , Kourtzanidis Konstantinos , Nikolopoulos Nikos , Sougkakis Vasileios , Fuligni Federica , Barberis Stefano , Verbeek Karin , Costa José Miguel , Formiga João
Published: July 2020

This study presents an experiential process and a market-oriented approach for realizing cities’ energy transition through smart solutions. The aim of this study is twofold: (a) present a process for defining a repository of innovative solutions that can be applied at building, district, or city level, for two European Union cities, Evora and Alkmaar, and support the deployment of positive energy districts enabling a sustainable energy transition, and (b) understand in a systematic way the attributes of value offered by energy-related smart city solutions, in order to facilitate the development of sustainable value propositions that can successfully address city needs. The repository is assessed against four elements of value, which include social impact, life-changing, emotional, and functional attributes, according to the value pyramid of Maslow. Results show that the value attributes of quality, motivation, integration, cost reduction, information, and organization are highly relevant to the proposed smart solutions. The results presented in this study are useful for city planners, decision-makers, public bodies, citizens, and businesses interested in designing their energy transition strategy and defining novel technologies that promote urban energy sustainability.
COVID-19 Infects Real Estate Markets: Short and Mid-Run Effects on Housing Prices in Campania Region (Italy)
Authors: Del Giudice Vincenzo , De Paola Pierfrancesco , Del Giudice Francesco Paolo
Published: July 2020

The COVID-19 (also called “SARS-CoV-2”) pandemic is causing a dramatic reduction in consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be added an increase in unemployment, which will further depress consumption. The real estate market, as for other productive and commercial sectors, in the short and mid-run, will not tend to move independently from the context of the aforementioned economic variables. The effect of pandemics or health emergencies on housing markets is an unexplored topic in international literature. For this reason, firstly, the few specific studies found are reported and, by analogy, studies on the effects of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently, beginning from the real estate dynamics and economic indicators of the Campania region before the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment, personal and household income, real estate judicial execution, real estate dynamics). Finally, a real estate pricing model is developed, evaluating the short and mid-run COVID-19 effects on housing prices. To predict possible changes in the mid-run of real estate judicial execution and real estate dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in the mid-run (late 2020–early 2021).
Impact of Research and Development Strategy on Sustainable Growth in Multinational Pharmaceutical Companies
Authors: Teramae Fumio , Makino Tomohiro , Lim Yeongjoo , Sengoku Shintaro , Kodama Kota
Published: July 2020

Research and development (R&D) productivity is continuously declining, and it is said that the conventional model of pharmaceutical business is becoming obsolete. Many research studies on R&D productivity focus on inputs (e.g., strategic transactions to absorb external innovation, R&D expenditures), outputs (e.g., approvals of a new drug), and outcomes (e.g., total sales, incomes). However, few prior studies address the relationship among these three components simultaneously. Therefore, we comprehensively analyzed factors affecting R&D productivity by statistically examining a sample of 30 large multinational companies. Our results show that strategic transactions do not increase the number of approved drugs and negatively affect growth in terms of total sales. Additionally, our results show that a home-region-oriented international strategy positively affects total sales, thus indicating that responsiveness to local medical needs is important for sustainable growth. This paper contributes to the body of research on R&D productivity in the pharmaceutical industry.
Understanding and Managing Vacant Houses in Support of a Material Stock-Type Society—The Case of Kitakyushu, Japan
Authors: Wuyts Wendy , Sedlitzky Raphael , Morita Masato , Tanikawa Hiroki
Published: July 2020

From a sustainable material management perspective, vacant houses represent material stock and still have potential in the circular economy. This article addresses two aspects of understanding and managing vacant houses: the difficulty of understanding their spatial and temporal patterns and the management of the social costs behind the phenomenon of vacant houses. These aspects are approached by combining a 4D GIS analysis with expert interviews and additional qualitative tools to assess the spatial and temporal dimension of vacant houses. Furthermore, this manuscript presents a tool to estimate the obsolete dwelling material stock distribution within a city. The case of the city of Kitakyushu demonstrates the relationship that exists between the historical trajectories of housing norms and standards, such as comfort, cleanliness, safety, and convenience, and the dynamics of the built material stock and demography for three selected neighbourhoods. The results show that the more locked-in a district is in terms of “obsolete norms and codes”, the more likely it is that the obsolete stock is dead, and consequently, urban mining should be considered. The article concludes that a revisiting of the norms and standards of convenience and other domains is one of the prerequisites of the transition toward a circular built environment and the prevention of obsolete stock accumulation.
Grocery Shopping Preferences during the COVID-19 Pandemic
Authors: Grashuis Jasper , Skevas Theodoros , Segovia Michelle S.
Published: July 2020

Considering the temporary closure of many food-away-from-home establishments, consumer expenditure on groceries during the COVID-19 pandemic has increased. While grocery shopping is an essential activity, not much is known about the dynamic relationship of the COVID-19 pandemic to the behavior of grocery shoppers. With an objective to inform variability in the behavior of grocery shoppers under various scenarios of the COVID-19 pandemic, we conducted an online framed choice experiment to elicit preferences for purchasing methods, time windows, minimum order requirements, and fees. The manipulating factor relates to the trend in the COVID-19 pandemic, where we consider three scenarios: an increasing, decreasing, or constant number of new cases in the past two-week period. Using 32,400 choice decisions from a representative sample of 900 grocery shoppers in the United States, we conclude that the trend in the COVID-19 pandemic causes significant differences in grocery shopping preferences. In situations where COVID-19 is spreading at an increasing rate, consumers are generally less willing to shop inside the grocery store. When COVID-19 is spreading at a decreasing rate, the relative importance of the purchasing method attribute is lower in its entirety. We use our findings to inform recommendations for practitioners and policymakers.
Evaluation and Selection Methods of Tourism Characteristic Town: The Case of Liaoning Province, China
Authors: Zhou Fang , Zhao Fei , Xu Qingwei , Yuan Yongbo , Zhang Mingyuan
Published: July 2020

The construction of a characteristic town is an important measure to promote the economic transformation and upgrading of China. It also promotes the coordinated and balanced development between urban and rural areas, and the construction of new urbanization and new rural areas. At present, China’s township governments are actively declaring construction projects of characteristic town. Considering the limitation of resources and funds, the government needs to choose projects with better foundation and better development prospects to invest and build, to avoid the waste of resources brought about by blind construction. Starting from the pre-selection of the construction projects of the characteristic town, taking Liaoning Province as an example, this research divided the characteristic town into four categories. Initially established the characteristic town evaluation index system blending comprehensive and multivariate index; then optimizes the index system through the statistical analysis, reliability analysis; and validity analysis of the questionnaire survey to finally obtain the selection index system of the construction projects of the characteristic town of Liaoning Province. Secondly, using the combination of Analytic Network Process (ANP) and Improved Technique for Order Preference by Similarity to an Ideal Solution (Improved-TOPSIS) method, the selection evaluation model of tourism-type characteristic town construction projects are obtained. Finally, taking the three tourism-type characteristic town projects declared by Liaoning Province as an example, using the ANP and Improved-TOPSIS evaluation model to evaluate and compare the three towns. The priority of “Xietun Town, Tanghe Town, Zhaoquanhe Town” is obtained. The result is consistent with the recommended order and verifies the applicability of the selection model. At the same time, through the feedback of the evaluation process, the current development constraints of the three towns are clarified, and the future development direction of the town is pointed out.
Higher-Order Velocity Moments, Turbulence Scales and Energy Dissipation Rate around a Boulder in a Rock-Ramp Fish Passage
Authors: Golpira Amir , Baki Abul BM , Zhu David Z.
Published: July 2020

This experimental study investigated the higher-order velocity moments, turbulence time and length scales, and energy dissipation rates around an intermediately submerged boulder within a wake-interference flow regime in a rock-ramp fish passage. The results show a noticeable variation in the studied parameters in the wake of the boulder, as well as near the bed and boulder crest. The higher-order velocity moments show the presence of infrequent strong ejections downstream of the boulder, which may lead to higher sediment deposition and vertical mixing. The eddy length scales and the volumetric energy dissipation in this experimental model were discussed in relation to fish behavior for both the experimental model and a prototype. Relationships were proposed to roughly estimate integral length scales and energy dissipation rates around the boulder over the flow depth. The findings of this study may improve the design of rock-ramp fish passages considering the effects of turbulence on fish swimming performance and sediment transport.
Gendered Roles in Agrarian Transition: A Study of Lowland Rice Farming in Lao PDR
Authors: Moglia Magnus , Alexander Kim S. , Larson Silva , (Giger)-Dray Anne , Greenhalgh Garry , Thammavong Phommath , Thephavanh Manithaythip , Case Peter
Published: July 2020

Traditional lifestyles of lowland rice farmers of the southern provinces of Lao People’s Democratic Republic are rapidly changing, due to two important trends. Firstly, there is a push towards modernization and commercialization of farming. Secondly, though farmers still focus on rice farming as a key activity, there is an increasing move towards diversification of livelihoods. The changes have seen the uptake of non-rice crops, livestock husbandry and forest and river utilization; as well as non-farming activities. This has influenced gender relations, impacting household agricultural production decisions and amplified transitional trends. To explore the processes, we analyzed data from a study of innovation adoption amongst rice farmers in southern Lao PDR. The study revealed nuances of gender-based differences in the priorities and attitudes towards farming and off-farm activities, as well as differences in behaviour related to the adoption of new practices. Women were more focused on non-farming practices and considered engaging in the modern, non-traditional, economy more so than men. Women also reported experiencing greater challenges when engaging and trading in the agricultural marketplace. The study supports the importance of taking a gendered approach to understanding the inherent complexities within agrarian change.
Microbial Communities Associated with Acetaminophen Biodegradation from Mangrove Sediment
Authors: Yang Chu-Wen , Chen Yi-En , Chang Bea-Ven
Published: July 2020

Acetaminophen (ACE) is a widely used medicine. Currently, concerns regarding its potential adverse effects on the environments are raised. The aim of this study was to evaluate ACE biodegradation in mangrove sediments under aerobic and anaerobic conditions. Three ACE biodegradation strategies in mangrove sediments were tested. The degradation half-lives (t1/2) of ACE in the sediments with spent mushroom compost under aerobic conditions ranged from 3.24 ± 0.16 to 6.25 ± 0.31 d. The degradation half-lives (t1/2) of ACE in sediments with isolated bacterial strains ranged from 2.54 ± 0.13 to 3.30 ± 0.17 d and from 2.62 ± 0.13 to 3.52 ± 0.17 d under aerobic and anaerobic conditions, respectively. The degradation half-lives (t1/2) of ACE in sediments amended with NaNO3, Na2SO4 and NaHCO3 under anaerobic conditions ranged from 1.16 ± 0.06 to 3.05 ± 0.15 d, 2.39 ± 0.12 to 3.84 ± 0.19 d and 2.79 ± 0.14 to 10.75 ± 0.53 d, respectively. The addition of the three electron acceptors enhanced ACE degradation in mangrove sediments, where NaNO3 yielded the best effects. Sixteen microbial genera were identified as the major members of microbial communities associated in anaerobic ACE degradation in mangrove sediments with addition of NaNO3 and Na2SO4. Three (Arthrobacter, Enterobacter and Bacillus) of the sixteen microbial genera were identified in the isolated ACE-degrading bacterial strains.
The Effect of Corporate Social Responsibility on Employees’ Organizational Citizenship Behavior: A Moderated Mediation Model of Grit and Meaning Orientation
Authors: Choi Jinsoo , Sohn Young Woo , Lee Suran
Published: July 2020

Even though research on the micro-effects of corporate social responsibility (CSR) has been growing, our understanding of the psychological mechanisms through which perceived CSR affects employees’ organizational citizenship behavior (OCB) remains insufficient. Based on social identity theory and the concept of whole self, this study examines the mediating role of meaning orientation as a psychological mechanism and tests the moderating role of grit, using survey data collected from 361 South Korean workers. Our results show that the positive association between perceived CSR and OCB is mediated by meaning orientation. In addition, grit strengthens the perceived CSR-meaning orientation relationship, which in turn boosts employees’ OCB. This study contributes to the literature by revealing a psychological mechanism and how it is moderated by individual traits, which are crucial in predicting CSR’s micro-effects. We discuss how our results can be practically applied by organizations as part of sustainable human resource management.
Entrepreneurial Orientation, Resource Orchestration Capability, Environmental Dynamics and Firm Performance: A Test of Three-Way Interaction
Authors: Choi Suk Bong , Lee Wang Ro , Kang Seung-Wan
Published: July 2020

This study investigated the effect of entrepreneurial orientation on firm performance with the firm resource orchestration capability and environmental dynamics in moderating roles. Using survey data collected from 301 Korean manufacturing and service firms, we devised a three-way interaction model to uncover the complex and dynamic conditions that maximize the effect of entrepreneurial orientation on firm performance. We found a positive association between entrepreneurial orientation and firm performance. Moreover, our findings indicated that both the firm resource orchestration capability and environmental dynamics played positive moderating roles in the above relationship. The results also showed that, in the case of a high level of environmental dynamics, entrepreneurial orientation was more positively related to firm performance for firms with a high resource orchestration capability. In addition, in the case of low resource orchestration capability, entrepreneurial orientation was associated more positively with firm performance for firms with high environmental dynamics. Thus, this study confirmed the importance of interaction between the three factors for enhancing firm performance. Furthermore, our investigation of substantial moderators provided key insights regarding the conditions that better explain how entrepreneurial orientation promotes firm performance. In addition to two-way interaction, the support for a three-way interaction suggests that moderators of the relationship interact to further explain the relationship. The theoretical and practical implications are discussed.
Basic Psychological Needs as a Motivational Competence: Examining Validity and Measurement Invariance of Spanish BPNSF Scale
Authors: Cardella Giuseppina Maria , Hernández-Sánchez Brizeida Raquel , Sánchez-García José Carlos
Published: July 2020

Motivation is an essential component in higher education. In this area, researchers have described three psychological needs that drive human behavior: Autonomy, competence, and relatedness. These three needs are central dimensions of Self-Determination Theory. Various measurement tools have been used to evaluate these dimensions. Despite the universality of these, the literature lacks validation of them in the Spanish university context. The present study aims to analyze the psychometric properties of the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) in this context. This scale was administered to a sample of 1075 Spanish university students. Confirmatory factor analysis yielded that the six-factor structure fitted the data. Results supported the reliability and validity of all the subscales. The obtained factor structure provided evidence in support of the comparability of the model between male and female students and different types of studies.