bluuebunny/crossref_metadata_2025 huggingface dataset:
A Defect Study and Classification of Brown Diamonds with Non-Deformation-Related Color
Authors: Hainschwang Thomas , Notari Franck , Pamies Gianna
Published: October 2020

While the first part of this study took a detailed look at the properties, defects and classification of brown diamonds with deformation-related (DR) brown color and compared them to pink to purple to red diamonds, this second part covers diamonds with non-deformation-related (referred to as NDR in this study) brown color, including diamonds with treatment-induced brown color and synthetic brown diamonds. It was found that the natural NDR brown diamonds include CO2 and Pseudo CO2 diamonds as well as certain hydrogen-rich diamonds. Based on these, the new classification of NDR brown diamonds has been elaborated, resulting in 5 different classes. The detailed defect study performed has shown and confirmed the complexity of the CO2 and Pseudo CO2 diamonds; the probable link between structurally bound oxygen and some of the spectroscopic features such as the 480 nm absorption band is apparent in these diamonds. One of the most interesting findings was made through the low temperature NIR spectroscopy of some usually hydrogen-rich diamonds, which has defined a defect of great interest, the 1330 nm center; we suggest that this defect, together with the many lines in the 970 to 1000 nm range—referred to as the 990 nm series in this study—are responsible for the complex UV-Vis-NIR spectra seen of these diamonds. The results indicate that both features are nickel-nitrogen-related defects, the 1330 nm defect without involvement of hydrogen and the 990 nm series likely with hydrogen involved. Another surprising result was that during various treatment experiments performed we created dark orangish brown color in originally pale yellow “cape” diamonds by HPHT treatment at 2500 °C. It is suggested that the creation of this brown hue is related to the destruction or transformation of the N3 center at such extreme conditions.
Prevention of Neural Tube Defects by Folic Acid Supplementation: A National Population-Based Study
Authors: de la Fournière Benoît , Dhombres Ferdinand , Maurice Paul , de Foucaud Sabine , Lallemant Pauline , Zérah Michel , Guilbaud Lucie , Jouannic Jean-Marie
Published: October 2020

Folic acid supplementation is recommended for neural tube defect prevention during pregnancy. We conducted an observational, retrospective national registry study to determine the rate of dispensing of periconceptional folic acid after prescription in a sample of French women representative of the general population. Our study population (n = 186,061) was a representative sample of the French population, recorded in the Health Data System database on pharmacy dispensing of medication and mandatory reporting of pregnancy. Between 2006 and 2016, 14.3% of pregnant women had a prescription for folic acid supplementation during the month preceding conception and for the first 12 weeks of pregnancy. Of these prescriptions, 30.9% were issued before the start of pregnancy. This percentage was lower for first pregnancies. The rate of pharmacy dispensing during the preconception period increased progressively from 3.8% to 8.3% between 2006 and 2016. In France, the rate of pharmacy dispensing of periconceptional folic acid after medical prescription is very low and does not follow international recommendations. It seems essential to implement awareness-raising policies targeting the general population and physicians regarding effective periconceptional supplementation, particularly starting in the preconception period. Clarification of international recommendations and fortification of flour could improve the efficacy of folate supplementation at population level.
Medicinal Plants to Strengthen Immunity during a Pandemic
Authors: Babich Olga , Sukhikh Stanislav , Prosekov Alexander , Asyakina Lyudmila , Ivanova Svetlana
Published: October 2020

The development of new effective anti-coronavirus drugs and therapies is important, but it requires significant human, financial and, most importantly, time expenditures. The current pandemic is neither the first nor the last. Humanity has already accumulated considerable survival experience. We cannot do without prevention and epidemiological protection measures. This study reviews medicinal plants that grow in Northeast Asia and whose antioxidant, antiviral, anti-inflammatory and immunomodulatory characteristics are already known, also in the framework of the prevention and treatment of pneumonia of various etiologies. The need for a comprehensive approach to maintaining immunodefences, including functional foods and positive emotions, is emphasized. In the period of pandemics, it is important to research various areas that allow to us accumulate a critical mass of information and cope with the next global disease.
Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach
Authors: Zizi Youssef , Oudgou Mohamed , El Moudden Abdeslam
Published: October 2020

This paper aims to identify the determinants and predictors of Small and Medium-sized Enterprises (SMEs)’ financial failure. Within this framework, we have opted for a quantitative method based on a sample of healthy and failing SMEs of a Moroccan bank. The main results of the different optimal models are obtained by the stepwise method of estimating logistic regression. These results show, in a normal economic context, that the variables that discriminate between healthy and failing SMEs are the main predictors of financial failure. Autonomy ratio, interest to sales, asset turnover, days in accounts receivable, and duration of trade payables are the variables that increase the probability of financial failure, while repayment capacity and return on assets reduce the probability of failure. These variables present an overall classification rate of healthy and failing SMEs of 91.11% three years before failure and of 84.44% two years and one year before failure.
Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images
Authors: Hu Jianming , Zhi Xiyang , Zhang Wei , Ren Longfei , Bruzzone Lorenzo
Published: October 2020

Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background prior. Secondly, we compute the prior probability of salient foreground regions and propose a weighting function to constrain false foreground clutter, gaining the foreground-based prediction map. Thirdly, we integrate the two prediction maps and improve the details of the integrated map by a guided filter function and a wake adjustment function, obtaining the fine selection of candidate regions. Afterwards, a classification is further performed to reduce false alarms and produce the final ship detection results. Qualitative and quantitative evaluations on two public available datasets demonstrate the robustness and efficiency of the proposed method against four advanced baseline methods.
Statistical Analysis of 1996–2017 Ozone Profile Data Obtained by Ground-Based Microwave Radiometry
Authors: Gaikovich Konstantin P. , Kropotkina Elena P. , Rozanov Sergey B.
Published: October 2020

Trends in the ozone layer remain among the major problems of the atmosphere physics; thus, results of measurements of the ozone altitude distribution (profile), carried out in the same place and via the same method, are very important. This paper presents the results of the statistical analysis of ensembles of ozone profiles obtained from ground-based microwave radiometry data acquired at the P.N. Lebedev Physical Institute over a period of two decades (1996–2017). The data collected show the significant difference between monthly mean statistical parameters of ozone profiles of the decades 1996–2006 and 2007–2017. The main and unexpected result is the drastic decrease in monthly root-mean-square (rms) variances of ozone profiles over Moscow above 30 km in cold months of the decade 2007–2017 (if compared to the variances in the decade 1996–2006) with the maximum fall by 46% at 39 km in February monthly mean variances. The decade change of variances obtained by averaging over all nine months in the analysis (from September to May) has the same decrease with maximum fall by 25% at 38 km. Additionally, significant decade changes were revealed in other monthly mean statistical parameters: probability density of ozone profile variances, inter-altitude covariance and correlation functions, and time covariance and correlation—as well as their frequency spectra. The decade change of the ozone profile obtained by averaging over the nine months appeared much less significant: the decrease by 5.7% at the altitude of 19 km (with 1.5% sampling error), minor decrease by 2.6% (with sampling error 1.5%) in the profile maximum at 37 km, and increases of 1.7% at 28 km and 2.5% at 47 km (with sampling errors 1.7%)—lower and higher of this maximum. In addition to that, the corresponding averaged mean total column (integral) ozone content above 20 km remained practically unchanged: 4.61 g/m2 for decade 1996–2006 as compared to 4.58 g/m2 for 2007–2017. Possible explanations of revealed offsets are proposed and discussed.
Monitoring Surface Displacement of a Deep-Seated Landslide by a Low-Cost and near Real-Time GNSS System
Authors: Šegina Ela , Peternel Tina , Urbančič Tilen , Realini Eugenio , Zupan Matija , Jež Jernej , Caldera Stefano , Gatti Andrea , Tagliaferro Giulio , Consoli Angelo , González Joaquín Reyes , Auflič Mateja Jemec
Published: October 2020

A prototype of a low-cost GNSS (Global Navigation Satellite System) monitoring system was installed on a deep-seated landslide in north-western Slovenia to test its performance under field conditions. The system consists of newly developed GNSS stations based on low-cost, dual-frequency receivers and open-source GNSS processing software. It automatically receives GNSS data and transmits them over the Internet. The system processes the data server-side and makes them available to the end user via a web portal. The detected surface displacements were evaluated through a comparison with the network of classic geodetic measurements. The results of a nine-month monitoring period using seven GNSS stations provided a detailed insight into the spatial and temporal pattern of deep-seated landslide surface movements. The displacement data were correlated with precipitation measurements at the site to reveal how different parts of the landslide react to rainfall. These data form the basis for the further development of an early-warning system which will help to manage the risk the landslide poses to the local population and infrastructure.
Using LANDSAT 8 and VENµS Data to Study the Effect of Geodiversity on Soil Moisture Dynamics in a Semiarid Shrubland
Authors: Dubinin Vladislav , Svoray Tal , Stavi Ilan , Yizhaq Hezi
Published: October 2020

Soil moisture content (SMC) is a limiting factor to ecosystem productivity in semiarid shrublands. Long-term droughts due to climatic changes may increase the water stress imposed on these lands. Recent observations demonstrate positive relations between geodiversity—expressed by the degree of soil stoniness—and SMC in the upper soil layers. This suggests that areas of high geodiversity can potentially provide a haven for plant survival under water scarcity conditions. The objective of this study was to assess the effect of geodiversity on the dynamics of SMC in semiarid environments, which so far has not been fully investigated. The optical trapezoid model (OPTRAM) applied to six-year time series data (November 2013–July 2018), obtained from LANDSAT 8 and highly correlated with field measurements (R2 = 0.96), shows here that the SMC in hillslopes with high geodiversity is consistently greater than that in hillslopes with low geodiversity. During winter periods (December–March), the difference between the two hillslope types was ~7%, while during summer periods (June–September) it reduced to ~4%. By using the high-resolution spectral-spatiotemporal VENµS data, we further studied the geodiversity mechanism during summertime, and at a smaller spatial scale. The VENµS-based Crop Water Content Index (CWCI) was compared with the OPTRAM measurements (R2 = 0.71). The Augmented Dickey–Fuller test showed that water loss in the high-geodiversity areas during summers was very small (p-value > 0.1). Furthermore, the biocrust index based on the VENµS data showed that biological crust activity in the high-geodiversity hillslopes during summers is high and almost stationary (ADF p-value > 0.1). We suggest that the mechanism responsible for the high SMC in the high-geodiversity areas may be related to lower evaporation rates in the dry season and high runoff rates in the wet season, both of which are the combined result of the greater presence of developed biocrusts and stoniness in the areas of higher geodiversity.
A New Remote Sensing Method to Estimate River to Ocean DOC Flux in Peatland Dominated Sarawak Coastal Regions, Borneo
Authors: ChunHock Sim , Cherukuru Nagur , Mujahid Aazani , Martin Patrick , Sanwlani Nivedita , Warneke Thorsten , Rixen Tim , Notholt Justus , Müller Moritz
Published: October 2020

We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three steps. In the first step, we developed an algorithm for estimating DOC concentrations using the ratio of Landsat-8 Red to Green bands B4/B3 (DOC (μM C) = 89.86 ·e0.27·(B4/B3)), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations.
Reliable and Efficient UAV Image Matching via Geometric Constraints Structured by Delaunay Triangulation
Authors: Jiang San , Jiang Wanshou , Li Lelin , Wang Lizhe , Huang Wei
Published: October 2020

Outlier removal is a crucial step in local feature-based unmanned aerial vehicle (UAV) image matching. Inspired by our previous work, this paper proposes a method for reliable and efficient outlier removal in UAV image matching. The inputs of the method are only two images without any other auxiliary data. The core idea is to design local geometric constraints within the neighboring structure via the Delaunay triangulation and use a two-stage method for outlier removal and match refinement. In the filter stage, initial matches are first organized as the Delaunay triangulation (DT) and its corresponding graph, and their dissimilarity scores are computed from the affine-invariant spatial angular order (SAO), which is used to achieve hierarchical outlier removal. In addition, by using the triangle constraint between the refined Delaunay triangulation and its corresponding graph, missed inliers are resumed from match expansion. In the verification stage, retained matches are refined using the RANSAC-based global geometric constraint. Therefore, the two-stage algorithm is termed DTSAO-RANSAC. Finally, using four datasets, DTSAO-RANSAC is comprehensively analyzed and compared with other methods in feature matching and image orientation tests. The experimental results demonstrate that compared with the LO-RANSAC algorithm, DTSAO-RANSAC can achieve efficient outlier removal with speedup ratios ranging from 4 to 16 and, it can provide reliable matching results for image orientation of UAV datasets.
The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images
Authors: Chen Zhou , Fei Xianyun , Gao Xiangwei , Wang Xiaoxue , Zhao Huimin , Wong Kapo , Tsou Jin Yeu , Zhang Yuanzhi
Published: October 2020

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.
Mitigation of Arctic Tundra Surface Warming by Plant Evapotranspiration: Complete Energy Balance Component Estimation Using LANDSAT Satellite Data
Authors: Nedbal Václav , Láska Kamil , Brom Jakub
Published: October 2020

Global climate change is expected to cause a strong temperature increase in the polar regions, accompanied by a reduction in snow cover. Due to a lower albedo, bare ground absorbs more solar energy and its temperature can increase more. Here, we show that vegetation growth in such bare ground areas can efficiently mitigate surface warming in the Arctic, thanks to plant evapotranspiration. In order to establish a comprehensive energy balance for the Arctic land surface, we used an ensemble of methods of ground-based measurements and multispectral satellite image analysis. Our estimate is that the low vegetation of polar tundra transforms 26% more solar energy into evapotranspiration than bare ground in clear sky weather. Due to its isolation properties, vegetation further reduces ground heat flux under the surface by ~4%, compared to bare areas, thus lowering the increase in subsurface temperature. As a result, ~22% less solar energy can be transformed into sensible heat flux at vegetated surfaces as opposed to bare ground, bringing about a decrease in surface temperature of ~7.8 °C.
Assessment of Forest Biomass Estimation from Dry and Wet SAR Acquisitions Collected during the 2019 UAVSAR AM-PM Campaign in Southeastern United States
Authors: Khati Unmesh , Lavalle Marco , Shiroma Gustavo H. X. , Meyer Victoria , Chapman Bruce
Published: October 2020

Forest above-ground biomass (AGB) estimation from SAR backscatter is affected by varying imaging and environmental conditions. This paper quantifies and compares the performance of forest biomass estimation from L-band SAR backscatter measured selectively under dry and wet conditions during the 2019 AM-PM NASA airborne campaign. Seven Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images acquired between June and October 2019 over a temperate deciduous forest in Southeastern United States with varying moisture and precipitation conditions are examined in conjunction with LIDAR and field measurements. Biomass is estimated by fitting a 3-parameter modified Water Cloud Model (WCM) to radiometric terrain corrected SAR backscatter. Our experiment is designed to quantify the biomass estimation errors when biomass models are calibrated and validated on varying acquisition conditions (dry or wet). Multi-temporal estimation strategies are also evaluated and compared with single-acquisition estimation approaches. As an outcome, the experiment shows that the WCM model calibrated and validated on single acquisitions adapts to different soil moisture conditions with RMSD up to 18.7 Mg/ha. The AGB estimation performance, however, decreases with RMSD upwards of 30 Mg/ha when the model is cross-validated on moisture and precipitation conditions different than the calibration conditions. Results confirm that calibrating the model over the multi-temporal data using averaged backscatter or weighted combinations of individual AGB estimates, improves the biomass estimation accuracy up to about 20% at L-band. This study helps design biomass cal/val procedures and biomass estimation algorithms for dense time-series to be collected by low-frequency radar missions such as NASA-ISRO SAR (NISAR) and BIOMASS.
Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement
Authors: Nguyen Thanh-Binh , Nguyen Trung-Hau , Chung Wan-Young
Published: October 2020

In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO2) concentration in a food package from headspace pressure measurement. While being stored, food quality degrades gradually as a function of time and storage conditions. A food monitoring system is, therefore, essential to prevent the detrimental problems of food waste and eating spoilt food. Since conventional works that invasively measure food pH level and CO2 concentration in food packages have shown several disadvantages in terms of power consumption, system size, cost, and reliability, our study proposes a system utilizing package headspace pressure to accurately and noninvasively extract food pH level and CO2 concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm × 2.5 cm smart sensor tag was designed and integrated with near-field communication (NFC)-based energy harvesting technology for battery-free operation. To validate the reliability of the proposed extraction method, various experiments were conducted with different foods, such as pork, chicken, and fish, in two storage environments. The experimental results show that the designed system can operate in a fully passive mode to communicate with an NFC-enabled smartphone. High correlation coefficients of the headspace pressure with the food pH level and the headspace CO2 concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO2 concentration with high accuracy. A linear regression model was then trained to linearly fit the sensor data. To display the estimated results, we also developed an Android mobile application with an easy-to-use interface.
Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method
Authors: Faqeerzada Mohammad Akbar , Lohumi Santosh , Kim Geonwoo , Joshi Rahul , Lee Hoonsoo , Kim Moon Sung , Cho Byoung-Kwan
Published: October 2020

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89–100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.
Global Navigation Process Simulation Based on Different Types of Gravity Data
Authors: Lytkin Ivan , Tarasov Oleg
Published: October 2020

A theoretical study on the feasibility of global navigation based on three different types of gravity data was performed. A computer simulation of gravity-aided navigation was performed for three models of sections of the Earth’s surface with gravity anomalies distributed as specified. For navigation, three types of data sources were used, e.g., the gravity vector magnitude, three orthogonal projections of the gravity vector, and five independent components of the full gravity tensor. For each data source, when searching a specified route, the dependencies of the number of the identified true and false points were determined in accordance with the measurement error specified. The problem of determining the true route on the set of the identified points is briefly reviewed. General conclusions are presented regarding the practical applicability of the reviewed data sources to the problem of global navigation.
A Complementary Filter Design on SE(3) to Identify Micro-Motions during 3D Motion Tracking
Authors: Phan Gia-Hoang , Hansen Clint , Tommasino Paolo , Hussain Asif , Formica Domenico , Campolo Domenico
Published: October 2020

In 3D motion capture, multiple methods have been developed in order to optimize the quality of the captured data. While certain technologies, such as inertial measurement units (IMU), are mostly suitable for 3D orientation estimation at relatively high frequencies, other technologies, such as marker-based motion capture, are more suitable for 3D position estimations at a lower frequency range. In this work, we introduce a complementary filter that complements 3D motion capture data with high-frequency acceleration signals from an IMU. While the local optimization reduces the error of the motion tracking, the additional accelerations can help to detect micro-motions that are useful when dealing with high-frequency human motions or robotic applications. The combination of high-frequency accelerometers improves the accuracy of the data and helps to overcome limitations in motion capture when micro-motions are not traceable with 3D motion tracking system. In our experimental evaluation, we demonstrate the improvements of the motion capture results during translational, rotational, and combined movements.
Enantiomeric Separation of Colchicine and Lacosamide by Nano-LC. Quantitative Analysis in Pharmaceutical Formulations
Authors: Casado Natalia , Jiang Zhengjin , García María Ángeles , Marina María Luisa
Published: October 2020

A chiral analytical methodology was developed by nano-liquid chromatography (nano-LC) enabling the enantiomeric separation of two chiral drugs, lacosamide (novel antiepileptic drug) and colchicine (antiuremic drug), commercialized as pure enantiomers. A capillary column lab-packed with an amylose tris(3,5-dimethylphenylcarbamate) chiral stationary phase was used in a lab-assembled nano-LC system. Lacosamide and colchicine enantiomers were separated in less than 8.0 and 9.0 min, respectively, with resolution values of 1.6 and 2.3, using 20 nL of sample and 1.8 µL of mobile phase per analysis. The analytical characteristics of the proposed methodology were evaluated according to the International Council for Harmonisation (ICH) guidelines, showing good analytical performance with good recoveries (97–98% and 100–103%) and precision values (relative standard deviation (RSD) <10.5 and <3.0%) for lacosamide and colchicine enantiomers, respectively. LODs were 1.7 and 2.0 µg/mL for (S)- and (R)-lacosamide, respectively, and 1.0 µg/mL for both colchicine enantiomers. Additionally, the developed methodology enabled to detect a 0.1% of the enantiomeric impurities, fulfilling the ICH regulation requirements. The method was applied to the determination of lacosamide and colchicine enantiomers in different pharmaceutical formulations to ensure their quality control. The content of the enantiomeric impurities was below a 0.1% and the amount of (R)-lacosamide and (S)-colchicine agreed with their labeled contents.
Comfort Evaluation of an Aircraft Cabin System Employing a Hybrid Model
Authors: Liu Jing , Yu Suihuai , Chu Jianjie
Published: October 2020

Comfort is becoming one of the most important principles in the process of design and evaluation of civil aircraft cabins. However, the comprehensive quantitative evaluation of comfort in an aircraft cabin is a complicated issue because of the subjectivity of comfort perception and a large number of factors involved in the whole complex cabin system. A hybrid model combined with the Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy comprehensive evaluation is proposed, which considers both the interrelation between the criteria and the fuzziness of subjective comfort perception concurrently. The result of the empirical study from the questionnaire survey in flight was consistent with that of the hybrid model. The proposed model is effective. It could provide a more reasonable priority to improve comfort in the aircraft cabin. According to the measured results of the cabin environment, the cabin facilities and layout, seats and service, the specific differences between the criteria can be displayed clearly, which is helpful to improve the cabin comfort level.
Enterprise Architecture as Explanatory Information Systems Theory for Understanding Small- and Medium-Sized Enterprise Growth
Authors: Gerber Aurona , le Roux Pierre , van der Merwe Alta
Published: October 2020

Understanding and explaining small- and medium-sized enterprise (SME) growth is important for sustainability from multiple perspectives. Research indicates that SMEs comprise more than 80% of most economies, and their cumulative impact on sustainability considerations is far from trivial. In addition, for sustainability concerns to be prioritized, an SME has to be successful over time. In most developing countries, SMEs play a major role in solving socio-economic challenges. SMEs are an active research topic within the information systems (IS) discipline, often within the enterprise architecture (EA) domain. EA fundamentally adopts a systems perspective to describe the essential elements of a socio-technical organization and their relationships to each other and to the environment in order to understand complexity and manage change. However, despite rapid adoption originally, EA research and practice often fails to deliver on expectations. In some circles, EA became synonymous with projects that are over-budget, over-time and costly without the expected return on investment. In this paper, we argue that EA remains indispensable for understanding and explaining enterprises and that we fundamentally need to revisit some of the applications of EA. We, therefore, executed a research study in two parts. In the first part, we applied IS theory perspectives and adopted the taxonomy and structural components of theory to argue that EA, as represented by the Zachman Framework for Enterprise Architecture (ZFEA), could be adopted as an explanatory IS theory. In the second part of the study, we subsequently analysed multiple case studies from this theoretical basis to investigate whether distinguishable focus patterns could be detected during SME growth. The final results provide evidence that EA, represented through an appropriate framework like the ZFEA, could serve as an explanatory theory for SMEs during start-up, growth and transformation. We identified focus patterns and from these results, it should be possible to understand and explain how SMEs grow. Positioning the ZFEA as explanatory IS theory provides insight into the role and purpose of the ZFEA (and by extension EA), and could assist researchers and practitioners with mediating the challenges experienced by SMEs, and, by extension, enhance sustainable development.
Gender-Typed Sport Practice, Physical Self-Perceptions, and Performance-Related Emotions in Adolescent Girls
Authors: Morano Milena , Robazza Claudio , Ruiz Montse C. , Cataldi Stefania , Fischetti Francesco , Bortoli Laura
Published: October 2020

Youth sport experience provides opportunities for physical, personal, and social development in youngsters. Sport is a social system in which socially constructed gender differences and stereotypes are incorporated, and specific sport activities are often perceived as gender characterized. The objective of this study was to examine the relationship between some salient physical and emotional self-perceptions and the type of sport practiced. A sample of 261 female athletes, aged 14–21 years (Mage = 15.59, SD = 2.00), practicing different sports, categorized as feminine (e.g., artistic and rhythmic gymnastics), masculine (e.g., soccer and rugby), or neutral (e.g., track and field and tennis), took part in a cross-sectional study. Significant differences were observed between aesthetic sports and other types of sports. Athletes involved in aesthetic sports reported the lowest values in their feelings of confidence and the highest values in feelings of worry related to competition. This may be attributed to the evaluation system of aesthetic sports, in which the athlete’s performance is evaluated by a jury. At the same time, they reported low values of dysfunctional psychobiosocial states associated with their general sport experience, likely because of their physical appearance close to the current body social standards for girls. Notwithstanding the differences by type of sport, athletes of all disciplines reported high mean values of functional psychobiosocial states, suggesting that their overall sporting experience was good.
The COVID-19 Pandemic as an Opportunity to Foster the Sustainable Development of Teaching in Higher Education
Authors: Sá Maria José , Serpa Sandro
Published: October 2020

The COVID-19 pandemic has had profound consequences on the social, economic and cultural life at the global level. The educational dimension has also been affected in the schools’ regular functioning, with the temporary closure of educational institutions, as well as the impediment of face-to-face classes. This perspective paper aims to add to the knowledge already produced on this topic, by arguing that these challenging conditions can be a pivotal moment of opportunity for reshaping higher education, with the implementation, development and diffusion, among academics and students, of digital technologies. The paper also discusses the role of leadership in the transformation of organizational culture in higher education. The methodology used to carry out this study is qualitative, and the technique employed to analyze the data collected was content analysis. Research studies, in diverse formats, already published on the COVID-19 topic and its impacts were the elected data sources. The results of this document analysis allow us to conclude that there is the need to improve the digital sustainable development in teaching in higher education, which entails profound challenges that higher education institutions need to face and overcome if they want to be at the forefront of success in the international education market. This is where the authors seek to contribute, by offering insights on the challenges—but also the opportunities—that COVID-19 poses to higher education at a time when it needs to redefine its teaching methods, leadership models, and interaction channels, by going digital towards the improvement of the sustainable development of its teaching.
Social Media Data Analytics to Enhance Sustainable Communications between Public Users and Providers in Weather Forecast Service Industry
Authors: Lee Ki-Kwang , Kim In-Gyum
Published: October 2020

The weather forecast service industry needs to understand customers’ opinions of the weather forecast to enhance sustainable communication between forecast providers and recipients particularly influenced by inherent uncertainty in the forecast itself and cultural factors. This study aims to investigate the potential for using social media data to analyze users’ opinions of the wrong weather forecast. Twitter data from Korea in 2014 are analyzed using textual analysis and association rule mining to extract meaningful emotions or behaviors from weather forecast users. The results of textual analysis show that the frequency of negative opinions is considerably high compared to positive opinions. More than half of the tweets mention precipitation forecasts among the meteorological phenomena, implying that most Koreans are sensitive to rain events. Moreover, association rules extracted from the negative tweets reveal a pattern of user criticism according to the seasons and types of forecast errors such as a “false alarm” or “miss” error. This study shows that social media data can provide valuable information on the actual opinion of the forecast users in almost real time, enabling the weather forecast providers to communicate effectively with the public.
A Comparative Review on Greenery Ecosystems and Their Impacts on Sustainability of Building Environment
Authors: Al-Kayiem Hussain H. , Koh Kelly , Riyadi Tri W. B. , Effendy Marwan
Published: October 2020

Greenery systems are sustainable ecosystems for buildings. Many studies on greenery systems, such as green roofs and green walls, have demonstrated that greenery systems support energy saving and improve thermal conditions in the building sector. This paper summarizes, discusses, and compares greenery systems and their contributions to the reduction of the urban heat index, the reduction of internal and external buildings’ wall temperatures, and the reduction of the energy consumption of buildings. The fundamental mechanisms of greenery systems, which are thermal insulation, evapotranspiration, and shading effect, are also discussed. The benefits of greenery systems include the improvement of stormwater management, the improvement of air quality, the reduction of sound pollution, the reduction of carbon dioxide, and the improvement of aesthetic building value. The summarized materials on the greenery systems in the article will be a point of references for the researchers, planners, and developers of urban and rural areas, as well as the individual’s interest for future urban and rural plans.
Coalitions Towards the Carbon Tax in the Swedish Heating Sector
Authors: Higa Cecilia , Cunha Marcelo , Silveira Semida
Published: October 2020

The carbon tax is a cornerstone of the climate policy in Sweden. Historically, it has played a central role in the replacement of fossil fuels in the heating sector. The purpose of this paper was to analyze the policy-making process that led to the formulation and adoption of the carbon tax in the Swedish heating sector, from 1980 to 1991. We used the Advocacy Coalition Framework (ACF), according to which policy actors form coalitions based on shared beliefs. Drawing from both literature and empirical experiences gathered through in-depth interviews, we identified supporting and opposing coalitions that diverged in their views concerning bioenergy development and the potential impact of the carbon tax. This study illustrates the complex conditions for introduction of environmental taxes and expansion of new industries, particularly when established businesses perceive the new activities as a threat. The Swedish experience shows that countries can progress by exploring internal synergies and innovative policy designs, despite potential resistance of established constituencies. The successful adoption of the carbon tax in the Swedish heating sector serves as inspiration for policymakers in other sectors and countries, as they contemplate policies to promote the decarbonization of the energy system.
Impact of Sustainability Reporting and Inadequate Management of ESG Factors on Corporate Performance and Sustainable Growth
Authors: Oprean-Stan Camelia , Oncioiu Ionica , Iuga Iulia Cristina , Stan Sebastian
Published: October 2020

The purpose of this research study is to examine and explain whether there is a positive or negative linear relationship between sustainability reporting, inadequate management of economic, social, and governance (ESG) factors, and corporate performance and sustainable growth. The financial and market performances of companies are both analyzed in this study. Sustainable growth at the company level is introduced as a dimension that depends on sustainability reporting and the management of ESG factors. In order to achieve the main objective of the paper, the methodology here focuses on the construction of multifactorial linear regressions, in which the dependent variables are measurements of financial and market performance and assess corporate sustainable growth. The independent variables of these regressions are the sustainability metrics and the control variables included in the models. Most of the existing literature focuses on the causality between sustainability performance and financial performance. While most impact studies on financial performance are restricted to sustainability performance, this study refers to the degree of risk associated with the inadequate management of economic, social, and governance factors. This work examines the effects of ESG risk management, not only on performance, but also on corporate sustainable growth. It is one of the few studies that addresses the problem of the involvement of companies in controversial events and the way in which such events impact the sustainability and sustainable growth of the company.
Assessing Social and Spatial Equity of Neighborhood Retail and Service Access in Seoul, South Korea
Authors: Kim Donghyun , Park Jina
Published: October 2020

Creating a sustainable urban space should allow everyone to benefit from urbanization regardless of their ability. Spatial equity is one of the significant factors of sustainability. Several studies have explored pedestrian accessibility and spatial equity, but few researchers have addressed daily retail activities. This study aimed to examine the equity of pedestrian accessibility to neighborhood retail and service (NRS) establishments in Seoul, Korea. Accessibility of NRSs was measured by pedestrian direction API and spatially clustered by local indicators of spatial association (LISA). Equity was examined using the Mann–Whitney U test to test the difference between socioeconomic and built environment variables between high and low accessibility areas. We found that vulnerable groups favored access to the NRSs over more affluent groups. This study’s results suggest that urban planners and designers should contemplate ways to enhance the walkability of the residents and continually monitor accessibility to prevent urban problems, such as food deserts and retail deserts. Additionally, the results provide empirical evidence for achieving equity in urban development and urban retail systems to further enhance sustainability.
Driving Network Externalities in Education for Sustainable Development
Authors: Ho Bach Quang , Inoue Yuki
Published: October 2020

Solving important social problems and promoting sustainable development requires solutions involving multiple stakeholders. Nevertheless, previous social marketing studies were limited to individual behavioral changes and lacked a perspective to involve surrounding stakeholders. This study focused on education for sustainable development (ESD) on a field trip and clarified the factors that promote students’ knowledge diffusion from the viewpoint of network externalities. A questionnaire was distributed, and responses from 1950 high school students were collected. This study used factor analysis to unveil the factors related to students’ features and field trip experiences and clarified how these factors promote driving network externalities and expanding the network through regression analysis. The findings indicated that the experiential value obtained from visiting a site with actual social problems has a large positive effect on driving network externalities and expanding the network. Therefore, encouraging driving network externalities and expanding networks by providing ESD on a field trip can contribute to solve social problems and achieve sustainable development.
Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context
Authors: Graba Massinissa , Kelouwani Sousso , Zeghmi Lotfi , Amamou Ali , Agbossou Kodjo , Mohammadpour Mohammad
Published: October 2020

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value.
Scale-Up of Physics-Based Models for Predicting Degradation of Large Lithium Ion Batteries
Authors: Kim Hong-Keun , Lee Kyu-Jin
Published: October 2020

Large lithium-ion batteries (LIBs) demonstrate different performance and lifetime compared to small LIB cells, owing to the size effects generated by the electrical configuration and property imbalance. However, the calculation time for performing life predictions with three-dimensional (3D) cell models is undesirably long. In this paper, a lumped cell model with equivalent resistances (LER cell model) is proposed as a reduced order model of the 3D cell model, which enables accurate and fast life predictions of large LIBs. The developed LER cell model is validated via the comparisons with results of the 3D cell models by simulating a 20-Ah commercial pouch cell (NCM/graphite) and the experimental values. In addition, the LER cell models are applied to different cell types and sizes, such as a 20-Ah cylindrical cell and a 60-Ah pouch cell.