bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating Nitrogen from Structural Crop Traits at Field Scale—A Novel Approach Versus Spectral Vegetation Indices
Authors: Tilly Nora , Bareth Georg
Published: September 2019

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R2 < 0.85) than on spectral data (R2 < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R2: 0.40–0.81) than on spectral data (R2: 0.18–0.68). Overall, this first study shows the potential of crop-specific across‑season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.
SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks—Optimization, Opportunities and Limits
Authors: Fuentes Reyes Mario , Auer Stefan , Merkle Nina , Henry Corentin , Schmitt Michael
Published: September 2019

Due to its all time capability, synthetic aperture radar (SAR) remote sensing plays an important role in Earth observation. The ability to interpret the data is limited, even for experts, as the human eye is not familiar to the impact of distance-dependent imaging, signal intensities detected in the radar spectrum as well as image characteristics related to speckle or steps of post-processing. This paper is concerned with machine learning for SAR-to-optical image-to-image translation in order to support the interpretation and analysis of original data. A conditional adversarial network is adopted and optimized in order to generate alternative SAR image representations based on the combination of SAR images (starting point) and optical images (reference) for training. Following this strategy, the focus is set on the value of empirical knowledge for initialization, the impact of results on follow-up applications, and the discussion of opportunities/drawbacks related to this application of deep learning. Case study results are shown for high resolution (SAR: TerraSAR-X, optical: ALOS PRISM) and low resolution (Sentinel-1 and -2) data. The properties of the alternative image representation are evaluated based on feedback from experts in SAR remote sensing and the impact on road extraction as an example for follow-up applications. The results provide the basis to explain fundamental limitations affecting the SAR-to-optical image translation idea but also indicate benefits from alternative SAR image representations.
Remote Sensing Estimation of Lake Total Phosphorus Concentration Based on MODIS: A Case Study of Lake Hongze
Authors: Xiong Junfeng , Lin Chen , Ma Ronghua , Cao Zhigang
Published: September 2019

Phosphorus (P) is an important substance for the growth of phytoplankton and an efficient index to assess the water quality. However, estimation of the TP concentration in waters by remote sensing must be associated with optical substances such as the chlorophyll-a (Chla) and the suspended particulate matter (SPM). Based on the good correlation between the suspended inorganic matter (SPIM) and P in Lake Hongze, we used the direct and indirect derivation methods to develop algorithms for the total phosphorus (TP) estimation with the MODIS/Aqua data. Results demonstrate that the direct derivation algorithm based on 645 nm and 1240 nm of the MODIS/Aqua performs a satisfied accuracy (R2 = 0.75, RMSE = 0.029mg/L, MRE = 39% for the training dataset, R2 = 0.68, RMSE = 0.033mg/L, MRE = 47% for the validate dataset), which is better than that of the indirect derivation algorithm. The 645 nm and 1240 nm of MODIS are the main characteristic band of the SPM, so that algorithm can effectively reflect the P variations in Lake Hongze. Additionally, the ratio of the TP to the SPM is positively correlated with the accuracy of the algorithm as well. The proportion of the SPIM in the SPM has a complex effect on the accuracy of the algorithm. When the SPIM accounts for 78%, the algorithm achieves the highest accuracy. Furthermore, the performance of this direct derivation algorithm was examined in two inland lakes in China (Lake Nanyi and Lake Chaohu), it derived the expected P distribution in Lake Nanyi whereas the algorithm failed in Lake Chaohu. Different water properties influence significantly the accuracy of this direct derivation algorithm, while the TP, Chla, and suspended particular inorganic matter (SPOM) of Lake Chaohu are much higher than those of the other two lakes, thus it is difficult to estimate the TP concentration by a simple band combination in Lake Chaohu. Although the algorithm depends on the dataset used in the development, it usually presents a good estimation for those waters where the SPIM dominated, especially when the SPIM accounts for 60% to 80% of the SPM. This research proposed a direct derivation algorithm for the TP estimation for the turbid lake and will provide a theoretical and practical reference for extending the optical remote sensing application and the TP empirical algorithm of Lake Hongze’s help for the local government management water quality.
Performance of Selected Ionospheric Models in Multi-Global Navigation Satellite System Single-Frequency Positioning over China
Authors: Wang Ahao , Chen Junping , Zhang Yize , Meng Lingdong , Wang Jiexian
Published: September 2019

Ionospheric delay as the major error source needs to be properly handled in multi-GNSS (Global Navigation Satellite System) single-frequency positioning and the different ionospheric models exhibit apparent performance difference. In this study, two single-frequency positioning solutions with different ionospheric corrections are utilized to comprehensively analyze the ionospheric delay effects on multi-frequency and multi-constellation positioning performance, including standard point positioning (SPP) and ionosphere-constrained precise point positioning (PPP). The four ionospheric models studied are the GPS broadcast ionospheric model (GPS-Klo), the BDS (BeiDou Navigation Satellite System) broadcast ionospheric model (BDS-Klo), the BDS ionospheric grid model (BDS-Grid) and the Global Ionosphere Maps (GIM) model. Datasets are collected from 10 stations over one month in 2019. The solar remained calm and the ionosphere was stable during the test period. The experimental results show that for single-frequency SPP, the GIM model achieves the best accuracy, and the positioning accuracy of the BDS-Klo and BDS-Grid model is much better than the solution with GPS-Klo model in the N and U components. For the single-frequency PPP performance, the average convergence time of the ionosphere-constrained PPP is much reduced compared with the traditional PPP approach, where the improvements are of 11.2%, 11.9%, 21.3% and 39.6% in the GPS-Klo-, BDS-Klo-, BDS-Grid- and GIM-constrained GPS + GLONASS + BDS single-frequency PPP solutions, respectively. Furthermore, the positioning accuracy of the BDS-Grid- and GIM-constrained PPP is generally the same as the ionosphere-free combined single-frequency PPP. Through the combination of GPS, GLONASS and BDS, the positioning accuracy and convergence performance for all single-system single-frequency SPP/PPP solutions can be effectively improved.
Selected Papers from the 9th World Congress on Industrial Process Tomography
Authors: Soleimani Manuchehr , Wondrak Thomas , Tan Chao
Published: September 2019

Industrial process tomography (IPT) is a set of multi-dimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors [...]
Detecting Moments of Stress from Measurements of Wearable Physiological Sensors
Authors: Kyriakou Kalliopi , Resch Bernd , Sagl Günther , Petutschnig Andreas , Werner Christian , Niederseer David , Liedlgruber Michael , Wilhelm Frank , Osborne Tess , Pykett Jessica
Published: September 2019

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.
Noise Suppression for GPR Data Based on SVD of Window-Length-Optimized Hankel Matrix
Authors: Xue Wei , Luo Yan , Yang Yue , Huang Yujin
Published: September 2019

Ground-penetrating radar (GPR) is an effective tool for subsurface detection. Due to the influence of the environment and equipment, the echoes of GPR contain significant noise. In order to suppress noise for GPR data, a method based on singular value decomposition (SVD) of a window-length-optimized Hankel matrix is proposed in this paper. First, SVD is applied to decompose the Hankel matrix of the original data, and the fourth root of the fourth central moment of singular values is used to optimize the window length of the Hankel matrix. Then, the difference spectrum of singular values is used to construct a threshold, which is used to distinguish between components of effective signals and components of noise. Finally, the Hankel matrix is reconstructed with singular values corresponding to effective signals to suppress noise, and the denoised data are recovered from the reconstructed Hankel matrix. The effectiveness of the proposed method is verified with both synthetic and field measurements. The experimental results show that the proposed method can effectively improve noise removal performance under different detection scenarios.
Multi-Sensor Fusion for Activity Recognition—A Survey
Authors: Aguileta Antonio A. , Brena Ramon F. , Mayora Oscar , Molino-Minero-Re Erik , Trejo Luis A.
Published: September 2019

In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.
Dynamic Residual Dense Network for Image Denoising
Authors: Song Yuda , Zhu Yunfang , Du Xin
Published: September 2019

Deep convolutional neural networks have achieved great performance on various image restoration tasks. Specifically, the residual dense network (RDN) has achieved great results on image noise reduction by cascading multiple residual dense blocks (RDBs) to make full use of the hierarchical feature. However, the RDN only performs well in denoising on a single noise level, and the computational cost of the RDN increases significantly with the increase in the number of RDBs, and this only slightly improves the effect of denoising. To overcome this, we propose the dynamic residual dense network (DRDN), a dynamic network that can selectively skip some RDBs based on the noise amount of the input image. Moreover, the DRDN allows modifying the denoising strength to manually get the best outputs, which can make the network more effective for real-world denoising. Our proposed DRDN can perform better than the RDN and reduces the computational cost by 40 – 50 % . Furthermore, we surpass the state-of-the-art CBDNet by 1.34 dB on the real-world noise benchmark.
Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean
Authors: Cao Ran , Yang Kunde , Yang Qiulong , Chen Peng , Sun Quan , Xue Runze
Published: September 2019

Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.
Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models
Authors: He Xing , Ji Jun , Liu Kaixin , Gao Zengliang , Liu Yi
Published: September 2019

The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning method, the valuable information hidden in unlabeled data can be explored and absorbed into the prediction model. The application results to an industrial blast furnace show that BLSM has better prediction performance compared with other supervised soft sensors.
Gold and ZnO-Based Metal-Semiconductor Network for Highly Sensitive Room-Temperature Gas Sensing
Authors: Zhang Renyun , Hummelgård Magnus , Ljunggren Joel , Olin Håkan
Published: September 2019

Metal-semiconductor junctions and interfaces have been studied for many years due to their importance in applications such as semiconductor electronics and solar cells. However, semiconductor-metal networks are less studied because there is a lack of effective methods to fabricate such structures. Here, we report a novel Au–ZnO-based metal-semiconductor (M-S)n network in which ZnO nanowires were grown horizontally on gold particles and extended to reach the neighboring particles, forming an (M-S)n network. The (M-S)n network was further used as a gas sensor for sensing ethanol and acetone gases. The results show that the (M-S)n network is sensitive to ethanol (28.1 ppm) and acetone (22.3 ppm) gases and has the capacity to recognize the two gases based on differences in the saturation time. This study provides a method for producing a new type of metal-semiconductor network structure and demonstrates its application in gas sensing.
Structural Insight into the In Vitro Anti-Intravasative Properties of Flavonoids
Authors: Eichsteininger Julia , Kirisits Kerstin , Smöch Claudia , Stadlbauer Christa , Nguyen Chi Huu , Jäger Walter , Özmen Ali , Ecker Gerhard , Krupitza Georg , Krenn Liselotte
Published: September 2019

We investigated the effect of 21 flavonoids in a three-dimensional in vitro system for their ability to inhibit gap formation by MCF-7 breast cancer spheroids in monolayers of lymphendothelial cells. Different representatives of the classes of flavones, flavonols, and flavanones were tested in the circular chemorepellent-induced defects (CCID)-assay. Bay11-7082, a known inhibitor of CCID formation served as the positive control. This study provides the first comparison of the potential of flavonoids to suppress features influencing the intravasation of MCF-7 breast cancer cells aggregates through the lymph endothelial barrier. The most significant effects were seen after incubation with the flavones luteolin, chrysin, and apigenin. Additional hydroxylation or methoxylation in positions 6 or 8, as expected, resulted in decreased activity. The tested flavanones remained without or low efficacy.
Short Food Supply Chains and Their Contributions to Sustainability: Participants’ Views and Perceptions from 12 European Cases
Authors: Vittersø Gunnar , Torjusen Hanne , Laitala Kirsi , Tocco Barbara , Biasini Beatrice , Csillag Peter , de Labarre Matthieu Duboys , Lecoeur Jean-Loup , Maj Agnieszka , Majewski Edward , Malak-Rawlikowska Agata , Menozzi Davide , Török Áron , Wavresky Pierre
Published: September 2019

The present food system faces major challenges in terms of sustainable development along social, economic and environmental dimensions. These challenges are often associated with industrialised production processes and longer and less transparent distribution chains. Thus, closer distribution systems through Short Food Supply Chains (SFSCs) may be considered as a sustainable alternative. This study explores the role of different types of SFSCs and their contribution to sustainability through participants’ (consumers, retailers and producers) views and perceptions. As part of the European H2020 project “Strength2Food” we conducted a cross-case analysis and examined 12 European SFSC cases from six countries: France, Hungary, Italy, Norway, Poland and the UK. We applied a mixed method approach including primary data collection, via in-depth interviews and customer surveys, as well as desk research. The findings suggest that, irrespective of the type of SFSC, a strong agreement among the participants were found on the contribution of SFSCs to social sustainability. However, participants’ views considerably differ regarding the economic and environmental dimensions of sustainability. These differences relate to the way the SFSCs were organised and to some degrees to regional differences attributed to the significance of SFSC in different parts of Europe. The article concludes that the spatial heterogeneity of SFSCs, including supply chain actor differences, different types and organisational forms of SFSCs as well as regional and territorial characteristics, must be taken into account and further emphasised in future policies aimed at strengthening European food chain sustainability.
The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence
Authors: Demirdelen Tugce , Tekin Pırıl , Aksu Inayet Ozge , Ekinci Firat
Published: September 2019

In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters’ estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions.
Overcoming Barriers for Adopting Cleaner Production: A Case Study in Brazilian Small Metal-Mechanic Companies
Authors: Leite Roberto , Amorim Marlene , Rodrigues Mário , Oliveira Neto Geraldo
Published: September 2019

Small enterprises negatively affect the environment due to the inadequate disposal of manufacturing and raw material wastes generated in production processes; in addition, small enterprises do not often adopt adequate environmental practices due to barriers that include the lack of investment capacity. However, the adoption of cleaner production results in economic and environmental gains. The purpose of this study is to demonstrate how Brazilian small metal mechanical enterprises can identify and overcome barriers with the aim of implementing cleaner production. The research method used multiple cases with data collected by means of interviews and observations. The results allowed us to conclude that cultural and technical barriers negatively affect the adoption of cleaner production in the small enterprises studied. However, cultural and technical barriers can be overcome by means of economic and environmental gains as well as through investment in employee training and in the acquisition of more efficient machines and equipment.
Antecedents and Consequences of Brand Experiences in a Historical and Cultural Theme Park
Authors: Wang Junhui , Kim Jinwon , Kang Sanghoon
Published: September 2019

Sustainability has been a major research topic in the tourism and hospitality industry, and theme parks are no exception, particularly in terms of their economic sustainability. Yet few studies have specifically explored sustainability approaches for theme parks. This work examines structural relationships among theme park attributes and visitors’ brand experiences, perceived value, satisfaction, and behavioral intentions at a theme park. Data from 321 respondents, collected at a famous historical and cultural theme park in China, were incorporated into statistical analyses (e.g., structural equation modeling) in R software. Compared to peripheral attributes, core attributes comprised an antecedent that was closely and significantly related to brand experiences. Of the brand experiences construct consisting of sensory, affective, intellectual, and behavioral experiences, sensory and behavioral experiences significantly influenced the functional value of the perceived value and sensory and intellectual experiences significantly influenced the emotional value of the perceived value. The functional and emotional values each significantly affected satisfaction, which in turn significantly influenced behavioral intention. This study is the first to apply the brand experience concept in a theme park context. Theoretical and practical implications for achieving sustainable theme park growth and development are also provided.
Water Status and Yield Response to Deficit Irrigation and Fertilization of Three Olive Oil Cultivars under the Semi-Arid Conditions of Tunisia
Authors: Aïachi Mezghani Mouna , Mguidiche Amel , Allouche Khebour Faiza , Zouari Imen , Attia Faouzi , Provenzano Giuseppe
Published: September 2019

Sustainability of olive production is possible by adopting the modern techniques of irrigation and fertilization. In Tunisia, olive trees are usually cultivated in poor soils, under semi-arid conditions characterized by water scarcity. This study investigated the effects of different water supply and fertilization on leaf water status and crop yield of three different olive oil varieties cultivated in central Tunisia, during four experimental seasons (2014–2017). Three treatments were examined: trees conducted under rainfed conditions (TRF), which represented the control treatment, trees irrigated with 50% ETc (T50) and, finally, trees irrigated with 50% ETc and with additional fertilization (T50F). Leaf water content and potential, yield and water use efficiency have been monitored on three different varieties, Chetoui, Chemlali, and Koroneiki, which are quite typical in the considered region. For all the growing seasons, midday leaf water potentials were measured from April to September. Midday leaf water potentials (MLWP) were generally higher for the two irrigated treatments (T50 and T50F) than for non-irrigated trees (TRF). As the season proceeded, MLWPs tended to decrease during summer for all the treatments and varieties. The lowest values were observed for the non-irrigated trees, varying between −3.25 MPa to −4.75 MPa. Relative leaf water content followed the same trends of midday leaf water potentials. Chetoui showed the lowest yield, which did not exceed 1530 Kg/(ha year), even for irrigated and fertilized trees. On the other hand, the yields of Chemlali and Koroneiki, cumulated in the four years, reached the maximum value of about 20 tons/ha. For these two varieties, the cumulated yield obtained in the control treatment (TRF) resulted significantly lower than the corresponding of the other two treatments (T50 and T50F). The highest irrigation water use efficiency (WUE) was estimated for Chemlali (T50) and (TRF). WUE was equal to 1.22 Kg/m3 for Koroneiki under fertigated treatment (T50F). Application of the only water supply (50% ETc) or associated with fertilizer improved the tree water status and increased the productivity of Chemlali and Koroneiki varieties.
Roles of Psychological Resistance to Change Factors and Heterogeneity in Car Stickiness and Transit Loyalty in Mode Shift Behavior: A Hybrid Choice Approach
Authors: Gao Kun , Shao Minhua , Sun Lijun
Published: September 2019

To support the scientific policy making and planning for promoting the share rate of sustainable public transit in urban areas of large metropolises, this study analyzes the influences of psychological resistance to change factors on commuters’ mode shift behavior while some external changes happen in the transport supplies. The heterogeneities in the car users’ stickiness to car and the metro users’ loyalty to metro are examined to support individual-specific travel behavior prediction. Web-scripted efficient experimental stated preference surveys including four commuting modes and three key factors are generated, and face-to-face interviews are conducted to collect reliable behavioral data. A hybrid choice approach, simultaneously considering the latent variables and quantitative level-of-service variables of different options, is employed for analysis. The results indicate that psychological resistance to change factors (routine seeking, cognitive rigidity, and emotion reaction) have significant and substantial influences on car users’ inclination to previously used commuting mode (i.e., car) in mode shift behavior. Car users with stronger routine seeking, stronger cognitive rigidity, and less emotion reaction show more predilection to car. Car users’ income level, gender, marital status, commuting distance, commuting time, license type, and flexible work time are found to partially explain the heterogeneity in car stickiness. In-vehicle crowding of public transit is a much more crucial factor for attracting car users to shift to public transit as compared to cost and travel time. Metro users with stronger routine seeking and less emotion reaction present a stronger inclination to metro in mode shift behavior. The influences of psychological resistance to change factors on metro users’ mode shift behavior are comparatively smaller than the influences of these factors on car users’ behavior. Metro users’ age, education level, commuting distance, commuting time, occupation, and flexible work time are identified to be associated with predilections for metro.
The Zero-Energy Idea in Districts: Application of a Methodological Approach to a Case Study of Epinlieu (Mons)
Authors: Koutra Sesil , Pagnoule Claire , Galatoulas Nikolaos-Fivos , Bagheri Ali , Waroux Thomas , Becue Vincent , Ioakimidis Christos S.
Published: September 2019

Rapidly increasing global energy demand has raised concerns about the exhaustion of energy resources and the consequent heavy environmental impact. Improving energy efficiency in cities comprises an initial measure for addressing these phenomena. Within the current context of globalization, EU initiatives and policy targets have been proposed in order to revise urban development strategies and motivate its member states (MSes) toward “zero-energy objectives”. Providing a methodological approach with a simulation district analysis, the present article summarizes how this challenge was analyzed in an existing district in Belgium. This study contributes to the scientific discussion by analyzing the applicability of a holistic approach to zero-energy objectives on a larger scale.
The Rich Picture Method: A Simple Tool for Reflective Teaching and Learning about Sustainable Food Systems
Authors: Grant Michelle , Gilgen Anna K. , Buchmann Nina
Published: September 2019

The World Food System Summer School is an innovative two-week course that seeks to develop the knowledge, skills and attitudes of the next generation of decision makers to build sustainable food systems. Meaningful learning, where the participant is able to relate new information to existing knowledge, is a critical part of education about complex systems and requires the integration of reflective approaches to teaching and learning. We adapted the rich picture method in three summer schools in Switzerland, South Africa and Côte d’Ivoire (74 participants with 29 nationalities) to support the reflection of participants on their knowledge gained on complex food systems. Coding and comparing 51 pairs of pre- and post-course pictures of food systems clearly demonstrated newly gained knowledge: The number of sub-categories drawn significantly increased from 11 to 19 in the post-course pictures, the largest increase occurred for environmental sustainability (57%). The rich picture method is a highly valuable and simple tool to gain insight into how participants’ knowledge changes and where there are gaps in meeting the learning objectives. This is particularly useful within a highly diverse participant cohort, as it allows participants to discuss and reflect on their own learning experience in a personalized way. Additionally, the rich picture method provides insights for faculty to improve their approaches to teaching on food systems.
Quantifying Differences among Ten Fingers in Force Control Capabilities by a Modified Meyer Model
Authors: Peng Cong , Wang Dangxiao , Zhang Yuru
Published: September 2019

Quantifiable differences among fingers in force control capability have both important practical and theoretical values in characterizing force control of accurate finger-tapping tasks. Following the classical Fitts’ law paradigm, we quantified the differences among ten fingers in term of speed–accuracy trade-off (SAT) in performing repetitive discrete force control tasks. Visual cues displaying targeted force magnitudes and tolerances were provided. Users were required to apply the targeted force within the given tolerance quickly and accurately by pressing a force sensor using the specified finger. We found that ten fingers obeyed the Meyer model in the SAT performance and they differed in reaction time, the index of performance (IP), and the goodness of fit for the Meyer model. A modified Meyer model was proposed to quantify the difference between ten fingers in the SAT performance using only one parameter, making the quantification easier than using the original Meyer model. Pairwise comparisons showed that the differences between symmetrical fingers on both hands were insignificant except for the pair of index fingers. These findings provided us with multiple perspectives on the differentiation among ten fingers in the force control capabilities. Our study helps lay the foundation for engineering systems that rely on finger force control ability.
Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks
Authors: Jiang Yun , Zhang Hai , Tan Ning , Chen Li
Published: September 2019

Automated retinal vessel segmentation technology has become an important tool for disease screening and diagnosis in clinical medicine. However, most of the available methods of retinal vessel segmentation still have problems such as poor accuracy and low generalization ability. This is because the symmetrical and asymmetrical patterns between blood vessels are complicated, and the contrast between the vessel and the background is relatively low due to illumination and pathology. Robust vessel segmentation of the retinal image is essential for improving the diagnosis of diseases such as vein occlusions and diabetic retinopathy. Automated retinal vein segmentation remains a challenging task. In this paper, we proposed an automatic retinal vessel segmentation framework using deep fully convolutional neural networks (FCN), which integrate novel methods of data preprocessing, data augmentation, and full convolutional neural networks. It is an end-to-end framework that automatically and efficiently performs retinal vessel segmentation. The framework was evaluated on three publicly available standard datasets, achieving F1 score of 0.8321, 0.8531, and 0.8243, an average accuracy of 0.9706, 0.9777, and 0.9773, and average area under the Receiver Operating Characteristic (ROC) curve of 0.9880, 0.9923 and 0.9917 on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. The experimental results show that our proposed framework achieves state-of-the-art vessel segmentation performance in all three benchmark tests.
Effect of Stall Cells on Pressure Fluctuations Characteristics in a Centrifugal Pump
Authors: Zhou Peijian , Dai Jiacheng , Yan Chaoshou , Zheng Shuihua , Ye Changliang , Zhang Xiang
Published: September 2019

Rotating stall is an unsteady flow phenomenon, which always leads to instability and efficiency degradation. In order to reveal pressure fluctuations in the impeller of centrifugal pump induced by stall cells, the flow structures in a volute-type centrifugal pump were calculated using Large Eddy Simulation (LES) method. The predicted results of the numerical model were compared with experimental flow-head curve. The simulation results were in good agreement with the experimental results. The stall phenomenon occurred when the flow rate dropped to 70% of design flow rate. Three stall cells located at the entrance of passages could be observed, which remained stationary relative to the rotating impeller. With the decrease of flow rate, the area occupied by stall cells gradually increased. The peak value of pressure fluctuation at 25% of design flow rate is obviously larger than that at 50% of design flow rate. For the unstalled or stalled passage, the impeller-volute interaction played a leading role in the pressure fluctuations of the impeller. For the stalled passage, the amplitude of the low frequency induced by stall cell is relatively insignificant.
The Network of Protected Areas (NPA) as an Instrument to Implement Cross-Border Public Services
Authors: Coronato Maria , Prezioso Maria
Published: September 2019

Polycentric development, territorial cohesion, and territorial diversity are some of the main principles supported at the European level for reducing regional disparities and for making territories more resilient and diversified, which strengthens the competitiveness of Europe in the global economy. This research article, starting from the final results of the ESPON Linking Networks of Protected Areas to Territorial Development (LinkPAs) project, considers that the protected areas (PAs) are a territorial unit able to connect—in a polycentric approach—the different territorial aspects (economic, social, environmental) present in an area to implement cross-border public services (CPS) to share in a larger territorial context through a network of protected areas (NPA). Toward this aim, this paper suggests applying the NPA management model developed in the ESPON LinkPAs project to CPS to assess if a soft governance mechanism is able to efficiently and sustainably manage the CPS.
An Enhanced System with Macrophytes and Polyurethane Sponge as an Eco-Technology for Restoring Eutrophic Water: A Pilot Test
Authors: Wang Yijie , Yin Xinan , Cai Yanpeng , Yang Zhifeng
Published: September 2019

Water eutrophication is one of the most serious environmental problems in urban lakes and ponds due to the excessive nutrients. To deal with this problem, the development of methods for supporting ecological rehabilitation has been undertaken. Meanwhile, the trophic interactions during rehabilitation also have been analyzed. In this study, a new technique was employed to solve the water eutrophication problems in an urban pond. To evaluate the water eutrophication at a pilot scale, an enhanced artificial floating-type biological treatment system (FBTS) composed of a floating bed, macrophyte, artificial biofilm carrier (polyurethane sponge) and aerator could be used as equipment for urban pond remediation. In addition, FBTS was employed to decrease the total nitrogen (TN), ammonia-nitrogen (NH3-N), total phosphorus (TP) and chemical oxygen demand (COD) in water. Meanwhile, the changes of water qualities were monitored in the remediation process, and differences in phytoplankton functional group diversity were also registered. Cyanobacteria would decrease after the removal of P, and the diatom assemblage composition changed. The dominant species Cyanophyta were transformed to co-existed with Bacillariophyta, Pyrrophyta and Chlorophyta due to the improvement of water quality. Consequently, this new FBTS could be a promising eco-technology for the removal of nitrogen and phosphorus from eutrophic water, and even could promote the phytoplankton succession.
The Long-Term Effects of Land Use and Climate Changes on the Hydro-Morphology of the Reno River Catchment (Northern Italy)
Authors: Pavanelli Donatella , Cavazza Claudio , Lavrnić Stevo , Toscano Attilio
Published: September 2019

Anthropogenic activities, and in particular land use/land cover (LULC) changes, have a considerable effect on rivers’ flow rates and their morphologies. A representative example of those changes and resulting impacts on the fluvial environment is the Reno Mountain Basin (RMB), located in Northern Italy. Characterized by forest exploitation and agricultural production until World War II, today the RMB consists predominantly of meadows, forests and uncultivated land, as a result of agricultural land abandonment. This study focuses on the changes of the Reno river’s morphology since the 1950s, with an objective of analyzing the factors that caused and influenced those changes. The factors considered were LULC changes, the Reno river flow rate and suspended sediment yield, and local climate data (precipitation and temperature). It was concluded that LUCL changes caused some important modifications in the riparian corridor, riverbed size, and river flow rate. A 40–80% reduction in the river bed area was observed, vegetation developed in the riparian buffer strips, and the river channel changed from braided to a single channel. The main causes identified are reductions in the river flow rate and suspended sediment yield (−36% and −38%, respectively), while climate change did not have a significant effect.
Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach
Authors: Mihu-Pintilie Alin , Cîmpianu Cătălin Ioan , Stoleriu Cristian Constantin , Pérez Martín Núñez , Paveluc Larisa Elena
Published: September 2019

The ability to extract streamflow hydraulic settings using geoinformatic techniques, especially in high populated territories like urban and peri-urban areas, is an important aspect of any disaster management plan and flood mitigation effort. 1D and 2D hydraulic models, generated based on DEMs with high accuracy (e.g., Light Detection and Ranging (LiDAR)) and processed in geographic information systems (GIS) modeling software (e.g., HEC-RAS), can improve urban flood hazard maps. In this study, we present a small-scale conceptual approach using HEC-RAS multi-scenario methodology based on remote sensing (RS), LiDAR data, and 2D hydraulic modeling for the urban and peri-urban area of Bacău City (Bistriţa River, NE Romania). In order to test the flood mitigation capacity of Bacău 1 reservoir (rB1) and Bacău 2 reservoir (rB2), four 2D streamflow hydraulic scenarios (s1–s4) based on average discharge and calculated discharge (s1–s4) data for rB1 spillway gate (Sw1) and for its hydro-power plant (H-pp) were computed. Compared with the large-scale flood hazard data provided by regional authorities, the 2D HEC-RAS multi-scenario provided a more realistic perspective about the possible flood threats in the study area and has shown to be a valuable asset in the improvement process of the official flood hazard maps.
Inappropriate Testing for Clostridioides difficile in Long-Term Care: Implications Highlight the Need for an Algorithm
Authors: Vivian Katie , Jones Rebecca
Published: September 2019

This article provides clear guidance related to appropriate testing for Clostridioides difficile (C. diff) and identifies the negative implications of inappropriate testing, repeat testing, and testing for cure. Residents of long-term care (LTC) facilities are at increased risk for developing C. diff. Complications can arise if a resident does not have an active C. diff infection (colonization) and has a positive C. diff laboratory test result. The authors share a fictional bedside story illustrating the negative consequences that can result from inappropriate C. diff testing, and present a decision tree that promotes mindful application of testing, which may result in cost savings and prevent adverse resident outcomes.

Keywords: C. diff, long-term care, test, diarrhea, stool, algorithm, treatment, diagnosis
General practitioners’ perceptions of best practice care at the end of life: a qualitative study
Authors: Herrmann Anne , Carey Mariko , Zucca Alison , Boyd Lucy , Roberts Bernadette
Published: September 2019

Background

GPs can play a central role in palliative care delivery. However, little is known about their views on what constitutes best practice care at the end of life.

Aim

To explore, in a sample of Australian GPs, their perceptions of best practice palliative care and their ideal role in its delivery.

Design & setting

A qualitative interview study of 25 GPs practising in metropolitan and non-metropolitan locations in New South Wales, Australia.

Method

Semi-structured telephone interviews were conducted. Data were analysed using qualitative content analysis.

Results

Participants had a mean age of 51 years, and had practised between 3 and 38 years (mean 19 years). Best practice palliative care was perceived to be proactive and responsive to a wide range of patient and family needs. Many participants indicated a need for relational continuity, which involves GPs establishing a care pathway from diagnosis to palliation, coordinating care across the pathway, and collaborating with other healthcare providers. A number of participants perceived palliative care as a natural extension of primary care and indicated that best practice palliative care mainly requires experiential knowledge and good communication skills, rather than specialised medical knowledge. Participants listed a number of communication strategies to offer patients and their families choice and ongoing negotiation about the recommended treatments.

Conclusion

This study provides novel in-depth insights into GPs’ perceptions of best practice palliative care. Future research should further investigate the identified features of care, and whether they can maximise the outcomes of patients and their families.
PROGRAMA MAIS MÉDICOS: AVERSÃO E PRECONCEITO. UMA ANÁLISE NA PERSPECTIVA DO PENSAMENTO DE FRANTZ FANON
Authors: Ramos Rogério Macedo
Published: September 2019

O presente trabalho tem por objetivo discutir e analisar o ódio e aversão ao programa “Mais Médicos”, que foram difundidos pelos canais de comunicação brasileiro, revistas e jornais. Entende-se que esse discurso contribui para disseminar repulsa e ódio, e que faz presente nas várias classes sociais, principalmente, as médias, tanto atribuído ao programa, bem como aos cubanos. Esses sujeitos foram imputados estereótipos que acabam por marcar um processo de inferiorização devido a cor da pele, ou seja, por ser negro e também comunista. Para tanto, toma-se como referencial de análise a questão racial na perspectiva de pensamento de Frantz Fanon (2008), a categoria estabelecidos-outsiders de Norbert Elias e John Scoltson (2008), em relação a esse sujeito que chega em um espaço estabelecido e sofre discriminação. Isso atrelado à discussão no que se refere às categorias cunhadas por Jessé Souza (2009), tais como: classe média, trabalhadores e ralé brasileira, dando corpo ao trabalho, já que essas categorias contribuem para formar um universo social de privilégios e reprodução do discurso elitista. Palavras chave: Mais médicos, classe média, cubanos, ódio e inferiorização