bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of UAV LiDAR for Mapping Coastal Environments
Authors: Lin Yi-Chun , Cheng Yi-Ting , Zhou Tian , Ravi Radhika , Hasheminasab Seyyed , Flatt John , Troy Cary , Habib Ayman
Published: December 2019

Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively.
Classification of Anomalous Pixels in the Focal Plane Arrays of Orbiting Carbon Observatory-2 and -3 via Machine Learning
Authors: Marchetti Yuliya , Rosenberg Robert , Crisp David
Published: December 2019

A machine learning approach was developed to improve the bad pixel maps that mask damaged or unusable pixels in the imaging spectrometers of National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3). The OCO-2 and OCO-3 instruments use nearly 500,000 pixels to record high resolution spectra in three infrared wavelength ranges. These spectra are analyzed to retrieve estimates of the column-average carbon dioxide (XCO 2) concentration in Earth’s atmosphere. To meet mission requirements, these XCO 2 estimates must have accuracies exceeding 0.25%, and small uncertainties in the bias or gain of even one detector pixel can add significant error to the retrieved XCO 2 estimates. Thus, anomalous pixels are identified and removed from the data stream by applying a bad pixel map prior to further processing. To develop these maps, we first characterize each pixel’s behavior through a collection of interpretable and statistically well-defined metrics. These features and a prior map are then used as inputs in a Random Forest classifier to assign a likelihood that a given pixel is bad. Consequently, the likelihoods are analyzed and thresholds are chosen to produce a new bad pixel map. The machine learning approach adopted here has improved data quality by identifying hundreds of new bad pixels in each detector. Such an approach can be generalized to other instruments that require independent calibration of many individual elements.
Quantifying Drought Sensitivity of Mediterranean Climate Vegetation to Recent Warming: A Case Study in Southern California
Authors: Dong Chunyu , MacDonald Glen , Okin Gregory S. , Gillespie Thomas W.
Published: December 2019

A combination of drought and high temperatures (“global-change-type drought”) is projected to become increasingly common in Mediterranean climate regions. Recently, Southern California has experienced record-breaking high temperatures coupled with significant precipitation deficits, which provides opportunities to investigate the impacts of high temperatures on the drought sensitivity of Mediterranean climate vegetation. Responses of different vegetation types to drought are quantified using the Moderate Resolution Imaging Spectroradiometer (MODIS) data for the period 2000–2017. The contrasting responses of the vegetation types to drought are captured by the correlation and regression coefficients between Normalized Difference Vegetation Index (NDVI) anomalies and the Palmer Drought Severity Index (PDSI). A novel bootstrapping regression approach is used to decompose the relationships between the vegetation sensitivity (NDVI–PDSI regression slopes) and the principle climate factors (temperature and precipitation) associated with the drought. Significantly increased sensitivity to drought in warmer locations indicates the important role of temperature in exacerbating vulnerability; however, spatial precipitation variations do not demonstrate significant effects in modulating drought sensitivity. Based on annual NDVI response, chaparral is the most vulnerable community to warming, which will probably be severely affected by hotter droughts in the future. Drought sensitivity of coastal sage scrub (CSS) is also shown to be very responsive to warming in fall and winter. Grassland and developed land will likely be less affected by this warming. The sensitivity of the overall vegetation to temperature increases is particularly concerning, as it is the variable that has had the strongest secular trend in recent decades, which is expected to continue or strengthen in the future. Increased temperatures will probably alter vegetation distribution, as well as possibly increase annual grassland cover, and decrease the extent and ecological services provided by perennial woody Mediterranean climate ecosystems as well.
Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification
Authors: Bazine Razika , Wu Huayi , Boukhechba Kamel
Published: December 2019

In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.
Analysis of Spatial and Temporal Variability in Libya-4 with Landsat 8 and Sentinel-2 Data for Optimized Ground Target Location
Authors: Rodrigo Juan Fernando , Gil Jorge , Salvador Pablo , Gómez Diego , Sanz Julia , Casanova Jose Luis
Published: December 2019

Pseudo-Invariant Calibration Sites (PICS) have been widely used by the remote sensing community in recent decades for post-launch absolute calibration, cross-calibration, and the monitoring of radiometric stability. The Committee on Earth Observation Satellites (CEOS) has established several official PICS for these purposes. Of these, Libya-4 is the most commonly used, due to its high uniformity and stability. The site was chosen as a large-area site for medium resolution sensors, and with high-resolution sensors now common, smaller sites are being identified. This work has identified an improved area of interest (AOI) within Libya-4 by using combined Landsat 8 and Sentinel 2 data. The Optimized Ground Target (OGT) was determined by calculating the coefficient of variation along with the use of a quasi-Newton optimization algorithm combined with the Basin–Hopping global optimization technique to constrain a search area small enough to perform a final brute-force refinement. The Coefficient of Variation CV of the proposed OGT is significantly lower than that in the original CEOS area, with differences between the CV of both zones in the order of 1% in the visible near-infrared (VNIR) bands. This new AOI has the potential to improve the cross-calibration between high-resolution sensors using the PICS methodology through an OGT with more homogeneous and stable characteristics.
Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network
Authors: Liu Wei , Yang MengYuan , Xie Meng , Guo Zihui , Li ErZhu , Zhang Lianpeng , Pei Tao , Wang Dong
Published: December 2019

Accurate extraction of buildings using high spatial resolution imagery is essential to a wide range of urban applications. However, it is difficult to extract semantic features from a variety of complex scenes (e.g., suburban, urban and urban village areas) because various complex man-made objects usually appear heterogeneous with large intra-class and low inter-class variations. The automatic extraction of buildings is thus extremely challenging. The fully convolutional neural networks (FCNs) developed in recent years have performed well in the extraction of urban man-made objects due to their ability to learn state-of-the-art features and to label pixels end-to-end. One of the most successful FCNs used in building extraction is U-net. However, the commonly used skip connection and feature fusion refinement modules in U-net often ignore the problem of feature selection, and the ability to extract smaller buildings and refine building boundaries needs to be improved. In this paper, we propose a trainable chain fully convolutional neural network (CFCN), which fuses high spatial resolution unmanned aerial vehicle (UAV) images and the digital surface model (DSM) for building extraction. Multilevel features are obtained from the fusion data, and an improved U-net is used for the coarse extraction of the building. To solve the problem of incomplete extraction of building boundaries, a U-net network is introduced by chain, which is used for the introduction of a coarse building boundary constraint, hole filling, and "speckle" removal. Typical areas such as suburban, urban, and urban villages were selected for building extraction experiments. The results show that the CFCN achieved recall of 98.67%, 98.62%, and 99.52% and intersection over union (IoU) of 96.23%, 96.43%, and 95.76% in suburban, urban, and urban village areas, respectively. Considering the IoU in conjunction with the CFCN and U-net resulted in improvements of 6.61%, 5.31%, and 6.45% in suburban, urban, and urban village areas, respectively. The proposed method can extract buildings with higher accuracy and with clearer and more complete boundaries.
Editorial for Special Issue “Advances in Satellite Altimetry and Its Application”
Authors: Dettmering Denise , Passaro Marcello , Braun Alexander
Published: December 2019

This special issue compiles studies from different disciplines presenting recent advances in the field of radar and laser altimetry including new and future altimetry missions and their applications. It comprises eight research papers as well as one review paper, and covers method development as well as applications, which target diverse Earth systems (oceans, coastal regions, sea-ice, inland) as well as the Moon.
Error Analysis and Modeling for an Absolute Capacitive Displacement Measuring System with High Accuracy and Long Range
Authors: Zhang Dongdong , Lin Li , Zheng Quanshui
Published: December 2019

We proposed a novel kind of absolute capacitive grating displacement measuring system with both high accuracy and long range in a previous article. The measuring system includes both a MOVER and a STATOR, the contact surfaces of which are coated by a thin layer of dielectric film with a low friction coefficient and high hardness. The measuring system works in contact mode to minimize the gap changes. This paper presents a theoretical analysis of the influence of some factors, including fabrication errors, installation errors, and environment disturbance, on measurement signals. The measuring signal model was modified according to the analysis. The signal processing methods were investigated to improve the signal sensitivity and signal-to-noise ratio (SNR). The displacement calculation model shows that the design of orthogonal signals can solve the dead-zone problem. Absolute displacement was obtained by a simple method using two coarse signals and highly accurate displacement was further obtained while using two fine signals with the help of absolute information. According to the displacement calculation model and error analysis, the error in fine calculation functions mainly determines the model’s accuracy and is locally affected by coarse calculation functions. It was also determined that amplitude differences, non-orthogonality, and signal offsets are not related to the accuracy of the displacement calculation model. The experiments were carried out to confirm the abovementioned theoretical analysis. The experimental results show that the displacement resolution and error in the displacement calculation model reach ±4.8 nm and ±34 nm, respectively, in the displacement range of 5 mm. The experiments and the theoretical analyses both indicate that our proposed measuring system has great potential for achieving an accuracy of tens of nanometers and a range of hundreds of millimeters.
Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination
Authors: Wang HuiJuan , Tang ZiYue , Zhao YuanQing , Chen YiChang , Zhu ZhenBo , Zhang YuanPeng
Published: December 2019

A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system.
Selection of the Best Model of Distribution of Measurement Points in Contact Coordinate Measurements of Free-Form Surfaces of Products
Authors: Magdziak Marek
Published: December 2019

The article presents a new method for determining the distribution of measurement points, which can be used in the case of contact coordinate measurements of curvilinear surfaces of products. The developed method is based on multi-criteria decision analysis. In the case of the new method, the selection of the distribution of measurement points on free-form surfaces is carried out based on the analysis of five different criteria. The selection of the best model of the distribution of measurement points results from the accuracy of coordinate measurements, the time needed to complete measurement tasks, the number of measurement points, the accuracy of the substitute surface representing the measured free-form surface and the area where measurement points are located. The purpose of using the developed method of the distribution of measurement points is to increase the performance of coordinate measurements primarily by increasing the automation of strategy planning of measurements of curvilinear surfaces and improving the accuracy of measurements of free-form surfaces of products. The new method takes into account various aspects of coordinate measurements to determine the final model of the distribution of measurement points on measured surfaces of products, thereby increasing the probability of the proper determination (i.e., identifying the highest deviations of a product) of the location of measurement points on the surfaces of a measured object. The paper presents an example of the application of the created method, which concerns the selection of the best model of the distribution of measurement points on a selected free-form surface. This example is based on, among others, the results of experimental investigations, which were carried out by using the ACCURA II coordinate measuring machine equipped with the VAST XXT measuring probe and the Calypso measurement software. The results of investigations indicate a significant reduction in time of coordinate measurements of products when using the new method for determining the distribution of measurement points. However, shortening the time of coordinate measurements does not reduce their accuracy.
Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
Authors: He Chaoxin , Zhang Min , Wu Guizhou , Guo Fucheng
Published: December 2019

To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced.
Theoretical Limits of Star Sensor Accuracy
Authors: Fialho Marcio A. A. , Mortari Daniele
Published: December 2019

To achieve mass, power and cost reduction, there is a trend to reduce the volume of many instruments aboard spacecraft, especially for small spacecraft (cubesats or nanosats) with very limited mass, volume and power budgets. With the current trend of miniaturizing spacecraft instruments one could naturally ask if is there a physical limit to this process for star sensors. This paper shows that there is a fundamental limit on star sensor accuracy, which depends on stellar distribution, star sensor dimensions and exposure time. An estimate of this limit is given for our location in the galaxy.
Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease
Authors: Rehman Rana Zia Ur , Del Din Silvia , Shi Jian Qing , Galna Brook , Lord Sue , Yarnall Alison J. , Guan Yu , Rochester Lynn
Published: December 2019

Early diagnosis of Parkinson’s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway—GAITRite; and an accelerometer attached at the lower back—Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.
Magnetic Angular Rate and Gravity Sensor Based Supervised Learning for Positioning Tasks
Authors: Nagy Balázs , Botzheim János , Korondi Péter
Published: December 2019

This paper deals with sensor fusion of magnetic, angular rate and gravity sensor (MARG). The main contribution of this paper is the sensor fusion performed by supervised learning, which means parallel processing of the different kinds of measured data and estimating the position in periodic and non-periodic cases. During the learning phase, the position estimated by sensor fusion is compared with position data of a motion capture system. The main challenge is avoiding the error caused by the implicit integral calculation of MARG. There are several filter based signal processing methods for disturbance and noise estimation, which are calculated for each sensor separately. These classical methods can be used for disturbance and noise reduction and extracting hidden information from it as well. This paper examines the different types of noises and proposes a machine learning-based method for calculation of position and orientation directly from nine separate sensors. This method includes the disturbance and noise reduction in addition to sensor fusion. The proposed method was validated by experiments which provided promising results on periodic and translational motion as well.
Computer Vision Approach for the Determination of Microbial Concentration and Growth Kinetics Using a Low Cost Sensor System
Authors: Grossi Marco , Parolin Carola , Vitali Beatrice , Riccò Bruno
Published: December 2019

The measurement of microbial contamination is of primary importance in different fields, from environmental monitoring to food safety and clinical analysis. Today, almost all microbiology laboratories make microbial concentration measurements using the standard Plate Count Technique (PCT), a manual method that must be performed by trained personnel. Since manual PCT analysis can result in eye fatigue and errors, in particular when hundreds of samples are processed every day, automatic colony counters have been built and are commercially available. While quick and reliable, these instruments are generally expensive, thus, portable colony counters based on smartphones have been developed and are of low cost but also not accurate as the commercial benchtop instruments. In this paper, a novel computer vision sensor system is presented that can measure the microbial concentration of a sample under test and also estimate the microbial growth kinetics by monitoring the colonies grown on a Petri dish at regular time intervals. The proposed method has been in-house validated by performing PCT analysis in parallel under the same conditions and using these results as a reference. All the measurements have been carried out in a laboratory using benchtop instruments, however, such a system can also be realized as an embedded sensor system to be deployed for microbial analysis outside a laboratory environment.
The Coach–Athlete Relationship in Strength and Conditioning: High Performance Athletes’ Perceptions
Authors: Foulds Steven J. , Hoffmann Samantha M. , Hinck Kris , Carson Fraser
Published: December 2019

The primary purpose of this study was to investigate high performance athlete perceptions of strength and conditioning coaches, specifically, (1) their character traits, (2) the effective behaviours that display these traits, and (3) how these relationships were fostered. Using the 3+1 C’s model of coach–athlete relationships as a framework (Jowett, 2007), 12 semi-structured interviews were conducted with high performance athletes (six female; six male) representing a variety of sports (i.e., freestyle wrestling, triathlon, field hockey, cycling, rowing, rugby union, netball, table tennis, and ice hockey). Participants ranged in age from 18 to 53 years (M = 29, SD = 9). Interviews took between 19–47 min and were transcribed verbatim. The transcripts equated to a total of 188 pages of data that were analyzed, coded, and further grouped into higher-order themes and general dimensions. The findings revealed 14 higher-order themes categorized under the 3+1 C’s general dimensions of closeness, commitment, complementarity, and co-orientation.
Drivers for Sustainable Business Models in Start-Ups: Multiple Case Studies
Authors: Voinea Cosmina Lelia , Logger Marcel , Rauf Fawad , Roijakkers Nadine
Published: December 2019

Mechanisms that large organizations employ to facilitate corporate social responsibility (CSR) engagement simply do not apply to start-ups due to distinct differences. The purpose of this study was to gain insight into how start-ups strive for sustainability in their business models by investigating internal and external drivers related to organizational processes, managerial characteristics, and stakeholder expectations. We explored key factors such as decision-making regarding CSR engagement, business values about sustainability, entrepreneurial orientation, and the relevance of the CSR theater (philanthropic orientation, disruptive innovation, or transforming the ecosystem). Multiple case studies and interview data elucidated how start-ups engage with their community and stakeholders to determine the best approach to sustainability demands, how start-ups embed sustainability practices within their business models, and how these practices match with the entrepreneurs’ personalities. On the basis of our case studies and data analysis, we propose that the decision to engage in CSR is treated as an investment decision. The business values of a start-up determine its CSR engagement. The philanthropic drive of a start-up determines its CSR initiatives, which are then in line with the field the start-up is operating in. Entrepreneurs’ willingness to adopt CSR practices is determined by their personalities and organizational expertise and experiences. CSR engagement within the business models of start-ups is based on a combination of financial and social capital, while financial benefits act as a continuous motivator for CSR engagement from inception.
The Paradox of Value and Economic Bubbles: New Insights for Sustainable Economic Development
Authors: Gryshova Inna , Shabatura Tatyana , Girdzijauskas Stasys , Streimikiene Dalia , Ciegis Remigijus , Griesiene Ingrida
Published: December 2019

The article presents a new approach to the solution to the paradox of value based on the theory of marginal utility of the Austrian School of Economics. The new approach is based on the theory of compound interests and economic bubbles. The study presents valuable theoretical insight towards sustainable economic development. The paper provides a brief formulation of the theory of compound interests, introduces the structure of compound interests model, and shows the relations between simple and compound interests as well as dimensionless interests. It reveals the saturation phenomena, and emphasizes its impact on economic and financial bubbles. The relation between the value of paradox and the price bubbles is presented. The article also explains the possibilities of the phenomenology of growth in formulating the paradox of saturation and studying the paradox of value. The phenomenological method allows to relate prices and financial and economic bubbles into a general entirety. Therefore, this paper deals with very important sustainable development issues as it discusses the market saturation phenomena, economic bubbles, and emotional consumer behavior. The limitations of behavioral theories are pointed out and their misunderstandings of information cascades are emphasized. The article provides the explanation of the nature of economic bubbles by employing the theory of compound interests.
Transport Service Provider Perception of Barriers and Urban Freight Policies in Brazil
Authors: de Oliveira Leise Kelli , Leite Nascimento Carla de Oliveira , de Sousa Paulo Renato , de Resende Paulo Tarso Vilela , Ferreira da Silva Francisco Gildemir
Published: December 2019

The aim of this paper is to analyze the perceptions of the transport service providers on the barriers in urban freight transport and the restrictions imposed on freight vehicles, identifying which urban freight transport policies can improve this activity in Brazil. Principal component analysis allowed us to identify which are barriers in urban cargo transport and the restrictions imposed on freight vehicles. In addition, we evaluated freight policies that can improve the activity using probabilistic preference. We obtained the database from a web-based survey of transport service providers in Brazil. The results indicate that the urban freight vehicle restriction is related to congestion and availability of loading and unloading areas. The freight vehicle restriction is a result of the lack of information and data. The theft of cargo significantly impacts urban freight transport. Transport service provider company perceptions indicate an urban mobility plan as the most-required freight policy to improve freight mobility. To reduce the barriers to urban freight planning, an understanding of the problem involving the stakeholders is needed. To do so, the challenge is to change the culture in the transportation planning process by integrating various actors to create an urban logistics plan that generates opportunities for the city.
The Influence of Teacup Shape on the Cognitive Perception of Tea, and the Sustainability Value of the Aesthetic and Practical Design of a Teacup
Authors: Yang Su-Chiu , Peng Li-Hsun , Hsu Li-Chieh
Published: December 2019

The ceramic industry is among the most profitable industries in the world, but, because of the use of nonrenewable materials and high fuel consumption, it also has a carbon footprint. Ceramic materials account for the majority of drinking vessels. Several scholars found that consumers’ awareness of drinks and purchasing desires are highly correlated with a vessel’s shape and color—in other words, the visual stimulation. However, since prior studies have focused on alcohol, bubble drinks, juice, coffee, cocoa, etc., there has rarely been any research on the appropriate drinking vessels for Chinese tea. This study intends to investigate the visual design of vessels for Chinese tea, in terms of its impact on the taste of the drink, by integrating the thinking and methods of expert users and designers for the sustainability of design and industry. In this study, tea experts and designers were asked for their opinions as a means of data collection. Fuzzy set qualitative comparative analysis (Fs/QCA) was used for data analysis. This study proved that the design of a tea-drinking vessel could have an influence on the perception of the taste and scent of the tea. This research not only brings new meaning to the traditional concept of teacup design, but also reflects famous Japanese craftsman Liu Zongyue’s idea of practical beauty, which is beneficial to promoting Chinese tea culture, and contributes to sustainable design and sustainable behavior.
Random Spatial and Systematic Random Sampling Approach to Development Survey Data: Evidence from Field Application in Malawi
Authors: Maduekwe Ebelechukwu , de Vries Walter Timo
Published: December 2019

Implementing development surveys in developing countries can be challenging. Limited time, high survey costs, lack of information, and technical difficulties are some of the general constraints that plague development researchers. These constraints can hinder data collection and introduce selection bias into the survey data. We outline a multilevel sampling approach for use in areas where comprehensive information on geographical or household characteristics of local population are not readily available. Our approach includes the use of geographical information systems (GIS) for random spatial sampling and personal digital assistants (PDAs) with a global positioning system (GPS) for household systematic random sampling with random walk. Evidence from our field application in Malawi show that the multilevel sampling approach yields relevant survey data which is comparable to historical and nationally representative values; and supports rapid aggregation of preliminary results after the survey. This multilevel design is cost-effective in implementation and reduces bias avenues in the household selection. Overall, this multilevel sampling approach can be used to generate survey data in developing countries where detailed geographical information and household characteristics data are not readily available. It also presents ways of reducing bias in survey data given budget constraints.
Alliance Decision of Supply Chain Considering Product Greenness and Recycling Competition
Authors: Liu Yong , Shi Qian-qian , Xu Qian
Published: December 2019

In a closed-loop supply chain (CLSC), the right alliance can help manufacturers better manufacture green products and make more profits. Choosing the most suitable alliance partner is also critical for manufacturers. In regard to product greenness and recycling competition, this paper considers the CLSC comprised of a dominant manufacturer, a retailer, and a third-party recycler. Based on the Stackelberg game and equilibrium analysis, we discuss the optimal supply chain decision-making under four different models. Then, in order to ensure supply chain (SC) members’ enthusiasm to participate in the alliance, we design a profit distribution method to distribute the total profit to SC members. The results show that manufacturer’s optimal alliance decision is related to the degree of recycling competition. When less than the threshold, C alliance(the manufacturer make an alliance with the retailer and the third-party recycler at the same time) is optimal, otherwise, MR alliance(the manufacturer and the retailer make an alliance ) is more beneficial for the manufacturer.
Development and Forecast of Employment in Forestry in the Czech Republic
Authors: Toth Daniel , Maitah Mansoor , Maitah Kamil
Published: December 2019

Employment in forestry is an essential component of the forestry industry. It is a socio-economic phenomenon, which has been at the edge of economists’ interest for quite a long time. The proportion of employees in the forestry sector is relatively small, standing at only 0.6%. However, forestry as a sector has a very significant multiplier effect which is reflected in the growth of related jobs. Examples of this can be found in the production of forestry machinery and equipment, the construction of wooden and timber structures, and the furniture sector. These sectors are kept separately in economic and statistical records, but forestry remains their natural determinant. The aim of this work is to describe, analyze, and formulate the prognosis for the development of these types of jobs. Conclusions of the work show that there has been a decrease in employment and simultaneously an increase in labor productivity. This is due to a increasingly high use of technological equipment. Development forecasts show that the Czech Republic does not differ from the overall surveyed trends in other selected countries. It is therefore evident that forecasts of the development of employment in forestry are also relevant in other similar countries. Our results show a statistically significant reduction in forestry employment. The analysis focused on the Czech Republic, but the results may also apply to other European countries. A significant decrease in employment leads to instability in the forestry sector. It means a skilled labor force leaves the forestry sector and is not replaced. Disruption of knowledge continuity leads to a negative impact on the environment.
A Building Retrofit and Sensitivity Analysis in an Automatically Calibrated Model Considering the Urban Heat Island Effect in Abu Dhabi, UAE
Authors: Bande Lindita , Cabrera Adalberto Guerra , Kim Young Ki , Afshari Afshin , Ragusini Mario Favalli , Cooke Melanie Gines
Published: December 2019

Villas are a very common building typology in Abu Dhabi. Due to its preponderance in residential areas, studying how to effectively reduce energy demand for this type of building is critical for Abu Dhabi, and many similar cities in the region. This study aims to show the impact of proposed energy efficiency measures on a villa using a calibrated model and to demonstrate that to be accurate, the model must be driven using urban weather data instead of rural weather data due to the significance of the urban heat island effect. Available data for this case study includes construction properties, on-site (urban) weather data, occupancy-related loads and schedules and rural weather data. Four main steps were followed, weather data customisation combining urban and rural weather variables, model calibration using a genetic algorithm-based tool and simulating retrofit strategies. We created a calibrated model for electricity demand during 2016–2017 with a 4% normalized mean bias error and an 11% coefficient of variation of the mean square error. Changing from none to all retrofit strategies results in a 34% reduction in annual energy consumption. According to the calibrated model, increased urban temperatures cause a 7.1% increase in total energy consumption.
Size-Segregated Particulate Matter in a Selected Sports Facility in Poland
Authors: Bralewska Karolina , Rogula-Kozłowska Wioletta , Bralewski Adrian
Published: December 2019

The aims of this study were to determine the concentration of particulate matter, analyze the percentage share of four particulate matter subfractions (PM1, PM2.5, PM4, PM10) in TSP (total mass of particulate matter (PM)) in a typical Polish sports hall at different day periods during heating and non-heating seasons, and compare the average daily doses of respirable dust (PM4) for three groups of the sports hall users (pupils, teachers, and athletes). Gravimetric measurements of PM4 and TSP concentrations and optical measurements of the concentrations of five PM fractions (PM1, PM2.5, PM4, PM10, PM100) were conducted for 8 h a day, simultaneously inside and outside the hall, for 20 days each in summer and winter. During training, PM mass was concentrated mainly in coarse particles (PM2.5–100) (summer—55%, winter—35%). Without activity, the main part of PM mass was from fine particles (PM2.5, summer—59%, winter—75%). In summer, PM inside the hall originated mainly from internal sources. In winter, the fine PM concentration was affected by outdoor sources. The daily doses of PM4 for different groups of sports hall users indicate that the health exposure of sports practitioners to PM may be greater than for non-practitioners staying in the same conditions.
Spatio-Temporal Variation of Heavy Metal Pollution during Accidents: A Case Study of the Heshangshan Protected Water Area, China
Authors: Ding Xiaowen , Tan Yue , Hou Baodeng
Published: December 2019

Recently, water environmental accidents have occasionally occurred which have had wide-ranging influences, long durations and are difficult to deal with. The development of the social economy, the acceleration of industrialization, the huge discharge of industrial wastewater and the occasional occurrence of ship transportation accidents pose serious threats to the water quality of water inlets and protected water areas. This article applied the two-dimensional water quality model, used a GIS platform and FORTRAN language, and predicted spatio-temporal variations of the iron concentration during a water pollution accident. This research selected the water inlet of Heshangshan Water Plant and the Heshangshan protected water area as the research objective, and assumed a water pollution event had occurred. It was suggested that we should take corresponding emergency measures and relevant solutions to deal with the bad effects of water pollution accidents. The processes mainly included the selection of the study area, the determination of the equation to be used, parameters determination, as well as the identification of the accident scenario and source. The durations of the iron concentration exceeding the standard at the water inlet were 12–18 min and in the protected water area were 16–36 min in four water periods after the accident. In addition, the durations taken for the iron concentration to decrease to the background value in the protected water area were 18–38 min after the accident in four water periods in the accident scenario. Relevant departments should take some contingency measures to avoid fetching water from the intake after the accident within 40 min after the accident and the relevant staff can cancel the warning 40 min after the accident.
Evolution of an Estuarine Island in the Anthropocene: Complex Dynamics of Chongming Island, Shanghai, P.R. China
Authors: Wu Siduo , Chen Ruishan , Meadows Michael E.
Published: December 2019

Islands are known to be vulnerable to many natural and anthropogenic hazards, especially estuarine islands, which are affected at rates and intensities above those found elsewhere around the globe. The sustainable development of estuarine islands has been a part of their evolution, which has been a continuous integration of human impact and response to natural processes. This study reviews the complex dynamics of Chongming Island, an estuarine island in Shanghai, with an emphasis on the nature of human intervention. We conclude that the island is an example of a coupled human-environment system, as it has been throughout its formation and evolution, which is integrated at the local (intracoupled), regional (pericoupled), and global (telecoupled) scale. This conceptual framework reveals Chongming Island to be an exemplar of—and indeed a test-case for—China’s vision of eco-civilization development, in which it is reimagined as an ‘eco-island’. Accordingly, we argue that islands are benchmarks for building sustainability in the Anthropocene, and a more complete understanding of their dynamics, and the factors that influence them in a metacoupled world, is critical for future development.
Life Cycle Assessment for Bioethanol Production from Oil Palm Frond Juice in an Oil Palm Based Biorefinery
Authors: Mohd YUSOF Siti Jamilah Hanim , Roslan Ahmad Muhaimin , Ibrahim Khairul Nadiah , Syed ABDULLAH Sharifah Soplah , Zakaria Mohd Rafein , Hassan Mohd Ali , Shirai Yoshihito
Published: December 2019

A study was conducted to estimate the possible environmental impacts arising from the generation of bioethanol from oil palm frond sugar juice in a theoretical oil palm based biorefinery model. A life cycle assessment (LCA) with the gate-to-gate approach was performed with the aid of SimaPro version 8.0 whereby ten impact categories were evaluated. The scope included frond collection and transportation, frond sugar juice extraction, and bioethanol fermentation and purification. Evaluation on the processes involved indicated that fermentation contributed to the environmental problems the most, with a contribution range of 52% to 97% for all the impact categories. This was due to a substantial usage of nutrient during this process, which consumes high energy for its production thus contributing a significant burden to the surrounding. Nevertheless, the present system offers a great option for biofuel generation as it utilizes sugar juice from the readily available oil palm waste. Not only solving the issue of land utilization for feedstock cultivation, the enzymatic saccharification step, which commonly necessary for lignocellulosic sugar recovery could also be eliminated.
Sustainable Tourism: A Competitiveness Strategy Perspective in Baja California
Authors: Bernal Escoto Blanca Estela , Portal Boza Malena , Feitó Madrigal Duniesky
Published: December 2019

Sustainability plays an important role in society by improving long-term quality of life, including future generations, seeking harmony between economic growth, social development and the protection of the environment. For decades, companies focused their efforts on maximizing returns for their stakeholders, setting aside the preservation of the environment by making indiscriminate use of local resources, which had a direct impact on the social and economic well-being of it, however today the organizations analyze and evaluate the demands, tastes and preferences of new generations as a competitive strategy, because they seek healthier and safer spars, showing increasing interest in social indicators, economics and mid-environment, denying sustainable strategies for their stakeholders. The combination of statistical techniques of descriptive and contingency analysis the sustainability of the tourist micro, small and medium-sized enterprises (MSMEs) of the state of Baja California through the application of the travel and tourism competitive index. The main findings show that the sustainable good practices implemented by micro-entrepreneurs so far has allowed them to develop competitive value strategies focused on their Stakeholders, seeing an increase in their competitiveness.
Can CEOs’ Corporate Social Responsibility Orientation Improve Firms’ Cooperation in International Scenarios?
Authors: González-Moreno Ángela , Ruiz-Palomino Pablo , Sáez-Martínez Francisco J.
Published: December 2019

This paper intends to analyze the role of the chief executive officer’s (CEO) Corporate Social Responsibility Orientation (CSRO) on interfirm cooperation. Interfirm cooperation is central to gaining competitiveness, particularly in international scenarios where firms must deal with uncertain challenges. Nonetheless, the current understanding of its key determinants needs further development. We argue that whereas environmental hostility pushes firms to cooperate for self-interested purposes, CEOs’ CSRO pulls towards cooperation as an end in itself, even more so under hostile conditions where the need for good-willed committed partners is higher. In a sample of 124 internationalized Spanish firms, we found that CEOs’ CSRO alone increases firms’ international interfirm cooperation and that this impact is stronger under hostile international environments. Our findings thus highlight the importance of leading firms in a socially responsible direction to boost their interfirm cooperation levels in international scenarios.