bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Identification of Crop Tree Crowns from UAV Multispectral Imagery by Means of Morphological Image Analysis
Authors: Sarabia Ricardo , Aquino Arturo , Ponce Juan Manuel , López Gilberto , Andújar José Manuel
Published: February 2020

Within the context of precision agriculture, goods insurance, public subsidies, fire damage assessment, etc., accurate knowledge about the plant population in crops represents valuable information. In this regard, the use of Unmanned Aerial Vehicles (UAVs) has proliferated as an alternative to traditional plant counting methods, which are laborious, time demanding and prone to human error. Hence, a methodology for the automated detection, geolocation and counting of crop trees in intensive cultivation orchards from high resolution multispectral images, acquired by UAV-based aerial imaging, is proposed. After image acquisition, the captures are processed by means of photogrammetry to yield a 3D point cloud-based representation of the study plot. To exploit the elevation information contained in it and eventually identify the plants, the cloud is deterministically interpolated, and subsequently transformed into a greyscale image. This image is processed, by using mathematical morphology techniques, in such a way that the absolute height of the trees with respect to their local surroundings is exploited to segment the tree pixel-regions, by global statistical thresholding binarization. This approach makes the segmentation process robust against surfaces with elevation variations of any magnitude, or to possible distracting artefacts with heights lower than expected. Finally, the segmented image is analysed by means of an ad-hoc moment representation-based algorithm to estimate the location of the trees. The methodology was tested in an intensive olive orchard of 17.5 ha, with a population of 3919 trees. Because of the plot’s plant density and tree spacing pattern, typical of intensive plantations, many occurrences of intra-row tree aggregations were observed, increasing the complexity of the scenario under study. Notwithstanding, it was achieved a precision of 99.92%, a sensibility of 99.67% and an F-score of 99.75%, thus correctly identifying and geolocating 3906 plants. The generated 3D point cloud reported root-mean square errors (RMSE) in the X, Y and Z directions of 0.73 m, 0.39 m and 1.20 m, respectively. These results support the viability and robustness of this methodology as a phenotyping solution for the automated plant counting and geolocation in olive orchards.
Multi-Modal, Remote Breathing Monitor
Authors: Regev Nir , Wulich Dov
Published: February 2020

Monitoring breathing is important for a plethora of applications including, but not limited to, baby monitoring, sleep monitoring, and elderly care. This paper presents a way to fuse both vision-based and RF-based modalities for the task of estimating the breathing rate of a human. The modalities used are the F200 Intel® RealSenseTM RGB and depth (RGBD) sensor, and an ultra-wideband (UWB) radar. RGB image-based features and their corresponding image coordinates are detected on the human body and are tracked using the famous optical flow algorithm of Lucas and Kanade. The depth at these coordinates is also tracked. The synced-radar received signal is processed to extract the breathing pattern. All of these signals are then passed to a harmonic signal detector which is based on a generalized likelihood ratio test. Finally, a spectral estimation algorithm based on the reformed Pisarenko algorithm tracks the breathing fundamental frequencies in real-time, which are then fused into a one optimal breathing rate in a maximum likelihood fashion. We tested this multimodal set-up on 14 human subjects and we report a maximum error of 0.5 BPM compared to the true breathing rate.
A Near-infrared Turn-on Fluorescent Sensor for Sensitive and Specific Detection of Albumin from Urine Samples
Authors: Kim Yoonjeong , Shin Eunryeol , Jung Woong , Kim Mi Kyoung , Chong Youhoon
Published: February 2020

A readily synthesizable fluorescent probe DMAT-π-CAP was evaluated for sensitive and selective detection of human serum albumin (HSA). DMAT-π-CAP showed selective turn-on fluorescence at 730 nm in the presence of HSA with more than 720-fold enhancement in emission intensity ([DMAT-π-CAP] = 10 μM), and rapid detection of HSA was accomplished in 3 s. The fluorescence intensity of DMAT-π-CAP was shown to increase in HSA concentration-dependent manner (Kd = 15.4 ± 3.3 μM), and the limit of detection of DMAT-π-CAP was determined to be 10.9 nM (0.72 mg/L). The 1:1 stoichiometry between DMAT-π-CAP and HSA was determined, and the displacement assay revealed that DMAT-π-CAP competes with hemin for the unique binding site, which rarely accommodates drugs and endogenous compounds. Based on the HSA-selective turn-on NIR fluorescence property as well as the unique binding site, DMAT-π-CAP was anticipated to serve as a fluorescence sensor for quantitative detection of the HSA level in biological samples with minimized background interference. Thus, urine samples were directly analyzed by DMAT-π-CAP to assess albumin levels, and the results were comparable to those obtained from immunoassay. The similar sensitivity and specificity to the immunoassay along with the simple, cost-effective, and fast detection of HSA warrants practical application of the NIR fluorescent albumin sensor, DMAT-π-CAP, in the analysis of albumin levels in various biological environments.
Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions
Authors: Yao Yong , Zhang Sen , Yang Suixian , Gui Gui
Published: February 2020

The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.
Method to Determine the Far-Field Beam Pattern of A Long Array From Subarray Beam Pattern Measurements
Authors: Jung Donghwan , Kim Jeasoo
Published: February 2020

Beam pattern measurement is essential to verifying the performance of an array sonar. However, common problems in beam pattern measurement of arrays include constraints on achieving the far-field condition and reaching plane waves mainly due to limited measurement space as in acoustic water tank. For this purpose, the conventional method of measuring beam patterns in limited spaces, which transform near-field measurement data into far-field results, is used. However, the conventional method is time-consuming because of the dense spatial sampling. Hence, we devised a method to measure the beam pattern of a discrete line array in limited space based on the subarray method. In this method, a discrete line array with a measurement space that does not satisfy the far-field condition is divided into several subarrays, and the beam pattern of the line array can then be determined from the subarray measurements by the spatial convolution that is equivalent to the multiplication of beam pattern. The proposed method was verified through simulation and experimental measurement on a line array with 256 elements of 16 subarrays.
Stabilization of a p-u Sensor Mounted on a Vehicle for Measuring the Acoustic Impedance of Road Surfaces
Authors: Bianco Francesco , Fredianelli Luca , Lo Castro Fabio , Gagliardi Paolo , Fidecaro Francesco , Licitra Gaetano
Published: February 2020

The knowledge of the acoustic impedance of a material allows for the calculation of its acoustic absorption. Impedance can also be linked to structural and physical proprieties of materials. However, while the impedance of pavement samples in laboratory conditions can usually be measured with high accuracy using devices such as the impedance tube, complete in-situ evaluation results are less accurate than the laboratory results and is so time consuming that a full scale implementation of in-situ evaluations is practically impossible. Such a system could provide information on the homogeneity and the correct laying of an installation, which is proven to be directly linked to its acoustic emission properties. The present work studies the development of a measurement instrument which can be fastened through holding elements to a moving laboratory (i.e., a vehicle). This device overcomes the issues that afflict traditional in-situ measurements, such as the impossibility to perform a continuous spatial characterization of a given pavement in order to yield a direct evaluation of the surface’s quality. The instrumentation has been uncoupled from the vehicle’s frame with a system including a Proportional Integral Derivative (PID) controller, studied to maintain the system at a fixed distance from the ground and to reduce damping. The stabilization of this device and the measurement system itself are evaluated and compared to the traditional one.
Camera-Based System for Drafting Detection While Cycling
Authors: Allebosch Gianni , Van den Bossche Simon , Veelaert Peter , Philips Wilfried
Published: February 2020

Drafting involves cycling so close behind another person that wind resistance is significantly reduced, which is illegal during most long distance and several short distance triathlon and duathlon events. In this paper, a proof of concept for a drafting detection system based on computer vision is proposed. After detecting and tracking a bicycle through the various scenes, the distance to this object is estimated through computational geometry. The probability of drafting is then determined through statistical analysis of subsequent measurements over an extended period of time. These algorithms are tested using a static recording and a recording that simulates a race situation with ground truth distances obtained from a Light Detection And Ranging (LiDAR) system. The most accurate developed distance estimation method yields an average error of 0.46 m in our test scenario. When sampling the distances at periods of 1 or 2 s, simulations demonstrate that a drafting violation is detected quickly for cyclists riding at 2 m or more below the limit, while generally avoiding false positives during the race-like test set-up and five hour race simulations.
Multi Criteria Decision Making for the Multi-Satellite Image Acquisition Scheduling Problem
Authors: Vasegaard Alex Elkjær , Picard Mathieu , Hennart Florent , Nielsen Peter , Saha Subrata
Published: February 2020

The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.
Safe Navigation Distance Between Marine Routes and Aquaculture Farms in South Korea Using Gaussian Mixture Model
Authors: Yoo Sang-Lok , Jeong Jong-Chul
Published: February 2020

The purpose of this study was to determine the minimum separation distance between aquaculture farms and ship traffic to prevent damage to either the farms or the vessels. A high-risk area in South Korea was selected for the study by overlapping shipping routes with fisheries using satellite and aerial photographs. The annual frequency of damage was calculated based on a probability distribution applied to the sea area, and a safe distance between the aquaculture farms and the traffic was derived. The Kolmogorov–Smirnov (KS) test was conducted to determine whether the Gaussian mixture model (GMM) follows the data of this study. It was found that a safe distance of at least 1000 m is needed to avoid farm or vessel damage. Then, it is possible to prevent damage to vessel propellers and fisheries locating aquaculture farms at the minimum safe distance from the traffic routes. For protection and security of these structures, the installation of a set of wirelessly Internet of Underwater Things (IoUT) sensors that can transmit the farm location to the ship’s navigator were suggested.
Retrieving Decadal Climate Change from Satellite Radiance Observations—A 100-year CO2 Doubling OSSE Demonstration
Authors: Smith William L. , Weisz Elisabeth , Knuteson Robert , Revercomb Henry , Feldman Daniel
Published: February 2020

Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000–2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique.
Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone
Authors: Shokrekhodaei Maryamsadat , Quinones Stella
Published: February 2020

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.
Application of Structural Equation Modeling (SEM) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Assessment of Safety Culture: An Integrated Modeling Approach
Authors: Çakıt Erman , Karwowski Waldemar , Murata Atsuo , Olak Andrzej Jan
Published: February 2020

The primary purpose of this study was to apply structural equation modeling (SEM) integrated with an adaptive neuro-fuzzy inference system (ANFIS) approach to model the safety culture of the petrochemical industry of Japan. Workers from five companies located in the Chugoku region of Japan completed a paper-based survey distributed by email. SEM and ANFIS methods were integrated in order to identify and model the important factors of the safety culture. The results of SEM indicate that employee attitudes toward safety, coworker’s support, work pressure, and plant safety management systems were significant factors influencing violation behavior, personnel safety motivation, and personnel error behavior. Furthermore, the application of the ANFIS modeling approach showed that employees’ attitude was the most critical predictor of violation behavior and personnel error behavior, while coworkers support was the most critical predictor in modeling personnel safety motivation.
Green Brand of Companies and Greenwashing under Sustainable Development Goals
Authors: Pimonenko Tetyana , Bilan Yuriy , Horák Jakub , Starchenko Liudmyla , Gajda Waldemar
Published: February 2020

Implementing Sustainable Development Goals (SDGs) and increasing environmental issues provokes changes in consumers’ and stakeholders’ behavior. Thus, stakeholders try to invest in green companies and projects; consumers prefer to buy eco-friendly products instead of traditional ones; and consumers and investors refuse to deal with unfair green companies. In this case, the companies should quickly adapt their strategy corresponding to the new trend of transformation from overconsumption to green consumption. This process leads to increasing the frequency of using greenwashing as an unfair marketing instrument to promote the company’s green achievements. Such companies’ behavior leads to a decrease in trust in the company’s green brand from the green investors. Thus, the aim of the study is to check the impact of greenwashing on companies’ green brand. For that purpose, the partial least-squares structural equation modeling (PLS-PM), content analysis and Fishbourne methods were used. The dataset for analysis was obtained from the companies’ websites and financial and non-financial reports. The objects of analysis were Ukrainian large industrial companies, which work not only in the local market but also in the international one. The findings proved that a one point increase in greenwashing leads to a 0.56 point decline in the company’s green brand with a load factor of 0.78. The most significant variable (loading factor 0.34) influencing greenwashing was the information at official websites masking the company’s real economic goals. Thus, a recommendation for companies is to eliminate greenwashing through the publishing of detailed official reports of the companies’ green policy and achievements.
Modelling the Influences of Economic, Demographic, and Institutional Factors on Fiscal Pressure Using OLS, PCSE, and FD-GMM Approaches
Authors: Nuță Alina-Cristina , Nuță Florian-Marcel
Published: February 2020

The purpose of our article is to assess the effect of diverse factors, such as economic, demographic, and institutional factors, on global and social fiscal pressure. The study is based on a panel analysis of 38 states during 2000–2017. We used ordinary least squares (OLS) as a base model for our estimations, and a linear regression with panel-corrected standard errors and a first difference generalized method of moments (GMM) with robust standard errors and orthogonal deviations. The results of our study indicate that the demographic and institutional factors involved in the analysis contribute to the identification of some variables that affect the global or social fiscal pressure.
An Action Research for Improving the Sustainability Assessment Framework Instruments
Authors: Condori-Fernandez Nelly , Lago Patricia , Luaces Miguel R. , Places Ángeles S.
Published: February 2020

In the last years, software engineering researchers have defined sustainability as a quality requirement of software, but not enough effort has been devoted to develop new methods/techniques to support the analysis and assessment of software sustainability. In this study, we present the Sustainability Assessment Framework (SAF) that consists of two instruments: the software sustainability–quality model, and the architectural decision map. Then, we use participatory and technical action research in close collaboration with the software industry to validate the SAF regarding its applicability in specific cases. The unit of analysis of our study is a family of software products (Geographic Information System- and Mobile-based Workforce Management Systems) that aim to address sustainability goals (e.g., efficient collection of dead animals to mitigate social and environmental sustainability risks). The results show that the sustainability–quality model integrated with the architectural decision maps can be used to identify sustainability–quality requirements as design concerns because most of its quality attributes (QAs) have been either addressed in the software project or acknowledged as relevant (i.e., creating awareness on the relevance of the multidimensional sustainability nature of certain QAs). Moreover, the action–research method has been helpful to enrich the sustainability–quality model, by identifying missing QAs (e.g., regulation compliance, data privacy). Finally, the architectural decision maps have been found as useful to guide software architects/designers in their decision-making process.
Capital Investments and Manufacturing Firms’ Performance: Panel-Data Analysis
Authors: Grozdić Vanja , Marić Branislav , Radišić Mladen , Šebestová Jarmila , Lis Marcin
Published: February 2020

The main goal of this study was to examine the effects of capital investments on firm performance, using panel-data analysis. For this purpose, financial data were gathered for 60 manufacturing firms based in Serbia, in the period from 2004 to 2016. The main research hypotheses were developed in accordance with the definition, nature, and time aspect of capital investments. Therefore, empirical expectation of this study was that the relationship between capital investments and firm performance should be positive—they probably bring losses to the firm in the short term, but they should increase firm performance in the long term. Finally, the results have indeed shown that capital investments have statistically significant negative effect on the short-term performance, but positive effect on the long-term performance of the analyzed firms, while controlling for time-fixed effects and certain internal factors.
Why Don’t People Ride Bicycles in High-Income Developing Countries, and Can Bike-Sharing Be the Solution? The Case of Qatar
Authors: Shaaban Khaled
Published: February 2020

Although cycling is increasing in developed regions, such as Europe and North America, high-income developing countries in the Arabian Gulf region still have low cycling activities. Limited research has investigated the barriers to cycling in these countries. In this study, the barriers and motivators in Qatar, a high-income developing country, were investigated. Respondents were asked to report their bicycle usage during the last 12 months. The results indicated that approximately 15% used a bicycle during this period, but only 1.7% bicycled for transportation purposes. The analysis revealed the different barriers to cycling and their relative strengths. The study also compared the perceived challenges of cycling between males and females. The questionnaire results indicated that both groups considered the issues related to the weather conditions, bicycle ownership, lack of paths or connections, and driver behavior as important barriers to cycling. However, the female participants identified clothing, parental consent, and cultural and societal pressure as far more important. When asked about motivators for cycling, the results revealed that improving intersections, adding additional infrastructure facilities, planting trees for shading, affordable bicycles, and campaigns targeting potential cyclists and drivers are needed in order to increase cycling. To better understand how bike-sharing will be perceived if implemented in the future, the feedback was obtained from the participants, and their opinions indicated that there is a great deal of acceptance for this type of service. The outcome of this study can be of benefit to public agencies in developing countries that have the goal of increasing cycling use among their populations.
A Dynamic Model for the Financial Sustainability of the Restoration Sponsorship
Authors: Dolores Luigi , Macchiaroli Maria , De Mare Gianluigi
Published: February 2020

The paper addresses the theme of sponsorship as the main form of public–private partnership through which to finance restoration/recovery interventions for the historical–architectural heritage. The goal is the maximization of sponsorship profitability for companies. Specifically, an existing dynamic model through which it was possible to estimate the optimal annual amount to be invested in sponsorship to maximize the current value of expected profits has been analyzed, reworked and for the first time applied to an Italian company. It was therefore assumed that the company is intent on supporting a multi-year program of sponsorship investment. It is also assumed that the corporation is a single-product company, operating in monopolistic competition and characterized by a Cobb–Douglas production function with decreasing returns to scale. The work is in continuity with a previous publication focused on the application and validation of a static model. The final goal is to provide tools for applied analysis of the financial sustainability of the sponsorship that forms incentive for companies to implement its use, facilitating the recovery of the historical–architectural heritage. Public bodies can thus benefit from the greater contribution of resources from private financiers for a zero-cost and sustainable valorization of cultural heritage.
Can the Introduction of an Environmental Target Assessment Policy Improve the TFP of Textile Enterprises? A Quasi-Natural Experiment Based on the Huai River Basin in China
Authors: Li Yi , Ding Lili , Yang Yongliang
Published: February 2020

Green development is an inevitable requirement to build a modern economic system and fundamental solution to pollution problems. Exploring the relationship between environmental regulation and enterprise total factor productivity (TFP) has great significance for realizing the win-win goal of achieving both environmental protection and economic development. Based on a firm-level dataset from 2000-2007, this paper explores the economic effects of the Environmental Target Assessment Policy of Huai River Basin (ETAP, HRB) in 2004, an environmental regulation that clarifies the responsibility of local governments, by identifying changes in the TFP of the clothing industry (CMI). The empirical findings support that the ETAP can significantly promote improvement in the TFP using the difference in differences (DID) method. Robustness tests, such as the triple differences (DDD) and propensity score matching-difference in differences (PSM-DID), are used to address concerns about the DID approach. Analysis of dynamic effects shows that the ETAP has no impact on enterprise TFP in 2004 but significantly improve the TFP on the next three years (2005-2007). The heterogeneity test results indicate that nonstate-owned enterprises are more sensitive to the ETAP, and the coefficient of the average treatment effect is 0.033. In addition, the ETAP has no noteworthy impact on large- and medium-scale enterprises but results in an average increase of 0.037 in small-scale enterprises’ TFP.
Quantitative Assessment of the Impact of Human Activities on Terrestrial Net Primary Productivity in the Yangtze River Delta
Authors: Huang Qing , Zhang Fangyi , Zhang Qian , Ou Hui , Jin Yunxiang
Published: February 2020

The continuous growth of the economy and population have promoted increasing consumption of natural resources, and raised concerns regarding the upper limits of the terrestrial ecosystems with biomass accessible for humanity. Here, human appropriation of net primary production (HANPP) was employed to assess the influence of human activities on terrestrial net primary production (NPP), and a detailed method was introduced to simulate the magnitude and trends of HANPP in the Yangtze River Delta. The results showed that the total HANPP of the Yangtze River Delta increased from 102.3 Tg C yr−1 to 142.2 Tg C yr−1, during 2005–2015, with an average of 121.3 Tg C yr−1. NPP changes induced by harvest (HANPPharv) made the dominant contribution of 79.9% to the total HANPP, and the increase of HANPPharv in cropland was the main driver of total HANPP growth, which was significantly correlated with the improvement in agricultural production conditions, such as total agricultural machinery power and effective irrigation area. The proportion of HANPP ranged from 59.3% to 72.4% of potential NPP during 2005–2015 in the Yangtze River Delta, and distinguishable differences in the proportions were found among the four provinces in the Yangtze River Delta. Shanghai had the largest proportion of 84.3%, while Zhejiang had the lowest proportion of 32.0%.
Economic Valuation of Earth’s Critical Zone: A Pilot Study of the Zhangxi Catchment, China
Authors: Nie Wan , Guo Hongyan , Yang Lei , Xu Yaoyang , Li Gang , Ruan Xiaohong , Zhu Yongguan , Chen Liding , Banwart Steven A.
Published: February 2020

Earth’s critical zone is the physical layer contained between the top of the vegetation canopy and the depth of the circulating groundwater below the land surface. The critical zone is defined within the study of Earth natural sciences as the unique terrestrial biophysical system that supplies most life-sustaining resources for humans. A feature of this specific physical system that is defined by geographical locale is the interactions of people with the vertically-connected biophysical flows and transformations (energy, material, biodiversity) that contribute to human welfare by delivering, both directly and indirectly, critical zone services to humankind. We have characterized these interactions by considering the full extent of the critical zone through the application of economic valuation methods. We estimated the current economic value of 14 critical zone services for 5 biophysical components of Earth’s critical zone, based on data collected from the Zhangxi catchment of Ningbo city located in the Yangtze River Delta region of China and from several additional published studies. For the full vertical extent of Earth’s critical zone bounded by the Zhangxi catchment, the value, most of which is outside the market, was estimated to be USD 116 million in 2018. Valuation of goods and services was delineated for benefits arising from key components of the critical zone physical system. The estimated value of the atmospheric component of Earth’s critical zone was USD 5 million; the vegetation component value was USD 96 million; the soil component value was USD 8 million; the surface water component value was USD 5 million; and the groundwater component value was USD 2 million. Because of the nature of the uncertainties and lack of data for the full range of identified services, these values are considered a minimum estimate. Gross domestic product in the Zhangxi catchment was around USD 431 million in 2018. These results illustrate, for one location, the range of services that arise when considering the full depth of Earth’s critical zone, the data needs for valuing this range of services, and the conceptual and potential methodological advances, and the challenges, that exist at the disciplinary interface between Earth natural sciences and applied economics.
Determinants of Repayment among Male and Female Microcredit Clients in the USA. An Approach Based on Managers’ Perceptions
Authors: Santandreu Emilio M. , López Pascual Joaquín , Cruz Rambaud Salvador
Published: February 2020

Research on microfinance institutions (MFIs) has normally been focused on developing and emerging markets. However, an analysis of developed countries is also important for foreign MFIs wishing to take advantage of the growth potential of those markets. Therefore, the aim of this article is to determine whether MFIs working in the USA’s market should change or adapt their microcredit policies with respect to women. In effect, there are no studies in the USA supporting the argument that women are a better risk of microcredit than men, or that there are differences in microcredit repayment behavior between women and men. Additionally, it was investigated if the payment behavior of women and men is related to variables such as their age, ethnicity, academic level, marital status, or the characteristics of the microcredits, like purposes, amounts, and payment terms. In the USA, there are not—as in other countries—strong incentives, motivations, or external pressures, other than those that men also have, which influence women to pay their microloans better than men. Then, domestic and international MFIs attracted to enter the USA’s market should review their microcredit policies in relation to women. More research is needed about the microfinance market in the USA.
Which Consumer Associations Can Build a Sustainable Fashion Brand Image? Evidence from Fast Fashion Brands
Authors: Kim Yunjeong , Oh Kyung Wha
Published: February 2020

Since fast fashion is often considered the opposite of sustainable fashion, this study was conducted to clarify the consumer brand associations with sustainable fashion by analyzing three fast fashion brands. Our research included two studies. First, we conducted in-depth interviews with 20 female consumers in Korea who had purchase experience with the sustainable fashion of three selected brands, H&M, Zara, and Uniqlo, to identify sustainable keyword associations. We then structured the keyword data using network analysis. The keyword associations for the three brands resulted in a network of 60 nodes and 629 links with the term “eco-friendly” as the most meaningful keyword. Second, we surveyed 200 women and quantitatively confirmed the association of “eco-friendly fabric” among the keywords suggestive of “eco-friendly” as the most important factor in building a sustainable fashion brand image. In addition, keywords, such as “marketing” and “campaign”, were ranked in the top ten in H&M and Zara, which may imply the opportunistic use of greenwash. This study contributes to the literature by applying in-depth analysis of consumer associations of fast fashion brands from a sustainability perspective through network analysis. We expect our findings to help fashion companies strategically build a sustainable fashion brand image.
Reduction Strategies for Greenhouse Gas Emissions from High-Speed Railway Station Buildings in a Cold Climate Zone of China
Authors: Wang Nan , Satola Daniel , Houlihan Wiberg Aoife , Liu Conghong , Gustavsen Arild
Published: February 2020

Implementing China’s emission reduction regulations requires a design approach that integrates specific architectural and functional properties of railway stations with low greenhouse gas (GHG) emission. This article analyzes life cycle GHG emissions related to materials production, replacement and operational energy use to identify design drivers and reduction strategies implemented in high-speed railway station (HSRS) buildings. A typical middle-sized HSRS building in a cold climate zone in China is studied. A detailed methodology was proposed for the development and assessment of emission reduction strategies through life cycle assessment (LCA), combined with a building information model (BIM). The results reveal that operational emissions contribute the most to total GHG emissions, constituting approximately 81% while embodied material emissions constitute 19%, with 94 kgCO2eq/m2·a and 22 kgCO2eq/m2·a respectively. Optimizing space can reduce operational GHG emissions and service life extension of insulation materials contributes to a 15% reduction in embodied GHG emissions. In all three scenarios, the reduction potentials of space, envelope, and material type optimization were 28.2%, 13.1%, and 3.5% and that measures for reduced life cycle emissions should focus on space in the early stage of building design. This study addresses the research gap by investigating the life cycle GHG emissions from HSRS buildings and reduction strategies to help influence the design decisions of similar projects and large space public buildings which are critical for emission reduction on a larger scale.
Applying a Multi-Criteria Project Portfolio Tool in Selecting Energy Peat Production Areas
Authors: Kurttila Mikko , Haara Arto , Juutinen Artti , Karhu Jouni , Ojanen Paavo , Pykäläinen Jouni , Saarimaa Miia , Tarvainen Oili , Sarkkola Sakari , Tolvanen Anne
Published: February 2020

This study demonstrates the characteristics of the new generic project portfolio selection tool YODA (“Your Own Decision Aid”). YODA does not include a mathematical aggregation model. Instead, the decision maker’s preferences are defined by the interactive articulation of acceptance thresholds of project-level decision criteria. Transparency and ease of adopting the method in participatory planning are sought using the method’s simple preference input. The characteristics of the YODA tool are introduced by presenting how it has been applied in participatory land use planning in northern Finland in selecting a combination of peat production sites to attain the goals defined at municipal level. In this process, each stakeholder first constructed a project portfolio that best met his or her preferences. In doing this, acceptance thresholds for project-level decision criteria were defined. In total, eight decision criteria were related to economic value, biodiversity, social impacts, and ecosystem services. Subsequently, the portfolios of different stakeholders were combined in line with the principles of robust portfolio modelling. Core projects were accepted by all stakeholders, while exterior projects were not accepted, and borderline projects by some of the stakeholders. Although the land use planning situation at hand was highly sensitive, because it was related to various aspects of sustainability, the use of YODA provided useful results. The first meeting with stakeholders identified 52 out of 99 sites that none of the stakeholders would use for energy peat production, due to their characteristics, whereas, in the second meeting, a smaller stakeholder group found 18 core projects and 26 borderline projects which could be potential areas for energy peat production. We conclude that YODA—as a generic project portfolio tool—can be used in various planning situations.
Integrating Sustainability in the Quality Assessment of EHEA Institutions: A Hybrid FDEMATEL-ANP-FIS Model
Authors: Puente Javier , Fernandez Isabel , Gomez Alberto , Priore Paolo
Published: February 2020

This paper proposes the design of a conceptual model of quality assessment in European higher education institutions (HEIs) that takes into account some of the critical reflections made by certain authors in the literature regarding standards and guidelines suggested for this purpose by the European Higher Education Area (EHEA). In addition, the evaluation of the conceptual model was carried out by means of the reliable hybrid methodology MCDM-FIS (multicriteria decision making approach–fuzzy inference system) using FDEMATEL and FDANP methods (fuzzy decision-making trial and evaluation laboratory and FDEMATEL-based analytic network process). The choice of these methodologies was justified by the existing interrelationships among the criteria and dimensions of the model and the degree of subjectivity inherent in its evaluation processes. Finally, it is suggested to include sustainability as a determining factor in the university context due to its great relevance in the training of future professionals.
Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators
Authors: Zhou Ziqiang , Tang Fei , Liu Dichen , Wang Chenxu , Gao Xin
Published: February 2020

Over the past decades, the deployment of distributed generations (DGs) in distribution systems has grown dramatically due to the concerns of environment and carbon emission. However, a large number of DGs have introduced more uncertainties and challenges into the operation of distribution networks. Due to the stochastic nature of renewable energy resources, probabilistic tools are needed to assist systems operators in analyzing operating states of systems. To address this issue, we develop a probabilistic framework for the assessment of systems. In the proposed framework, the uncertainties of DGs outputs are modeled using short term forecast values and errors. Moreover, an adaptive cluster-based cumulant method is developed for probabilistic load flow calculation. The performance of the proposed framework is evaluated in the IEEE 33-bus system and PG&E 69-bus system. The results indicate that the proposed framework could yield accurate results with a reasonable computational burden. The excellent performance of the proposed framework in estimating technological violations can help system operators underlying the potential risks of systems.
Global Sensitivity Analysis of the Standardized Precipitation Evapotranspiration Index at Different Time Scales in Jilin Province, China
Authors: Zhang Rui , Chen Taotao , Chi Daocai
Published: February 2020

The Standardized Precipitation Evapotranspiration Index (SPEI) has been widely applied, due to its multi-scalar features and the ability to identify different drought types. However, its sensitivity to climatic variables still remains unclear, especially at different time scales. Therefore, this study investigates the sensitivity of SPEI to average temperature (Tmean), diurnal temperature ranges (Tdelta), relative humidity (RH), solar radiation (Rs), wind speed (U2), geothermal flux (G), and precipitation (P) from 1957 to 2017 using the extended Fourier Amplitude Sensitivity Test at different time scales in Jilin Province, China. Jilin Province experienced a significant rise in Tmean, and a sharp decrease in Tdelta, Rs, and U2. P is undoubtedly the most influential factor to the SPEI among the meteorological variables, which explained 59.9%–97.9% of the total variability, especially during the main crop growing season (from May to September). While Tmean, RH, or U2 observably affect the SPEI and cannot be neglected during the nongrowing season. In terms of spatial distribution, the SPEI was mainly affected by P in the eastern region, while it was also influenced by Tmean, RH, and U2 as well in the western region. The sensitivity of the SPEI differs in time scales: P > Tmean > RH > U2 > Rs > G > Tdelta (1 to 6 month), P > U2 > RH ≈ Tmean > G > Rs > Tdelta (7 to 18 month), and P > U2 > G > Tmean > RH > Rs > Tdelta (more than 24 month time scale), respectively. The results have the potential to provide a reference for agricultural production and management in Jilin Province, China.
An Empirical Evidence Study of Consumer Perception and Socioeconomic Profiles for Digital Stores in Vietnam
Authors: Singh Sonia , Mondal Subhra , Singh Lata Bajpai , Sahoo Kalyan Kumar , Das Subhankar
Published: February 2020

Digital stores are spreading their tentacles slowly in Vietnam, the most upcoming economy of the ASEAN region, so it is a relevant and contemporary study to know the perceptual thought and socioeconomic profiles of customers. Since there is a lack of independent study in this area, the relationships can be researched and presented. The context of digitization and consumerism is slowly growing in the Vietnamese market. So, this article studies the factors influencing online consumer perception and purchase orientation for digital stores operating in Vietnam through apps and websites. This study is a bi-phase study. In the first phase, constructs, such as digital store images, digital customer loyalty, satisfaction and digital socio-economic profiles of consumers, are examined. In the second part, the empirical analysis is carried by the authors. The study is done by SPSS 22, R studio, and R-cran software packages. Exploratory factor analysis, confirmatory factor analysis, very simple structure (VSS) criterion, and parallel analysis are used for the study. Customers have an almost balanced view of shifting to other stores and the image of the digital store, which makes the price have a substantial effect on the digital store image. The education and empathetic nature of employees also affect the way customers complain, and the way delivery is handled, which is Factor 1 for the model. Factor 2 is promotion, pricing, billing ease, or proper billing, which influence the purchasing power of the customer. So, the service provider or digital store has to be cautious of a specific socioeconomic variable. Digital store image is significantly about complaint handling methods, promotional aspects, and image-driven pricing. So the digital store has to be more careful about these factors. So, from the model, it can be concluded that digital consumers’ socioeconomic profiles, the price of the product, and the digital store’s image appear to have a significant relationship. A consumer pattern is having the education and the period of purchase, which explains the relationship better. The level of education and the period of purchase from a digital store also have significant differences among each other. Thus, the digital service provider should consider the level of education and the period of continuous purchase from a digital store as the criteria for evaluating digital customer loyalty. The price of the product seems to be significant as a variable too. The digital store image appears to be substantial for grievances, promotion, and image driven by price. So this considerable relationship has to be taken into consideration by the service provider while focusing on making decisions for customer loyalty.