bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II
Authors: Liu Wei , Luo Fengming , Liu Yuanhong , Ding Wei
Published: December 2019

With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level.
Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)
Authors: Haarpaintner Jörg , Hindberg Heidi
Published: December 2019

The European Space Agency’s (ESA) “SAR for REDD” project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C- band SAR can compensate for the L-band’s deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4–5 percent points and 1–2 percent points for C- and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C- and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program’s and JAXA’s ALOS-1/2 based global FNF maps.
Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm
Authors: Abdalla Alwaseela , Cen Haiyan , Abdel-Rahman Elfatih , Wan Liang , He Yong
Published: December 2019

Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ΔΕ and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels.
Filtering the NPP-VIIRS Nighttime Light Data for Improved Detection of Settlements in Africa
Authors: Yuan Xiaotian , Jia Li , Menenti Massimo , Zhou Jie , Chen Qiting
Published: December 2019

Observing and understanding changes in Africa is a hotspot in global ecological environmental research since the early 1970s. As possible causes of environmental degradation, frequent droughts and human activities attracted wide attention. Remote sensing of nighttime light provides an effective way to map human activities and assess their intensity. To identify settlements more effectively, this study focused on nighttime light in the northern Equatorial Africa and Sahel settlements to propose a new method, namely, the patches filtering method (PFM) to identify nighttime lights related to settlements from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) monthly nighttime light data by separating signal components induced by biomass burning, thereby generating a new annual image in 2016. The results show that PFM is useful for improving the quality of NPP-VIIRS monthly nighttime light data. Settlement lights were effectively separated from biomass burning lights, in addition to capturing the seasonality of biomass burning. We show that the new 2016 nighttime light image can very effectively identify even small settlements, notwithstanding their fragmentation and unstable power supply. We compared the image with earlier NPP-VIIRS annual nighttime light data from the National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Information (NCEI) for 2016 and the Sentinel-2 prototype Land Cover 20 m 2016 map of Africa released by the European Space Agency (ESA-S2-AFRICA-LC20). We found that the new annual nighttime light data performed best among the three datasets in capturing settlements, with a high recognition rate of 61.8%, and absolute superiority for settlements of 2.5 square kilometers or less. This shows that the method separates biomass burning signals very effectively, while retaining the relatively stable, although dim, lights of small settlements. The new 2016 annual image demonstrates good performance in identifying human settlements in sparsely populated areas toward a better understanding of human activities.
Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong
Authors: Xu Kai , Wang Xiaofeng , Kong Chunfang , Feng Ruyi , Liu Gang , Wu Chonglong
Published: December 2019

Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.
Hybrid Signal-Processing Method Based on Neural Network for Prediction of NO3, K, Ca, and Mg Ions in Hydroponic Solutions Using an Array of Ion-Selective Electrodes
Authors: Cho Woo-Jae , Kim Hak-Jin , Jung Dae-Hyun , Han Hee-Jo , Cho Young-Yeol
Published: December 2019

In closed hydroponics, fast and continuous measurement of individual nutrient concentrations is necessary to improve water- and nutrient-use efficiencies and crop production. Ion-selective electrodes (ISEs) could be one of the most attractive tools for hydroponic applications. However, signal drifts over time and interferences from other ions present in hydroponic solutions make it difficult to use the ISEs in hydroponic solutions. In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed. In addition, the ANN-based approach for the prediction of Mg concentration which had no feasible ISE was conducted by interpreting the signals from a sensor array consisting of electrical conductivity (EC) and ion-selective electrodes (NO3, K, and Ca). From the application test using 8 samples from real greenhouses, the hybrid method based on a combination of the TPN and ANN methods showed relatively low root mean square errors of 47.2, 13.2, and 18.9 mg∙L−1 with coefficients of variation (CVs) below 10% for NO3, K, and Ca, respectively, compared to those obtained by separate use of the two methods. Furthermore, the Mg prediction results with a root mean square error (RMSE) of 14.6 mg∙L−1 over the range of 10–60 mg∙L−1 showed potential as an approximate diagnostic tool to measure Mg in hydroponic solutions. These results demonstrate that the hybrid method can improve the accuracy and feasibility of ISEs in hydroponic applications.
Ear Detection Using Convolutional Neural Network on Graphs with Filter Rotation
Authors: Tomczyk Arkadiusz , Szczepaniak Piotr S.
Published: December 2019

Geometric deep learning (GDL) generalizes convolutional neural networks (CNNs) to non-Euclidean domains. In this work, a GDL technique, allowing the application of CNN on graphs, is examined. It defines convolutional filters with the use of the Gaussian mixture model (GMM). As those filters are defined in continuous space, they can be easily rotated without the need for some additional interpolation. This, in turn, allows constructing systems having rotation equivariance property. The characteristic of the proposed approach is illustrated with the problem of ear detection, which is of great importance in biometric systems enabling image based, discrete human identification. The analyzed graphs were constructed taking into account superpixels representing image content. This kind of representation has several advantages. On the one hand, it significantly reduces the amount of processed data, allowing building simpler and more effective models. On the other hand, it seems to be closer to the conscious process of human image understanding as it does not operate on millions of pixels. The contributions of the paper lie both in GDL application area extension (semantic segmentation of the images) and in the novel concept of trained filter transformations. We show that even significantly reduced information about image content and a relatively simple, in comparison with classic CNN, model (smaller number of parameters and significantly faster processing) allows obtaining detection results on the quality level similar to those reported in the literature on the UBEAR dataset. Moreover, we show experimentally that the proposed approach possesses in fact the rotation equivariance property allowing detecting rotated structures without the need for labor consuming training on all rotated and non-rotated images.
Hand Rehabilitation and Telemonitoring through Smart Toys
Authors: Borghese N. Alberto , Essenziale Jacopo , Mainetti Renato , Mancon Elena , Pagliaro Rossella , Pajardi Giorgio
Published: December 2019

We describe here a platform for autonomous hand rehabilitation and telemonitoring of young patients. A toy embedding the electronics required to sense fingers pressure in different grasping modalities is the core element of this platform. The system has been realized following the user-centered design methodology taking into account stakeholder needs from start: clinicians require reliable measurements and the ability to get a picture remotely on rehabilitation progression; children have asked to interact with a pleasant and comfortable object that is easy to use, safe, and rewarding. These requirements are not antithetic, and considering both since the design phase has allowed the realization of a platform reliable to clinicians and keen to be used by young children.
A Spatial Adaptive Algorithm Framework for Building Pattern Recognition Using Graph Convolutional Networks
Authors: Bei Weijia , Guo Mingqiang , Huang Ying
Published: December 2019

Graph learning methods, especially graph convolutional networks, have been investigated for their potential applicability in many fields of study based on topological data. Their topological data processing capabilities have proven to be powerful. However, the relationships among separate entities include not only topological adjacency, but also correlation in vision, for example, the spatial vector data of buildings. In this study, we propose a spatial adaptive algorithm framework with a data-driven design to accomplish building group division and building group pattern recognition tasks, which is not sensitive to the difference in the spatial distribution of the buildings in various geographical regions. In addition, the algorithm framework has a multi-stage design, and processes the building group data from whole to parts, since the objective is closely related to multi-object detection on topological data. By using the graph convolution method and a deep neural network (DNN), the multitask model in this study can learn human thoughts through supervised training, and the whole process only depends upon the descriptive vector data of buildings without any ancillary data for building group partition. Experiments confirmed that the method for expressing buildings and the effect of the algorithm framework proposed are satisfactory. In summary, using deep learning methods to complete the tasks of building group division and building group pattern recognition is potentially effective, and the algorithm framework is worth further research.
Investigation of Regression Methods for Reduction of Errors Caused by Bending of FSR-Based Pressure Sensing Systems Used for Prosthetic Applications
Authors: Ahmadizadeh Chakaveh , Menon Carlo
Published: December 2019

The pressure map at the interface of a prosthetic socket and a residual limb contains information that can be used in various prosthetic applications including prosthetic control and prosthetic fitting. The interface pressure is often obtained using force sensitive resistors (FSRs). However, as reported by multiple studies, accuracies of the FSR-based pressure sensing systems decrease when sensors are bent to be positioned on a limb. This study proposes the use of regression-based methods for sensor calibration to address this problem. A sensor matrix was placed in a pressure chamber as the pressure was increased and decreased in a cyclic manner. Sensors’ responses were assessed when the matrix was placed on a flat surface or on one of five curved surfaces with various curvatures. Three regression algorithms, namely linear regression (LR), general regression neural network (GRNN), and random forest (RF), were assessed. GRNN was selected due to its performance. Various error compensation methods using GRNN were investigated and compared to improve instability of sensors’ responses. All methods showed improvements in results compared to the baseline. Developing a different model for each of the curvatures yielded the best results. This study proved the feasibility of using regression-based error compensation methods to improve the accuracy of mapping sensor readings to pressure values. This can improve the overall accuracy of FSR-based sensory systems used in prosthetic applications.
Magnetic Condition-Independent 3D Joint Angle Estimation Using Inertial Sensors and Kinematic Constraints
Authors: Lee Jung Keun , Jeon Tae Hyeong
Published: December 2019

In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional (3D) joint angle estimation method based on IMU signals. The proposed method is implemented in a sequential direction cosine matrix-based orientation Kalman filter (KF), which is composed of an attitude estimation KF followed by a heading estimation KF. In the heading estimation KF, an acceleration-level kinematic constraint from a spherical joint replaces the magnetometer signals for the correction procedure. Because the proposed method does not rely on the magnetometer, it is completely magnetic condition-independent and is not affected by the magnetic disturbance. For the averaged root mean squared errors of the three tests performed using a rigid two-link system, the proposed method produced 1.58°, while the conventional method with the magnetic disturbance compensation mechanism produced 5.38°, showing a higher accuracy of the proposed method in the magnetically disturbed conditions. Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment.
Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study
Authors: Mohino-Herranz Inma , Gil-Pita Roberto , Rosa-Zurera Manuel , Seoane Fernando
Published: December 2019

Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 s window length.
Real-Time Noninvasive Measurement of Glucose Concentration Using a Modified Hilbert Shaped Microwave Sensor
Authors: Odabashyan Levon , Babajanyan Arsen , Baghdasaryan Zhirayr , Kim Seungwan , Kim Jongchel , Friedman Barry , Lee Jung-Ha , Lee Kiejin
Published: December 2019

We developed a microwave glucose sensor based on the modified first-order Hilbert curve design and measured glucose concentration in aqueous solutions by using a real-time microwave near-field electromagnetic interaction technique. We observed S21 transmission parameters of the sensor at resonant frequencies depend on the glucose concentration. We could determine the glucose concentration in the 0–250 mg/dL concentration range at an operating frequency of near 6 GHz. The measured minimum detectable signal was 0.0156 dB/(mg/dL) and the measured minimum detectable concentration was 1.92 mg/dL. The simulation result for the minimum detectable signal and the minimum detectable concentration was 0.0182 dB/(mg/dL) and 1.65 mg/dL, respectively. The temperature instability of the sensor for human glycemia in situ measurement range (27–34 °C for fingers and 36–40 °C for body temperature ranges) can be improved by the integration of the temperature sensor in the microwave stripline platform and the obtained data can be corrected during signal processing. The microwave signal–temperature dependence is almost linear with the same slope for a glucose concentration range of 50–150 mg/dL. The temperature correlation coefficient is 0.05 dB/°C and 0.15 dB/°C in 27–34 °C and 36–40 °C temperature range, respectively. The presented system has a cheap, easy fabrication process and has great potential for non-invasive glucose monitoring.
Lessons Learned from Rural Electrification Experiences with Third Generation Solar Home Systems in Latin America: Case Studies in Peru, Mexico, and Bolivia
Authors: Eras-Almeida Andrea A. , Fernández Miguel , Eisman Julio , Martín José G. , Caamaño Estefanía , Egido-Aguilera Miguel A.
Published: December 2019

There are 17 million people without access to electricity services in Latin America. This population lives in small isolated and scattered communities with low incomes where it is difficult to achieve 100% access to electricity by the grid extension. Therefore, it is necessary to create market mechanisms and promote off-grid electrification in which photovoltaic (PV) technology plays a fundamental role. This research assesses successful projects developed in Peru, Mexico, and Bolivia, where 3rd Generation Solar Home Systems (3G-SHSs) are being introduced to support off-grid initiatives. To do so, we applied a mixed-methods approach including a comparative case study analysis, an extensive literature review, focus group discussions, and field research. Thereby, the lessons learned reveal that confidence, commitment, and flexibility are the main pillars of rural electrification. Additionally, it is demonstrated that the combination of various business models—an energy service company, fee-for-service, pay-as-you-go, and a microfranchising—with 3G-SHSs is powerfully effective in terms of sustainability. Our findings are useful to policy makers, researchers, promoters, and other stakeholders to rethink intervention strategies in rural areas. Access to electricity must be a state policy to facilitate the participation of new actors, especially of the private sector and communities, and the introduction of innovative business models and high-quality technology.
Evaluation of Sustainable Development Management in EU Countries
Authors: Popović Blaženka , Janković Šoja Svjetlana , Paunović Tamara , Maletić Radojka
Published: December 2019

The term sustainable development is used in many areas and spheres of life and is becoming a modern phenomenon determining the direction of progress of every society. Sustainable development implies continuous economic development with respect to environmental principles and focuses on overcoming conflicts between economy and ecology. The aim of this paper is to evaluate sustainable development management in EU countries through selected indicators characterizing sustainable development and its core dimensions. We present the results of an evaluation of the sustainable development of EU countries using cluster analysis. Countries that are similar in terms of sustainability are identified by cluster analysis. Four clusters were separated, the affiliation of each country to a particular cluster was determined, and the characteristics of each cluster were defined. The goal of sustainability management is to create an adequate approach to development of the economic and environmental performance, and therefore achieve sustainability. The achievements of most developed countries should be an incentive for those that are at a lower level of development. In addition, this research provides insight into some specific goals of sustainable development where some countries are lagging behind. This could be the basis for directing efforts in specific areas of sustainable development to improve their own position in such an important global issue.
Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance
Authors: Hao Shengbin , Zhang Haili , Song Michael
Published: December 2019

Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability.
Assessing SMEs’ Sustainable Growth through Entrepreneurs’ Ability and Entrepreneurial Orientation: An Insight into SMEs in Côte d’Ivoire
Authors: Diabate Ardjouman , Sibiri Hagan , Wang Linyu , Yu Liying
Published: December 2019

In most countries, small and medium-sized enterprises (SMEs) play a key role in driving sustainable economic growth and job creation; hence, the need to investigate factors (e.g., entrepreneurial factors) that influence SMEs’ sustainable growth (SMESG). This study provides an insight into entrepreneurs’ abilities (EAs) that affect SMESG in Côte d’Ivoire (a middle-income economy located in the West African region) and an assessment of the extent to which entrepreneurial orientation (EO) influences the EA–SMESG relationship. By using data from 320 Ivorian SMEs, the results of hypothesis testing confirm an association between SMESG and each EA dimension (creativity, risk control, relationship, and opportunity detection ability), learning ability excepted. As for the moderating effect of EO, the innovativeness in entrepreneurship positively and significantly regulates the EA–SMESG relationship; proactiveness positively regulates the relationship between almost all EA dimensions and SMESG; and risk tendency regulates the relationship EA–SMESG for creativity and risk-control ability. Based on major findings, management implications are formulated in relation to promoting SMEs’ sustainable growth. For example, in light of the impact of EA on SMESG, development actors can increase the efficiency of Ivorian SMEs through actions aiming at strengthening the abilities of entrepreneurs and managers.
Effects of SNS Social Capital on E-Service Quality and Sustained Referral Intentions of E-Fitness Apparel: Comparative Body Image Satisfaction Analysis
Authors: Nam Changhyun , Son Jihyeong , Yu Jae-Gu
Published: December 2019

Fitness apparel companies target consumers with easy access to social media (e.g., Facebook, Instagram, and Pinterest). However, fitness apparel companies have struggled to incorporate social interactivity into their marketing strategies due to a lack of knowledge about consumers’ social media behaviors and different country contexts. The purpose of this study was to investigate (1) comparison of college students’ body image satisfaction in both the United States (U.S.) and South Korea and (2) how their body satisfaction influences consumer communication and the sustained referral intentions of fitness apparel in social media. The findings from 1144 survey responses of U.S. and South Korean college students reveal that student body satisfaction differs between the two countries. Body-dissatisfied U.S. and South Korean students with social capital on social networking websites are directly influenced by word-of-mouth regarding online fitness apparel purchases. Furthermore, perceived e-service quality, including website design and website responsiveness, is a significant mediator in both cultures, affecting the word-of-mouth for fitness-related purchases. This study provides evidence for marketers of fitness apparel, particularly e-marketers, to consider the cultural differences in customer preferences and customer body satisfaction, so as to enhance service performance.
Specificity of Sustainable Structural Dynamics of Local Economy in Romanian Tourist Resorts
Authors: Grecu Alexandra , Gruia Andreea Karina , Marin Marian , Bănuță Mariana , Olteanu Cosmin , Constantin Ionuț , Gadoiu Mihaela , Teodorescu Camelia , Dobrea Răzvan Cătălin , Drăghici Cristian Constantin
Published: December 2019

The lack of sustainable development strategies of the tourist resorts from Romania caused the structural dynamics of their economy to record large fluctuations under the impact of determinants with unpredictable developments. The spectacular dynamics have led to developments difficult to predict, in many situations that generate environmental conflicts, where the economic pressure has exceeded the support capacity of the natural environment. In this study, we aim to analyze the dynamic relationship between tourism and the other components of the economy, from each resort from Romania. The significant growth of tourism in recent years requires new approaches, relevant for understanding the role of tourism in increasing the complexity of the local economy. Measuring the role of tourism in the development of local economy was achieved by building an economic database for all tourist resorts from Romania (according to Government Decision No. 107 of 2018), at the four-digit NACE code level (Classification of National Economy Activities), for the economic indicators considered relevant: number of companies, number of employees, and turnover and profit, for the period 2000–2016, as well as at territorial administrative unit level. Detailed analyses revealed very different structural dynamics, the spectacular dynamics, lacking a coherent strategic framework, led in all situations to the emergence of environmental conflicts. Tourist resorts in which tourism has become an essential component of economic development have based their development on capitalization of local resources, which led to multiplication effects, materialized in a spectacular evolution of the local economy, and an increasing pressure on the natural environment.
Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis
Authors: Pirra Miriam , Pensa Ruggero G.
Published: December 2019

The quality of the transport system offered at city level constitutes an important and challenging goal for society, for local authorities, and transport operators. Therefore, appropriate evaluation of travellers’ satisfaction is required to support service performance monitoring, benchmarking, and market analysis. This aspect implies the collection of satisfaction levels for different passengers’ groups, as it could provide interesting suggestions for identifying priority areas of action. To this end, an original study aimed at understanding the main aspects affecting the common view of satisfaction among different kinds of travellers at European level is presented in this paper. A specific survey investigating how travellers perceive the quality of their journey is proposed to people living in cities characterised by different sizes. Data are then analysed through a multi-view co-clustering algorithm, an innovative machine learning technique that highlights clusters of respondents grouped according to various categories of features. Such results could be used by local authorities and transport providers to understand the specific actions to be operated to improve the quality of transport service offered in a market segmentation dimension.
Investigation of Groundwater Withdrawal and Recharge Affecting Underground Structures in the Shanghai Urban Area
Authors: Wu Yong-Xia , Yang Tian-Liang , Li Pei-Chao , Lin Jin-Xin
Published: December 2019

In this paper, the hydrogeological features of Quaternary deposits in Shanghai as well as the characteristics of groundwater withdrawal and recharge in urban areas are investigated. One phreatic aquifer and five confined aquifers (AqI to AqV) are present in Shanghai, and these aquifers are separated by five aquitards. Groundwater withdrawal from confined aquifers has resulted in land subsidence in Shanghai. To control land subsidence, the groundwater withdrawal volume has been decreased, and the groundwater recharge volume has been increased since 1965. Correspondingly, the pressure head in confined aquifers has risen. The groundwater head increases in shallow aquifers may impact underground structures and lead to the following issues: i) an increased risk of water in-rushing hazards caused by confined water pressure during structural excavations and ii) an increased instability risk caused by groundwater buoyancy. Both excavation anti-uprush and underground structure anti-floating are discussed in this paper. Based on the risk possibilities, the anti-uprush of the excavation is divided into six regions, and the structural anti-floating is divided into five regions in urban areas. To avoid geohazards caused by the rise in groundwater head, real-time monitoring of the pressure head in AqII is recommended.
Green ICT Adoption Using a Maturity Model
Authors: Hankel Albert , Heimeriks Gaston , Lago Patricia
Published: December 2019

The field green ICT focuses on the greening of ICT and using ICT to optimise the energy footprint of the ICT-supported processes. For organisations, applying green ICT in the broadest sense presents them with challenges. In this paper we explore what factors are influencing the adoption of green ICT. We follow three organisations that used a green ICT tool, the SURF Green ICT Maturity Model, to identify such factors. Tools like the maturity model help organisations identify the what and how. We found other factors, such as strategic alignment, culture and leadership, determine the why. As ICT is a general purpose technology, it potentially affects all processes in an organisation. To have a greening impact, the main challenge for green ICT is to take a systemic approach and involve all (relevant) parties. ICT departments often position themselves as support (followers, not leaders); this proves to be a big hurdle in the adoption of green ICT.
The Role of Planning Policies in Promoting Urban Sprawl in Intermediate Cities: Evidence from Chile
Authors: Barton Jonathan R. , Ramírez María Inés
Published: December 2019

Urban sprawl has been studied principally as a phenomenon produced by a lack of or weakness in urban planning, as a consequence of real estate liberalization. This article examines the Chilean case, and proposes that the state has been the engine of this phenomenon through spatial planning instruments that have both neoliberal and neostructural features, and that are best defined by the concept, new public management. The analysis tracks urban sprawl in four intermediate cities, which have experienced high rates of growth since 2000, using photointerpretation of satellite images between 2003 and 2011, and the creation of a typology to define land uses and housing types. The results show that intermediate cities follow similar trends to the capital city, Santiago, and face similar problems, in particular the concentration of services in the urban core. These similarities are produced by the application of general planning instruments: Article 55 and Decree Law 3516. While most research on urban sprawl focuses on private agency, this article highlights the role of the state in its production. It is therefore relevant to explore the nature of public agency in urban sprawl processes in different metropolitan and intermediate cities, and how planning policies can be adapted to curb the phenomenon.
Radiative Processes of Two Accelerated Entangled Atoms Near Boundaries
Authors: Zhang Chi , Zhou Wenting
Published: December 2019

By considering the interaction between a two-atom system and the vacuum massless scalar field in the viewpoint of an instantaneously inertial observer, we study the rates of transition of a uniformly accelerated two-atom system in the symmetric/antisymmetric entangled state near a reflecting boundary and in a cavity, respectively. We find that both the downward transition | ψ ± ⟩ → | g A g B ⟩ and the upward transition | ψ ± ⟩ → | e A e B ⟩ occur for the accelerated two-atom system, as in sharp contrast with the case of a static two-atom system, in which the upward transition can never happen. Similar to the rates of transition of atoms immersed in a thermal bath with the FDU temperature, both the downward transition rate and the upward transition rate are characterized by the Plank factor ( e 2 π ω 0 / a − 1 ) − 1 . This character of the transition rates is very different from the other radiative properties of the accelerated two-atom system, such as the resonance interatomic energy, for which the revisions of the effects of uniform acceleration are never characterized by such a factor. We show with analytical and numerical results that both the downward transition and the upward transition processes can be effectively manipulated by the atomic non-inertial motion and by the presence of boundaries. By comparing the upward transition rate with the downward transition rate, we discover that, when ω 0 ≫ a , with ω 0 and a being the energy space and the proper acceleration of the two-atom system, the disentanglement caused by the upward transition is negligible, while, if ω 0 ≪ a , the disentanglement caused by the upward transition becomes as important as that caused by the downward transition.
The Therapeutic Effects and Pathophysiology of Botulinum Toxin A on Voiding Dysfunction Due to Urethral Sphincter Dysfunction
Authors: Kao Yao-Lin , Huang Kuan-Hsun , Kuo Hann-Chorng , Ou Yin-Chien
Published: December 2019

Neurogenic and non-neurogenic urethral sphincter dysfunction are common causes of voiding dysfunction. Injections of botulinum toxin A (BoNT-A) into the urethral sphincter have been used to treat urethral sphincter dysfunction (USD) refractory to conventional treatment. Since its first use for patients with detrusor sphincter dyssynergia in 1988, BoNT-A has been applied to various causes of USD, including dysfunctional voiding, Fowler’s syndrome, and poor relaxation of the external urethral sphincter. BoNT-A is believed to decrease urethral resistance via paralysis of the striated sphincter muscle through inhibition of acetylcholine release in the neuromuscular junction. Recovery of detrusor function in patients with detrusor underactivity combined with a hyperactive sphincter also suggested the potential neuromodulation effect of sphincteric BoNT-A injection. A large proportion of patients with different causes of USD report significant improvement in voiding after sphincteric BoNT-A injections. However, patient satisfaction might not increase with an improvement in the symptoms because of concomitant side effects including exacerbated incontinence, urinary urgency, and over-expectation. Nonetheless, in terms of efficacy and safety, BoNT-A is still a reasonable option for refractory voiding function. To date, studies focusing on urethral sphincter BoNT-A injections have been limited to the heterogeneous etiologies of USD. Further well-designed studies are thus needed.
The Cytotoxicity Effect of Resveratrol: Cell Cycle Arrest and Induced Apoptosis of Breast Cancer 4T1 Cells
Authors: Wu Hong , Chen Liang , Zhu Feifei , Han Xu , Sun Lindan , Chen Keping
Published: December 2019

Resveratrol, a natural polyterpenoid, can scavenge reactive oxygen species in vivo to carry out the functions of antioxidation and antiaging. Resveratrol’s anti-cancer capability has attracted widespread attention, but its molecular mechanism has not been systematically explained. In this study, by comparing the activity of normal cell lines and cancer cell lines after treating with resveratrol, it was found that resveratrol has more significant cytotoxicity in cancer cell lines. Resveratrol could play a toxic role through inducing apoptosis of the cancer cell in a time- and concentration-dependent manner. A total of 330 significantly differential genes were identified through large-scale transcriptome sequencing, among which 103 genes were upregulated and 227 genes were downregulated. Transcriptome and qRT-PCR data proved that a large number of genes related to cell cycle were differentially expressed after the treatment of resveratrol. The changes of cell cycle phases at different time points after treating with resveratrol were further detected, and it was found that the cells were arrested in the S phase because of the percentage of cells in S phase increased and cells in G1/G0 phase decreased. In conclusion, resveratrol can inhibit the proliferation of 4T1 cancer cells by inhibiting cell cycle and inducing apoptosis.
The Evolution of the Malaria Clinic: The Cornerstone of Malaria Elimination in Thailand
Authors: Sudathip Prayuth , Kitchakarn Suravadee , Thimasarn Krongthong , Gopinath Deyer , Naing Tinzar , Sajjad Omar , Hengprasert Sumetha
Published: December 2019

Background: Malaria Clinics (MCs) have served communities in Thailand since 1965 and are still playing a critical role in providing early diagnosis and effective treatment of malaria. Methods: We reviewed six decades of published manuscripts, articles, strategies, and plans regarding MC operations in Thailand;,and analyzed national program surveillance data in both malaria control and malaria elimination phases. Results: MCs accounted for 39.8% of malaria tests and 54.8% of positive cases by the end of the 1980s. The highest number of MCs established was 544 in 1997. MCs contributed to 6.7% of all tests and 30% of all positive cases over the 2015–2017 period. Between 2017 and June 2019, during the malaria elimination phase, MCs continued to test an average of 67% of all persons tested for malaria, and confirmed 38.3% of all positive cases detected in the country. Conclusions: Testing and positive rates of MCs are on a gradual decline as the overall burden of malaria declines annually, which may reflect decreasing transmission intensity. Although the number of MCs in the last three years has been stable (n = 240), the attrition of MC staff poses a real challenge to the longevity of MCs in the absence of a human resource plan to support the elimination phase. It is necessary to identify and support capacity gaps and needs as MCs are absorbed into an integrated and decentralized program, while ensuring that the Division of Vector Borne Diseases (DVBD) maintains its necessary technical and advisory role.
Distinct Lineages of Feline Parvovirus Associated with Epizootic Outbreaks in Australia, New Zealand and the United Arab Emirates
Authors: Van Brussel Kate , Carrai Maura , Lin Carrie , Kelman Mark , Setyo Laura , Aberdein Danielle , Brailey Juliana , Lawler Michelle , Maher Simone , Plaganyi Ildiko , Lewis Emily , Hawkswell Adele , Allison Andrew B. , Meers Joanne , Martella Vito , Beatty Julia A. , Holmes Edward C. , Decaro Nicola , Barrs Vanessa R.
Published: December 2019

Feline panleukopenia (FPL), a frequently fatal disease of cats, is caused by feline parvovirus (FPV) or canine parvovirus (CPV). We investigated simultaneous outbreaks of FPL between 2014 and 2018 in Australia, New Zealand and the United Arab Emirates (UAE) where FPL outbreaks had not been reported for several decades. Case data from 989 cats and clinical samples from additional 113 cats were obtained to determine the cause of the outbreaks and epidemiological factors involved. Most cats with FPL were shelter-housed, 9 to 10 weeks old at diagnosis, unvaccinated, had not completed a primary vaccination series or had received vaccinations noncompliant with current guidelines. Analysis of parvoviral VP2 sequence data confirmed that all FPL cases were caused by FPV and not CPV. Phylogenetic analysis revealed that each of these outbreaks was caused by a distinct FPV, with two virus lineages present in eastern Australia and virus movement between different geographical locations. Viruses from the UAE outbreak formed a lineage of unknown origin. FPV vaccine virus was detected in the New Zealand cases, highlighting the difficulty of distinguishing the co-incidental shedding of vaccine virus in vaccinated cats. Inadequate vaccination coverage in shelter-housed cats was a common factor in all outbreaks, likely precipitating the multiple re-emergence of infection events.