bluuebunny/crossref_metadata_2025 huggingface dataset:
Usefulness of Global Root Zone Soil Moisture Product for Streamflow Prediction of Ungauged Basins
Authors: Choi Jeonghyeon , Won Jeongeun , Lee Okjeong , Kim Sangdan
Published: February 2021

Using modelling approaches to predict stream flow from ungauged basins requires new model calibration strategies and evaluation methods that are different from the existing ones. Soil moisture information plays an important role in hydrological applications in basins. Increased availability of remote sensing data presents a significant opportunity to obtain the predictive performance of hydrological models (especially in ungauged basins), but there is still a limit to applying remote sensing soil moisture data directly to models. The Soil Moisture Active Passive (SMAP) satellite mission provides global soil moisture data estimated by assimilating remotely sensed brightness temperature to a land surface model. This study investigates the potential of a hydrological model calibrated using only global root zone soil moisture based on satellite observation when attempting to predict stream flow in ungauged basins. This approach’s advantage is that it is particularly useful for stream flow prediction in ungauged basins since it does not require observed stream flow data to calibrate a model. The modelling experiments were carried out on upstream watersheds of two dams in South Korea with high-quality stream flow data. The resulting model outputs when calibrated using soil moisture data without observed stream flow data are particularly impressive when simulating monthly stream flows upstream of the dams, and daily stream flows also showed a satisfactory level of predictive performance. In particular, the model calibrated using soil moisture data for dry years showed better predictive performance than for wet years. The performance of the model calibrated using soil moisture data was significantly improved under low flow conditions compared to the traditional regionalization approach. Additionally, the overall stream flow was also predicted better. In addition, the uncertainty of the model calibrated using soil moisture was not much different from that of the model calibrated using observed stream flow data, and showed more robust outputs when compared to the traditional regionalization approach. These results prove that the application of the global soil moisture product for predicting stream flows in ungauged basins is promising.
Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model
Authors: Pan Yanding , Zhao Chen , Liu Zhaorong
Published: February 2021

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.
Assessing Summertime Primary Production Required in Changed Marine Environments in Upwelling Ecosystems Around the Taiwan Bank
Authors: Hsiao Po-Yuan , Shimada Teruhisa , Lan Kuo-Wei , Lee Ming-An , Liao Cheng-Hsin
Published: February 2021

The Taiwan Bank (TB) is located in the southern Taiwan Strait, where the marine environments are affected by South China Sea Warm Current and Kuroshio Branch Current in summer. The bottom water flows upward along the edge of the continental shelf, forming an upwelling region that is an essential high-productivity fishing ground. Using trophic dynamic theory, fishery resources can be converted into primary production required (PPR) by primary production, which indicates the environmental tolerance of marine ecosystems. This study calculated the PPR of benthic and pelagic species, sea surface temperature (SST), upwelling size, and net primary production (NPP) to analyze fishery resource structure and the spatial distribution of PPR in upwelling, non-upwelling, and thermal front (frontal) areas of the TB in summer. Pelagic species, predominated by those in the Scombridae, Carangidae families and Trachurus japonicus, accounted for 77% of PPR (67% of the total catch). The benthic species were dominated by Mene maculata and members of the Loliginidae family. The upwelling intensity was the strongest in June and weakest in August. Generalized additive models revealed that the benthic species PPR in frontal habitats had the highest deviance explained (28.5%). Moreover, frontal habitats were influenced by NPP, which was also the main factor affecting the PPR of benthic species in all three habitats. Pelagic species were affected by high NPP, as well as low SST and negative values of the multivariate El Niño–Southern Oscillation (ENSO) index in upwelling habitats (16.9%) and non-upwelling habitats (11.5%). The composition of pelagic species varied by habitat; this variation can be ascribed to impacts from the ENSO. No significant differences were noted in benthic species composition. Overall, pelagic species resources are susceptible to climate change, whereas benthic species are mostly insensitive to climatic factors and are more affected by NPP.
An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI
Authors: Zheng Yuanmao , Zhou Qiang , He Yuanrong , Wang Cuiping , Wang Xiaorong , Wang Haowei
Published: February 2021

Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development.
Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait
Authors: Dufour-Beauséjour Sophie , Bernier Monique , Simon Jérome , Homayouni Saeid , Gilbert Véronique , Gauthier Yves , Tuniq Juupi , Wendleder Anna , Roth Achim
Published: February 2021

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients.
Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang
Authors: Li Xiaohang , Ding Jianli , Liu Jie , Ge Xiangyu , Zhang Junyong
Published: February 2021

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.
Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status
Authors: Ansper-Toomsalu Ave , Alikas Krista , Nielsen Karina , Tuvikene Lea , Kangro Kersti
Published: February 2021

European countries are obligated to monitor and estimate ecological status of lakes under European Union Water Framework Directive (2000/60/EC) for sustainable lakes’ ecosystems in the future. In large and shallow lakes, physical, chemical, and biological water quality parameters are influenced by the high natural variability of water level, exceeding anthropogenic variability, and causing large uncertainty to the assessment of ecological status. Correction of metric values used for the assessment of ecological status for the effect of natural water level fluctuation reduces the signal-to-noise ratio in data and decreases the uncertainty of the status estimate. Here we have explored the potential to create synergy between optical and altimetry data for more accurate estimation of ecological status class of lakes. We have combined data from Sentinel-3 Synthetic Aperture Radar Altimeter and Cryosat-2 SAR Interferometric Radar Altimeter to derive water level estimations in order to apply corrections for chlorophyll a, phytoplankton biomass, and Secchi disc depth estimations from Sentinel-3 Ocean and Land Color Instrument data. Long-term in situ data was used to develop the methodology for the correction of water quality data for the effects of water level applicable on the satellite data. The study shows suitability and potential to combine optical and altimetry data to support in situ measurements and thereby support lake monitoring and management. Combination of two different types of satellite data from the continuous Copernicus program will advance the monitoring of lakes and improves the estimation of ecological status under European Union Water Framework Directive.
Frequency-Tunable Terahertz Plasmonic Structure Based on the Solid Immersed Method for Sensing
Authors: Sugaya Toshio , Kawano Yukio
Published: February 2021

Terahertz waves are located in the frequency band between radio waves and light, and they are being considered for various applications as a light source. Generally, the use of light requires focusing; however, when a terahertz wave is irradiated onto a small detector or a small measurement sample, its wavelength, which is much longer than that of visible light, causes problems. The diffraction limit may make it impossible to focus the terahertz light down to the desired range by using common lenses. The Bull’s Eye structure, which is a plasmonic structure, is a promising tool for focusing the terahertz light beyond the diffraction limit and into the sub-wavelength region. By utilizing the surface plasmon propagation, the electric field intensity and transmission coefficient can be enhanced. In this study, we improved the electric field intensity and light focusing in a small region by adapting the solid immersion method (SIM) from our previous study, which had a frequency-tunable nonconcentric Bull’s Eye structure. Through electromagnetic field analysis, the electric field intensity was confirmed to be approximately 20 times higher than that of the case without the SIM, and the transmission measurements confirmed that the transmission through an aperture had a gap of 1/20 that of the wavelength. This fabricated device can be used in imaging and sensing applications because of the close contact between the transmission aperture and the measurement sample.
Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan
Authors: Wei Chih-Chiang , Hsu Chen-Chia
Published: February 2021

This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.
An Energy-Efficient UWB Transmitter with Wireless Injection Locking for RF Energy-Harvesting Sensors
Authors: Kim Jun-Tae , Heo Bo-Ram , Kwon Ickjin
Published: February 2021

An ultralow-power ultrawideband (UWB) transmitter with an energy-efficient injection-locked radio frequency (RF) clock harvester that generates a carrier from an RF signal is proposed for RF energy-harvesting Internet-of-Things (IoT) sensor applications. The energy-efficient RF clock harvester based on the injection-locked ring oscillator (ILRO) is proposed to achieve optimal locking range and minimum input sensitivity to obtain an injection-locked 450 MHz clock in ultralow-power operation. A current-starved inverter-based delay stage is adopted that allows delay adjustment by bias voltage to minimize dynamic current consumption while maintaining a constant delay regardless of changes in process, supply voltage, and temperature (PVT). To minimize static current consumption, a UWB transmitter based on a digital-based UWB pulse generator and a pulse-driven switching drive amplifier is proposed. The proposed injection-locked RF clock harvester achieves the best RF input sensitivity of −34 dBm at a power consumption of 2.03 μW, enabling energy-efficient clock harvesting from low RF input power. In ultralow-power operation, a 23.8% locking range is achieved at the RF injection power of −15 dBm to cope with frequency changes due to PVT variations. The proposed UWB transmitter with RF clock harvester achieves the lowest energy consumption per pulse with an average power consumption of 97.03 μW and an energy consumption of 19.41 pJ/pulse, enabling operation with the energy available in RF energy-harvesting applications.
Monitoring Contractile Cardiomyocytes via Impedance Using Multipurpose Thin Film Ruthenium Oxide Electrodes
Authors: Tanumihardja Esther , de Bruijn Douwe S. , Slaats Rolf H. , Olthuis Wouter , van den Berg Albert
Published: February 2021

A ruthenium oxide (RuOx) electrode was used to monitor contractile events of human pluripotent stem cells-derived cardiomyocytes (hPSC-CMs) through electrical impedance spectroscopy (EIS). Using RuOx electrodes presents an advantage over standard thin film Pt electrodes because the RuOx electrodes can also be used as electrochemical sensor for pH, O2, and nitric oxide, providing multisensory functionality with the same electrode. First, the EIS signal was validated in an optically transparent well-plate setup using Pt wire electrodes. This way, visual data could be recorded simultaneously. Frequency analyses of both EIS and the visual data revealed almost identical frequency components. This suggests both the EIS and visual data captured the similar events of the beating of (an area of) hPSC-CMs. Similar EIS measurement was then performed using the RuOx electrode, which yielded comparable signal and periodicity. This mode of operation adds to the versatility of the RuOx electrode’s use in in vitro studies.
Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net
Authors: Li Yung-Hui , Putri Wenny Ramadha , Aslam Muhammad Saqlain , Chang Ching-Chun
Published: February 2021

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.
Some Contributions for Antenna 3D Far Field Characterization at Terahertz
Authors: Le Coq Laurent , Mézières Nicolas , Leroy Paul , Fuchs Benjamin
Published: February 2021

The three-dimensional (3D) characterization of antenna far field patterns at terahertz frequencies is addressed. This task is challenging, because the phase of the electric field is difficult to measure accurately and reliably. From the sub-millimeter wave range, the small wavelength indeed significantly increases the impact of mechanical and electrical errors. Models and procedures to estimate these errors are proposed to mitigate their effects. The 3D far field patterns of a circularly polarized horn measured at 300 GHz and a multibeam pillbox antenna at 270 GHz are shown. The agreement between the 3D measurements and the two-dimensional (2D) patterns of reference as well as the radiated pattern before and after correction demonstrates the interest of the proposed approach and experimentally validate the proposed error estimation procedures. The methodology can be applied to direct far field measurement facilities as well as compact antenna test ranges.
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution
Authors: Safarov Sirojbek , Whangbo Taeg Keun
Published: February 2021

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automatic colorectal polyp segmentation and detection in colonoscopy videos help endoscopists to identify colorectal disease more easily, making it a promising method to prevent colon cancer. In this study, we developed a fully automated pixel-wise polyp segmentation model named A-DenseUNet. The proposed architecture adapts different datasets, adjusting for the unknown depth of the network by sharing multiscale encoding information to the different levels of the decoder side. We also used multiple dilated convolutions with various atrous rates to observe a large field of view without increasing the computational cost and prevent loss of spatial information, which would cause dimensionality reduction. We utilized an attention mechanism to remove noise and inappropriate information, leading to the comprehensive re-establishment of contextual features. Our experiments demonstrated that the proposed architecture achieved significant segmentation results on public datasets. A-DenseUNet achieved a 90% Dice coefficient score on the Kvasir-SEG dataset and a 91% Dice coefficient score on the CVC-612 dataset, both of which were higher than the scores of other deep learning models such as UNet++, ResUNet, U-Net, PraNet, and ResUNet++ for segmenting polyps in colonoscopy images.
Fast Approximation for Sparse Coding with Applications to Object Recognition
Authors: Sun Zhenzhen , Yu Yuanlong
Published: February 2021

Sparse Coding (SC) has been widely studied and shown its superiority in the fields of signal processing, statistics, and machine learning. However, due to the high computational cost of the optimization algorithms required to compute the sparse feature, the applicability of SC to real-time object recognition tasks is limited. Many deep neural networks have been constructed to low fast estimate the sparse feature with the help of a large number of training samples, which is not suitable for small-scale datasets. Therefore, this work presents a simple and efficient fast approximation method for SC, in which a special single-hidden-layer neural network (SLNNs) is constructed to perform the approximation task, and the optimal sparse features of training samples exactly computed by sparse coding algorithm are used as ground truth to train the SLNNs. After training, the proposed SLNNs can quickly estimate sparse features for testing samples. Ten benchmark data sets taken from UCI databases and two face image datasets are used for experiment, and the low root mean square error (RMSE) results between the approximated sparse features and the optimal ones have verified the approximation performance of this proposed method. Furthermore, the recognition results demonstrate that the proposed method can effectively reduce the computational time of testing process while maintaining the recognition performance, and outperforms several state-of-the-art fast approximation sparse coding methods, as well as the exact sparse coding algorithms.
Building Institutional Capacity to Plan for Climate Neutrality: The Role of Local Co-Operation and Inter-Municipal Networks at the Regional Level
Authors: Wretling Vincent , Balfors Berit
Published: February 2021

The institutional capacity of local authorities to integrate climate change mitigation aspects into spatial planning is of vital importance. This paper sets out to investigate this capacity in the county of Stockholm, Sweden, by surveying 26 municipalities and analysing two case study municipalities through participation in workshops and interviews. The study shows the need both for a local energy and climate policy domain that can be integrated into spatial planning as well as dedicated personnel to maintain this policy, thereby securing knowledge resources within the organisation. Additionally, the importance of relations to and repertoires for interacting with politicians is highlighted. Lastly, the study demonstrates the complementary added value of a regional-local network administered by the County Administrative Board, spreading learning examples and diffusing theoretical knowledge, as well as sub-regional, inter-municipal networks that enable the transfer of more practical knowledge and provide a platform for concrete action.
Combined Pretreatment by Ultrasound and Struvite Precipitation of Raw Substrates: A Strategy to Overcome C/N Ratio Unbalance in Nitrogen-Rich Anaerobic Co-Digestion Systems
Authors: Coura Renata D’arc , Rodrigues Ana Cristina , Alonso Joaquim Mamede , Ferraz Ana Isabel , Brito Luis Miguel , Abrantes João Carlos Castro , Brito António Guerreiro
Published: February 2021

The present study aimed to optimize the struvite chemical precipitation process in nitrogen-rich anaerobic co-digestion systems. Struvite precipitation experiments were carried out using a mixture of cattle slurry liquid fraction and sewage sludge, with and without ultrasound pretreatment. Marine salt or MgCl2 were used as magnesium source in NH4+:Mg2+ stoichiometric proportions of 1:1.5 and 1:3. Under the tested conditions, ammonium nitrogen and orthophosphate were removed from the mixed liquor with a maximum observed efficiency of 43% and 92%, respectively, when the ultrasound treatment was applied prior to struvite precipitation, using MgCl2 as source of magnesium (NH4+:Mg2+ of 1:3). The operating time was 40 min. Different pretreatments were tested prior to the biomethanization experiments, struvite precipitation, ultrasound and a combination of both pretreatments. The application of ultrasound (with an energy input of 218 kJ L−1) and struvite precipitation (NH4+:Mg2+ of 1:3) increased the methane content in the biogas by 82% and reduced hydraulic retention time by 28%, when compared to the anaerobic co-digestion assays without pretreatment. The hydrolytic pretreatment increased the bioavailability of nitrogen by 5%, thus enhancing the removal efficiency of ammonium nitrogen by 20%. Consequently, an increase in the carbon to nitrogen ratio was observed, favoring the methanogenesis process.
Investigating Sustainable Commuting Patterns by Socio-Economic Factors
Authors: Jang Woo , Yuan Fei , Lopez Jose Javier
Published: February 2021

This research aims to analyze how modes of transportation differ according to socio-economic factors in an urban space. The study area is Ramsey County, the most densely populated county in Minnesota. The primary data used were from the recent 2012–2016 Census Transportation Planning Products (CTPP). We performed regression models to identify the relationship between mode of transport and socio-economic variables, and further analyzed disaggregate trip data to provide a more realistic evaluation of commuting patterns by use of multiple variables in combination. The research found that sustainable commuting patterns correlated significantly with both poverty and minority group status, but bore no significant relationship to older workers. Additionally, there was a significant correlation between commuting alone by car with both minority group status and older workers, but not with poverty. This research also confirmed that the sustainable commuting patterns of the working poor were mostly located in the downtown area, while causes of low-income workers driving alone typically involved much longer commutes to and from points throughout the study area, suggesting that more efficient commutes are a significant quality of life factor for the urban poor when evaluating residential and employment opportunities in the central city.
Suburban Fertility and Metropolitan Cycles: Insights from European Cities
Authors: Rodrigo-Comino Jesús , Egidi Gianluca , Sateriano Adele , Poponi Stefano , Mosconi Enrico Maria , Gimenez Morera Antonio
Published: February 2021

Being largely diversified along the urban–rural gradient, fertility gaps have demonstrated to fuel metropolitan expansion, contributing to natural population growth and social change. In this direction, population dynamics and economic transformations have continuously shaped urban cycles in Europe. Assuming suburban fertility to be a relevant engine of metropolitan growth, the present study investigates and discusses the intrinsic relationship between fertility transitions and urban expansion, focusing on European metropolitan regions. An average crude birth rate referring to the last decade (2013–2018) was estimated from official statistics at 671 Functional Urban Areas (FUAs, Eurostat Urban Audit definition) of 30 European countries, distinguishing ‘central cities’ from ‘suburban’ locations. Local contexts with a higher crude birth rate as compared with neighboring settlements were identified analyzing differential fertility levels in urban and suburban locations. By providing an indirect, comparative verification of the ‘suburban fertility hypothesis’ in European cities, the results of this study demonstrate how suburbanization has been basically associated to younger and larger families—and thus higher fertility levels—only in Eastern and Southern Europe. Birth rates that were higher in suburbs than in central cities were observed in 70% of Eastern European cities and 55% of Mediterranean cities. The reverse pattern was observed in Western (20%), Northern (25%) and Central (30%) Europe, suggesting that urban cycles in the European continent are not completely phased: most of Western, Central, and Northern European cities are experiencing re-urbanization after a long suburbanization wave. Demographic indicators are demonstrated to comprehensively delineate settlement patterns and socioeconomic trends along urban–suburban–rural gradients, giving insights on the differential metropolitan cycles between (and within) countries.
Techno-Economic Investigation of Wind Energy Potential in Selected Sites with Uncertainty Factors
Authors: Balaguru Varadharajan Sankaralingam Sriraja , Swaroopan Nesamony Jothi , Raju Kannadasan , Alsharif Mohammed H. , Kim Mun-Kyeom
Published: February 2021

This work demonstrates a techno-economical assessment of wind energy potential for four passes of Tamil Nadu (Aralvaimozhi, Shencottah, Palghat, and Cumbum) with uncertainty factors. First, a potential assessment was carried out with time-series data, and the Weibull parameters, such as c (scale) and k (shape), were determined using the modern-era retrospective analysis for research and applications (MEERA) data set. Using these parameters, the mean speed, most probable speed, power density, maximum energy-carrying speed of wind power were determined. From the analysis, it was observed that all four passes had better wind parameters; notably, the Aralvaimozhi pass attained a better range of about 6.563 m/s (mean wind speed), 226 W/m2 (wind power density), 6.403 m/s (most probable wind speed), and 8.699 m/s (max wind speed). Further, uncertainty factors, such as the probability of exceedance (PoE), wind shear co-efficient (WSC), surface roughness, and wake loss effect (WLE), were evaluated. The value of PoE was found to be within the bound for all the locations, i.e., below 15%. In addition, the ranged of WSC showed a good trend between 0.05 and 0.5. Moreover, the surface length of the passes was evaluated and recorded to be 0.0024 m with a 73% energy index. Further, output power, annual energy production (AEP), capacity factor (CF), and cost of wind energy of all four passes were computed using different wind turbine ratings in two cases, i.e., with and without WLE. It was observed that there was a huge profit in loss from all the four locations due to WLE that was estimated to be Rupees (Rs.) 10.07 crores without considering interest components and Rs. 13.66 crores with interest component at a 10% annual rate of interest.
Unpacking Plastic: Investigating Plastic Related Ambivalence
Authors: Hahn Lena , Buttlar Benjamin , Walther Eva
Published: February 2021

Many people are aware of the negative consequences of plastic use on the environment. Nevertheless, they use plastic due to its functionality. In the present paper, we hypothesized that this leads to the experience of ambivalence—the simultaneous existence of positive and negative evaluations of plastic. In two studies, we found that participants showed greater ambivalence toward plastic packed food than unpacked food. Moreover, they rated plastic packed food less favorably than unpacked food in response evaluations. In Study 2, we tested whether one-sided (only positive vs. only negative) information interventions could effectively influence ambivalence. Results showed that ambivalence is resistant to (social) influence. Directions for future research were discussed.
Analysis of Product-Country Image from Consumer’s Perspective: The Impact of Subjective Knowledge, Perceived Risk and Media Influence
Authors: Serrano-Arcos María del Mar , Sánchez-Fernández Raquel , Pérez-Mesa Juan Carlos
Published: February 2021

Limited research has empirically explored the antecedents that explain product-country image in the context of international marketing. This topic is particularly important as consumers’ concerns about sustainability and the country of origin are relevant factors influencing purchase decisions relating to foreign products. This study addresses the gap by developing a causal relationship model to explain which are the main determinants of how consumers perceive product-country image on the basis of key aspects that define sustainability (environmental, social, quality/safety, and economic factors). This research is focused on the Spanish horticultural sector, Europe’s leading fresh produce supplier, which has historically experienced major crises that have affected its image abroad. The model is tested by using PLS-SEM on a sample of 303 consumers in Germany, one of the main destination markets of Spanish horticultural products. The results indicate that consumers’ subjective knowledge about image crises of the country and its products/services and consumers’ perceived risk of these products and information sources (organic and induced image) influence the formation of product-country image. The results have implications in the design of marketing campaigns and in the improvement of sustainable strategies for international companies.
Monetizing the IoT Revolution
Authors: Donner Herman , Steep Michael
Published: February 2021

Academics and businesses alike tend to fail at understanding how the IoT revolution is monetized. We outline three main categories of how IoT will impact business models: (a) improved customer matching and tracking of marketing returns, (b) individualized offers and pricing when consumer demand and price elasticities can be identified, and (c) smart device and usage monitoring that allows for outcome-based contracts and servitization. Data convergence creates context-based-intelligence, which enables a shift from using consumer profiles for targeted advertising to individualized offers and pricing. The required depth of both consumer data and understanding of context will require collaborative efforts between companies and blur the lines between industrial- and consumer-IoT applications. Outlining concerns for privacy and cybersecurity, we find that consumer demand for decision-simplicity and relevant content aligns with the business model of “free” services in return for data, despite consumer concerns relating to data collection.
Multi-Criteria Performance Assessment for Semi-Transparent Photovoltaic Windows in Different Climate Contexts
Authors: Setyantho Gigih , Park Hansaem , Chang Seongju
Published: February 2021

Semi-transparent photovoltaic (STPV) windows, one of the building façade elements, can generate electricity and provide a certain amount of daylight for occupants. Nevertheless, expensive cost and unsatisfying indoor daylight performance in the room are common problems with STPV windows. This study investigates the thermal, daylight, energy, and life-cycle cost performance of STPV windows by considering varied window-to-wall ratios, building orientations, and STPV module types. The electricity balance index (elBI) indicator is proposed as one of the performance evaluation criteria. Two types of building models are established for this study: a rig-test building as the baseline building model and a KAIST campus research facility as the test building model along with the actual measurements and simulations using DesignBuilder. Results show that the STPV window in the Mediterranean climate demonstrates higher efficiency based on the elBI indicator. Decision-making analysis using the analytic hierarchy process and PROMETHEE II found weighting rates of 0.309, 0.076, and 0.465 for elBI, comfort, and cost criteria, respectively. Furthermore, lighting energy consumption becomes a critical variable for STPV module type selection, while a simple ON/OFF lighting control system can improve the elBI value by 0.02 ~ 0.04. Our research findings could potentially improve the decision-making process for building and urban energy systems selection in different climate types.
Methodology for Calculating the European Innovation Scoreboard—Proposition for Modification
Authors: Bielińska-Dusza Edyta , Hamerska Monika
Published: February 2021

The primary purpose of this article is to identify determinants affecting the Summary Innovation Index and, consequently, the positions of countries on the European Innovation Scoreboard (EIS). Then, based on the identified determinants, these countries are ranked using the linear ordering method. This article presents the concept of innovation as an unwavering subject of interest for researchers from around the world. Issues relating to measuring innovation, which is necessary for the efficient management of an organization, as well as to the study of innovation in individual countries, are discussed. Special attention is drawn to the methodological aspects of constructing the European Innovation Scoreboard (EIS). The identification of determinants affecting the level of the Summary Innovation Index is performed through stepwise regression. This makes it possible to reduce the number of factors utilized in the EIS ranking procedure from 27 to 22. This was the inspiration to apply an innovative approach and use the linear ordering method, in order to show that it is possible to obtain a ranking that is very similar to the EIS ranking with a reduced number of indicators. These results may be significant, both for units developing this type of ranking and for users, such as decision-makers, using the results to make strategic decisions. In our opinion, this innovative approach—that is, using the linear ordering method and a reduced number of indicators—makes it possible to create a more transparent EIS ranking. This article is of theoretical, methodological, and empirical interest.
Design Opportunities to Reduce Waste in Operating Rooms
Authors: Harding Charlotte , Van Loon Joren , Moons Ingrid , De Win Gunter , Du Bois Els
Published: February 2021

While taking care of the population’s health, hospitals generate mountains of waste, which in turn causes a hazard to the environment of the population. The operating room is responsible for a disproportionately big amount of hospital waste. This research aims to investigate waste creation in the operating room in order to identify design opportunities to support waste reduction according to the circular economy. Eight observations and five expert interviews were conducted in a large sized hospital. The hospital’s waste infrastructure, management, and sterilization department were mapped out. Findings are that washable towels and operation instruments are reused; paper, cardboard, and specific fabric are being recycled; and (non-)hazardous medical waste is being incinerated. Observation results and literature findings are largely comparable, stating that covering sheets of the operation bed, sterile clothing, sterile packaging, and department-specific products are as well the most used and discarded. The research also identified two waste hotspots: the logistical packaging (tertiary, secondary, and primary) of products and incorrect sorting between hazardous and non-hazardous medical waste. Design opportunities include optimization of recycling and increased use of reusables. Reuse is the preferred method, more specifically by exploring the possibilities of reuse of textiles, consumables, and packaging.
Hydrogen Fuel Cell Legal Framework in the United States, Germany, and South Korea—A Model for a Regulation in Malaysia
Authors: Azni Muhammad Asyraf , Md Khalid Rasyikah
Published: February 2021

As a party to the United Nation Framework Convention on Climate Change (UNFCCC), Malaysia is committed to reduce its greenhouse gases (GHG) emission intensity of gross domestic product (GDP) by 45% by 2030 relative to the emission intensity of GDP in 2005. One of the ways for Malaysia to reduce its GHG emission is to diversify its energy mix and to include hydrogen fuel cell (HFC) in its energy mix. Since Malaysia does not have any legal framework for HFCs, it is best to see how other countries are doing and how can it be replicated in Malaysia. This paper reviews the HFC legal framework in the United States, Germany and South Korea as these countries are among those that have advanced themselves in this technology. The researchers conducted a library-based research and obtained the related materials from online databases and public domain. Based on the reviews, the researchers find that these countries have a proper legal framework in place for HFC. With these legal frameworks, funds will be available to support research and development, as well as demonstration of HFC. Thus, it is recommended that Malaysia to have a proper HFC legal framework in place in order to support the development of the HFC industry.
The Agroecological Approach as a Model for Multifunctional Agriculture and Farming towards the European Green Deal 2030—Some Evidence from the Italian Experience
Authors: Gargano Giuseppe , Licciardo Francesco , Verrascina Milena , Zanetti Barbara
Published: February 2021

Agroecology can be considered an approach to farming and food systems which integrates the use of ecological principles and biological cycles methods of the traditional systems to the design and management of sustainable agriculture. Despite some differences mainly due to specific national developments on the topic, today there is a substantial convergence in the belief that the term agroecology reconciles three dimensions: scientific discipline, social movement, cultural practice. Beyond the deep-rooted French experience, at the level of the European Union (EU) there is no clear strategy for agroecological practices and action plans. In the Italian case, a first step forward, especially in terms of initiatives, was taken following the universal exhibition “EXPO 2015 Milan” while with respect to the promotion of agroecological practices, some positive feedback is ascribable to the experience of the Bio-districts. The purpose of this study is to investigate whether the model adopted by the Italian multifunctional farms can be considered precursory with respect to the approach that, from a theoretical point of view, identifies agroecology. The Italian system on farms diversification would seem to show it has somehow anticipated the new European Green Deal strategy for the next ten years due to the simultaneous presence of key elements that concern not only agricultural practice but also ethical and social aspects by involving farmers and communities that insist on the territory. Green Deal and Farm to Fork strategies recognize a new and important role for the agriculture and agri-food sectors and to invite farmers to engage consumers’ interest. The study also considers some characteristics of the farmers, such as the level of education and the economic sector in which they were employed before devoting themselves to the agritourism business but also the ability to develop multi-actor and multi-level networks. The analysis of these determinants would allow them to place different professional skills at the service of the primary sector that tend to positively influence the organization and farm performance. At the same time, the farmers’ skills are influenced by different sectors of activity such as participation in research projects or even in local initiatives with public and private actors. This allows the agroecological approach to be put into practice in a conscious (or unconscious) way.