bluuebunny/crossref_metadata_2025 huggingface dataset:
A Framework for Unsupervised Wildfire Damage Assessment Using VHR Satellite Images with PlanetScope Data
Authors: Chung Minkyung , Han Youkyung , Kim Yongil
Published: November 2020

The application of remote sensing techniques for disaster management often requires rapid damage assessment to support decision-making for post-treatment activities. As the on-demand acquisition of pre-event very high-resolution (VHR) images is typically limited, PlanetScope (PS) offers daily images of global coverage, thereby providing favorable opportunities to obtain high-resolution pre-event images. In this study, we propose an unsupervised change detection framework that uses post-fire VHR images with pre-fire PS data to facilitate the assessment of wildfire damage. To minimize the time and cost of human intervention, the entire process was executed in an unsupervised manner from image selection to change detection. First, to select clear pre-fire PS images, a blur kernel was adopted for the blind and automatic evaluation of local image quality. Subsequently, pseudo-training data were automatically generated from contextual features regardless of the statistical distribution of the data, whereas spectral and textural features were employed in the change detection procedure to fully exploit the properties of different features. The proposed method was validated in a case study of the 2019 Gangwon wildfire in South Korea, using post-fire GeoEye-1 (GE-1) and pre-fire PS images. The experimental results verified the effectiveness of the proposed change detection method, achieving an overall accuracy of over 99% with low false alarm rate (FAR), which is comparable to the accuracy level of the supervised approach. The proposed unsupervised framework accomplished efficient wildfire damage assessment without any prior information by utilizing the multiple features from multi-sensor bi-temporal images.
Impact of the One-Stream Cloud Detection Method on the Assimilation of AMSU-A Data in GRAPES
Authors: Qin Zhengkun , Wu Zhiwen , Li Juan
Published: November 2020

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.
A New TDR-Based Sensing Cable for Improving Performance of Bridge Scour Monitoring
Authors: Wang Kai , Lin Chih-Ping , Jheng Wei-Hao
Published: November 2020

The use of time domain reflectometry (TDR) for real-time monitoring of bridge scour process has gone through several stages of development. The recently-proposed concept of bundled TDR sensing cable, in which two sets of insulated steel strands are twisted around and connected to a central coaxial cable to form a compact sensing cable, is a major change that has several advantages including the bottom-up sensing mechanism. Nevertheless, there is big room for improving its measurement sensitivity and signal to noise ratio (SNR). Changes in waveguide configuration also need to be made to avoid the adverse effect of insulation abrasion observed in field implementation. This study evaluated three new conductor and insulator configurations for constructing the sensing waveguide, including a balanced two-conductor waveguide (Type I), an unbalanced three-conductor waveguide with insulation coating on the middle conductor (Type II) and an unbalanced three-conductor with insulation coating on the two outer conductors (Type III). In all cases, the spacing between the two sets of steel strands (i.e., the waveguide conductors) was especially enlarged by replacing some steel strands with non-conductor wires to increases measurement sensitivity and avoid shorted conditions due to insulation abrasion. Experimental results show that Type III has the best performance on all counts. A new improved TDR sensing cable was hence proposed based on Type III configuration. Its performance was further evaluated by a full-scale experiment to take into consideration the long range of measurement in most field conditions. Detailed discussions on improvements of measurement sensitivity and SNR, limitation of sensing range, and mitigating the adverse effect of insulation abrasion are presented.
Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks
Authors: Książek Kamil , Romaszewski Michał , Głomb Przemysław , Grabowski Bartosz , Cholewa Michał
Published: November 2020

In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area—blood stain classification—is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98–100% for the easier image set, and 74–94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage; their best Overall Accuracy is in the range of 57–71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem.
Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach
Authors: Hossain Sharif , Chow Christopher W.K. , Hewa Guna A. , Cook David , Harris Martin
Published: November 2020

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.
Precise Topographic Model Assisted Slope Displacement Retrieval from Small Baseline Subsets Results: Case Study over a High and Steep Mining Slope
Authors: Wei Lianhuan , Feng Qiuyue , Liu Feiyue , Mao Yachun , Liu Shanjun , Yang Tianhong , Tolomei Cristiano , Bignami Christian , Wu Lixin
Published: November 2020

Due to the intrinsic side-looking geometry of synthetic aperture radar (SAR), time series interferometric SAR is only able to monitor displacements in line-of-sight (LOS) direction, which limits the accuracy of displacement measurement in landslide monitoring. This is because the LOS displacement is only a three dimensional projection of real displacement of a certain ground object. Targeting at this problem, a precise digital elevation model (DEM) assisted slope displacement retrieval method is proposed and applied to a case study over the high and steep slope of the Dagushan open pit mine. In the case study, the precise DEM generated by laser scanning is first used to minimize topographic residuals in small baseline subsets analysis. Then, the LOS displacements are converted to slope direction with assistance of the precise DEM. By comparing with ground measurements, relative root mean square errors (RMSE) of the estimated slope displacements reach approximately 12–13% for the ascending orbit, and 5.4–9.2% for the descending orbit in our study area. In order to validate the experimental results, comparison with microseism monitoring results is also conducted. Moreover, both results have found that the largest slope displacements occur on the slope part, with elevations varying from −138 m to −210 m, which corresponds to the landslide area. Moreover, there is a certain correlation with precipitation, as revealed by the displacement time series. The outcome of this article shows that rock mass structure, lithology, and precipitation are main factors affecting the stability of high and steep mining slopes.
Open-Ended Coaxial Probe Measurements of Complex Dielectric Permittivity in Diesel-Contaminated Soil during Bioremediation
Authors: Vergnano Andrea , Godio Alberto , Raffa Carla Maria , Chiampo Fulvia , Tobon Vasquez Jorge A. , Vipiana Francesca
Published: November 2020

In the bioremediation field, geophysical techniques are commonly applied, at lab scale and field scale, to perform the characterization and the monitoring of contaminated soils. We propose a method for detecting the dielectric properties of contaminated soil during a process of bioremediation. An open-ended coaxial probe measured the complex dielectric permittivity (between 0.2 and 20 GHz) on a series of six soil microcosms contaminated by diesel oil (13.5% Voil/Vtot). The microcosms had different moisture content (13%, 19%, and 24% Vw/Vtot) and different salinity due to the addition of nutrients (22 and 15 g/L). The real and the imaginary component of the complex dielectric permittivity were evaluated at the initial stage of contamination and after 130 days. In almost all microcosms, the real component showed a significant decrease (up to 2 units) at all frequencies. The results revealed that the changes in the real part of the dielectric permittivity are related to the amount of degradation and loss in moisture content. The imaginary component, mainly linked to the electrical conductivity of the soil, shows a significant drop to almost 0 at low frequencies. This could be explained by a salt depletion during bioremediation. Despite a moderate accuracy reduction compared to measurements performed on liquid media, this technology can be successfully applied to granular materials such as soil. The open-ended coaxial probe is a promising instrument to check the dielectric properties of soil to characterize or monitor a bioremediation process.
Dielectric Spectroscopy and Application of Mixing Models Describing Dielectric Dispersion in Clay Minerals and Clayey Soils
Authors: González-Teruel Juan D. , Jones Scott B. , Soto-Valles Fulgencio , Torres-Sánchez Roque , Lebron Inmaculada , Friedman Shmulik P. , Robinson David A.
Published: November 2020

The number of sensors, ground-based and remote, exploiting the relationship between soil dielectric response and soil water content continues to grow. Empirical expressions for this relationship generally work well in coarse-textured soils but can break down for high-surface area and intricate materials such as clayey soils. Dielectric mixing models are helpful for exploring mechanisms and developing new understanding of the dielectric response in porous media that do not conform to a simple empirical approach, such as clayey soils. Here, we explore the dielectric response of clay minerals and clayey soils using the mixing model approach in the frequency domain. Our modeling focuses on the use of mixing models to explore geometrical effects. New spectroscopic data are presented for clay minerals (talc, kaolinite, illite and montmorillonite) and soils dominated by these clay minerals in the 1 MHz–6 GHz bandwidth. We also present a new typology for the way water is held in soils that we hope will act as a framework for furthering discussion on sensor design. We found that the frequency-domain response can be mostly accounted for by adjusting model structural parameters, which needs to be conducted to describe the Maxwell–Wagner (MW) relaxation effects. The work supports the importance of accounting for soil structural properties to understand and predict soil dielectric response and ultimately to find models that can describe the dielectric–water content relationship in fine-textured soils measured with sensors.
Large Intelligent Surfaces Communicating Through Massive MIMO Rayleigh Fading Channels
Authors: Coelho Ferreira Ricardo , Facina Michelle S. P. , de Figueiredo Felipe A. P. , Fraidenraich Gustavo , de Lima Eduardo Rodrigues
Published: November 2020

Large intelligent surfaces (LIS) promises not only to improve the signal to noise ratio, and spectral efficiency but also to reduce the energy consumption during the transmission. We consider a base station equipped with an antenna array using the maximum ratio transmission (MRT), and a large reflector array sending signals to a single user. Each subchannel is affected by the Rayleigh flat fading, and the reflecting elements perform non-perfect phase correction which introduces a Von Mises distributed phase error. Based on the central limit theorem (CLT), we conclude that the overall channel has an equivalent Gamma fading whose parameters are derived from the moments of the channel fading between the antenna array and LIS, and also from the LIS to the single user. Assuming that the equivalent channel can be modeled as a Gamma distribution, we propose very accurate closed-form expressions for the bit error probability and a very tight upper bound. For the case where the LIS is not able to perform perfect phase cancellation, that is, under phase errors, it is possible to analyze the system performance considering the analytical approximations and the simulated results obtained using the well known Monte Carlo method. The analytical expressions for the parameters of the Gamma distribution are very difficult to be obtained due to the complexity of the nonlinear transformations of random variables with non-zero mean and correlated terms. Even with perfect phase cancellation, all the fading coefficients are complex due to the link between the user and the base station that is not neglected in this paper.
FarpScusn: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks
Authors: Li Fengyin , Wang Ying , Ju Hongwei , Wang Yanli , Wang Zhaojie , Zhou Huiyu
Published: November 2020

Anonymous technology is an effective way for protecting users’ privacy. Anonymity in sensor networks is to prevent the unauthorized third party from revealing the identities of the communication parties. While, in unstable wireless sensor networks, frequent topology changes often lead to route-failure in anonymous communication. To deal with the problems of anonymous route-failure in unstable sensor networks, in this paper we propose a fully anonymous routing protocol with self-healing capability in unstable sensor networks by constructing a new key agreement scheme and proposing an anonymous identity scheme. The proposed protocol maintains full anonymity of sensor nodes with the self-healing capability of anonymous routes. The results from the performance analysis show that the proposed self-healing anonymity-focused protocol achieves full anonymity of source nodes, destination nodes, and communication association.
Imaging Tremor Quantification for Neurological Disease Diagnosis
Authors: Mitsui Yuichi , Zin Thi Thi , Ishii Nobuyuki , Mochizuki Hitoshi
Published: November 2020

In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.
Energy Harvesting Technologies for Structural Health Monitoring of Airplane Components—A Review
Authors: Zelenika Saša , Hadas Zdenek , Bader Sebastian , Becker Thomas , Gljušćić Petar , Hlinka Jiri , Janak Ludek , Kamenar Ervin , Ksica Filip , Kyratsi Theodora , Louca Loucas , Mrlik Miroslav , Osmanović Adnan , Pakrashi Vikram , Rubes Ondrej , Ševeček Oldřich , Silva José , Tofel Pavel , Trkulja Bojan , Unnthorsson Runar , Velagić Jasmin , Vrcan Željko
Published: November 2020

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.
SodSAR: A Tower-Based 1–10 GHz SAR System for Snow, Soil and Vegetation Studies
Authors: Jorge Ruiz Jorge , Vehmas Risto , Lemmetyinen Juha , Uusitalo Josu , Lahtinen Janne , Lehtinen Kari , Kontu Anna , Rautiainen Kimmo , Tarvainen Riku , Pulliainen Jouni , Praks Jaan
Published: November 2020

We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, Finland. The system is based on a Vector Network Analyzer (VNA)-operated scatterometer mounted on a rail allowing the formation of SAR images, including interferometric pairs separated by a temporal baseline. We present the description of the radar, the applied SAR focusing technique, the radar calibration and measurement stability analysis. Measured stability of the backscattering intensity over a three-month period was observed to be better than 0.5 dB, when measuring a target with a known radar cross section. Deviations of the estimated target range were in the order of a few cm over the same period, indicating also good stability of the measured phase. Interforometric SAR (InSAR) capabilities are also discussed, and as a example, the coherence of subsequent SAR acquisitions over the observed boreal forest stand are analyzed over increasing temporal baselines. The analysis shows good conservation of coherence in particular at L-band, while higher frequencies are susceptible to loss of coherence in particular for dense vegetation. The potential of the instrument for satellite calibration and validation activities is also discussed.
Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images
Authors: Rivas-Villar David , Rouco José , Penedo Manuel G. , Carballeira Rafael , Novo Jorge
Published: November 2020

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.
The Implication of Petrographic Characteristics on the Mechanical Behavior of Middle Eocene Limestone, 15th May City, Egypt
Authors: Shinawi Abdelaziz El , Mésároš Peter , Zeleňáková Martina
Published: November 2020

The construction purposes of carbonate rocks are considered a major aspect of using these bedrocks based on their mechanical behavior. Accordingly, the physical and mechanical characterization of Middle Eocene Limestone bedrock in the new urban area at the 15th May City, Egypt was studied to assess the suitability of the carbonate rocks for construction. This study has been carried out to investigate the effect of petrographic characteristics on mechanical properties. To achieve this objective, the intact 30 rock core samples from 15 boreholes were selected at different depths. Based on study of the selected samples in thin sections, the limestone in the area was classified as lime-mudstone, wackestone, and grainstone. Additionally, the uniaxial compressive strength (UCS) and Schmidt Rebound Hammer (Rn) were determined to detect the mechanical properties of the limestone bedrock. The measured parameters (UCS and Rn) demonstrated a high direct relationship with mudstone and a poor direct relationship with dolomite and high negative correlation with wackestone and grainstone. Therefore, the Middle Eocene Limestone bedrock is more durable and has medium-strength, which made it suitable for constructions. Regression analysis was performed to find out some linear relationship between mechanical properties (UCS) with petrographic characteristics. The study reveals significant positive correlation between UCS and Rn with mudstone in accordance higher values of regression coefficient (R2 = 0.91 and R2 = 0.036), and an inverse relationship of Rn with dolomite % (R2 = 0.89 and R2 = 0.02), respectively. Consequently, the strong confidence on the mechanical parameters opens the way for engineers to predict the mechanical parameters that are required for engineering properties of limestone for the urban expansion.
Utilization of Evolutionary Plant Breeding Increases Stability and Adaptation of Winter Wheat Across Diverse Precipitation Zones
Authors: Merrick Lance F. , Lyon Steven R. , Balow Kerry A. , Murphy Kevin M. , Jones Stephen S. , Carter Arron H.
Published: November 2020

Evolutionary plant breeding (EPB) is a breeding method that was used to create wheat (Triticum aestivum L.)-evolving populations (EP), bi-parental and composite-cross populations (BPPs and CCPs), by using natural selection and bulking of seed to select the most adaptable, diverse population in an environment by increasing the frequency of favorable alleles in a heterogeneous population. This study used seven EPs to evaluate EPB in its ability to increase the performance of agronomic, quality, and disease resistance traits and adaptability across different precipitation zones. The populations were tested in field trials in three diverse locations over 2 years. Least significant differences showed the EPs performance was dependent on their pedigree and were statistically similar and even out-performed some of their respective parents in regards to grain yield, grain protein concentration, and disease resistance. Stability models including Eberhart and Russel’s deviation from Regression (S2di), Shukla’s Stability Variance (σi2), Wricke’s Ecovalance (Wi), and the multivariate Additive Main Effects and Multiplicative Interaction (AMMI) model were used to evaluate the adaptability of the EPs and their parents. The BPPs and CCPs demonstrated significantly greater stability over the parents across precipitation zones, confirming the capacity of genetically diverse EP populations to adapt to different environments.
The Intention to Purchase Recycled Products: Towards an Integrative Theoretical Framework
Authors: Bigliardi Barbara , Campisi Domenico , Ferraro Giovanna , Filippelli Serena , Galati Francesco , Petroni Alberto
Published: November 2020

The growing interest of the scientific literature regarding purchase behavior, circular economy and new business models has generated the need, as well as the opportunity, for a comprehensive review and categorization of the state of the existing research carried out so far. The present study aims at reconciling the wide but fragmented literature dealing with the purchase intention of recycled products. An integrative theoretical framework, able to combine several constructs, perspectives, and theories discussed to date on the topic, is proposed. Such framework represents a further step toward a comprehensive understanding of behavioral theories and constructs, which need to be understood to design effective business models for the circular economy. This effort could be highly valuable both for scholars interested in the topic—as the integrative framework could assist them in theorizing additional effects—and for firms’ managers—who can understand, more in depth, the drivers of the consumers’ purchasing process and act accordingly.
Assessing Collaborative Capabilities for Sustainability in Interorganizational Networks
Authors: Gonçalves de Almeida Juliana Maria , Gohr Cláudia Fabiana , Santos Luciano Costa
Published: November 2020

Sustainability in interorganizational networks depends on developing collaborative capabilities for this purpose. However, to improve their collaborative capabilities for sustainability (CCS), companies in interorganizational networks need methods to assess them. The existing CCS assessment approaches in the literature do not indicate what capabilities should be improved in an individual company to support collaborative strategies. Addressing this gap, the main contribution of this paper is providing a framework to assess CCS in interorganizational networks, providing support for improving firm-level capabilities. To attain this aim, the framework was based on the graph-theoretic approach (GTA), a multi-attribute technique that captures the interrelationships between elements of a system, providing multi-level and overall assessment. We tested the framework in three hotels from a tourism cluster in Brazil, where sustainability has been an unsettling issue. By applying the assessment framework, it was possible to generate a CCS index for each company and, thereby, to compare the results. Findings from the field confirmed the benefits of using the framework and its utility in assessing CCS and setting priorities for improvement.
Modeling Cross-National Differences in Automated Vehicle Acceptance
Authors: Etzioni Shelly , Hamadneh Jamil , Elvarsson Arnór B. , Esztergár-Kiss Domokos , Djukanovic Milena , Neophytou Stelios N. , Sodnik Jaka , Polydoropoulou Amalia , Tsouros Ioannis , Pronello Cristina , Thomopoulos Nikolas , Shiftan Yoram
Published: November 2020

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.
Do Corporate Environmental Sustainability Practices Influence Firm Value? The Role of Independent Directors: Evidence from Saudi Arabia
Authors: Ammer Mohammed Abdullah , Aliedan Meqbel Mishary , Alyahya Mansour Abdullah
Published: November 2020

Environmental sustainability has become a significant approach for firms to enhance their competitive advantage and reputation. This study examines the association between environmental sustainability disclosures and firm value, in addition to the moderating impact of independent board directors on this association. Using data from Saudi listed firms, we find that reporting environmental sustainability practices has a positive and significant impact on firm value, suggesting that enhanced responsibility and transparency in addition to improved stakeholder trust are important in promoting firm value. We also find that the influence of the reported environmental sustainability practices on firm value is strongly and positively moderated by the presence of independent directors on firms’ boards, signifying that stakeholders relate environmental reporting by firms to more independent directors providing better accountability to environmental practices. The implications of this study will be of great importance for policymakers, firm management, academia, and investors in considering the adoption and importance of firms’ environmental practices.
Reusing Newspaper Kiosks for Last-Mile Delivery in Urban Areas
Authors: González-Varona José M. , Villafáñez Félix , Acebes Fernando , Redondo Alfonso , Poza David
Published: November 2020

The current increase in e-commerce is generating growing problems in urban areas in terms of both traffic flow (increasing traffic, no parking spaces) and environmental issues (noise, atmospheric pollution, etc.). In parallel, an iconic element of historic districts is disappearing: more and more newspaper kiosks are closing their business as their work dwindles. In this scenario, the objective of this paper is to propose a model for last-mile parcel delivery that exploits the current available newspaper kiosk network by using them as parcel lockers. To demonstrate the benefits of this proposal, we map the kiosk network of the city of Valladolid (Spain), and compare the environmental impact of a traditional (door-to-door) delivery and the proposed model which reuses old kiosks as parcel lockers. The necessary steps to carry out simulations are described in detail so that experiments can be replicated in other cities that face the same issues.
Trait Optimism and Work from Home Adjustment in the COVID-19 Pandemic: Considering the Mediating Role of Situational Optimism and the Moderating Role of Cultural Optimism
Authors: Biron Michal , Peretz Hilla , Turgeman-Lupo Keren
Published: November 2020

An organization’s capacity to sustain a crisis, and to benefit from work-from-home (WFH) arrangements in routine times, is dependent on its employees’ ability to successfully adjust to WFH conditions. The COVID-19 pandemic, which forced vast numbers of employees worldwide to WFH, provides an unprecedented opportunity to identify factors that facilitate WFH adjustment. Leveraging this opportunity and drawing from theories on person-environment fit and work adjustment, we consider trait optimism as a possible facilitator of WFH adjustment during the pandemic. We further investigate how situational optimism and cultural (country-level) optimism contribute to the relationship between trait optimism and WFH adjustment. Using data from 388 employees in five countries, we find that trait optimism positively relates to WFH adjustment. This relationship is partly mediated by situational expectations regarding health/financial benefits of WFH amid the pandemic. Moreover, trait optimism is more strongly related to WFH adjustment in countries with high (vs. low) cultural optimism. This study addresses the call to investigate whether and how personality traits relate to WFH adjustment. Our findings can improve organizations’ ability to select and train employees who WFH, and to enhance operational resilience to future crises. Managers in global firms can draw from our results to understand how cultural differences affect the ease with which WFH is adopted, and to develop country-specific WFH practices.
Environment for Preschool Children to Learn Fundamental Motor Skills: The Role of Teaching Venue and Class Size
Authors: Cheung Peggy , Zhang Li
Published: November 2020

The development of children’s fundamental motor skills (FMS) is shaped by the environment surrounding them. The purpose of this study was to examine whether the changes of children’s FMS after an intervention program differed between classes conducted in different schooling conditions. Participants were 295 preschool children (62 boys, 133 girls; M = 5.4 years; SD = 0.28) from eight preschools in Hong Kong. Children participated in an 8-week FMS program and their FMS were assessed using the Test of Gross Motor Development-2 (TGMD-2). A linear mixed model was used to analyze the association on the changes of FMS score between children in different schooling conditions: (1) teaching venue size (large or small); and (2) class size (large, medium, or small), while accounting for the clustering of participants within preschools. The changes of object control skills were significantly different between groups with different teaching venue size (p = 0.000) (small: 5.54 (SD = 5.84) vs. large: 2.46 (SD = 6.42)) and different class size (p = 0.000) (small: 8.12 (SD = 6.34) vs. medium: 2.92 (SD = 6.26) vs. large: 4.00 (SD = 5.79)). The findings have practical implications for the teaching of FMS in that the social and physical environment in preschools should be considered for the design of FMS intervention programs.
The Perspective of E-Business Sustainability and Website Accessibility of Online Stores
Authors: Paștiu Carmen Adina , Oncioiu Ionica , Gârdan Daniel Adrian , Maican Silvia Ștefania , Gârdan Iuliana Petronela , Muntean Andreea Cipriana
Published: November 2020

E-commerce’s latest trends, highlighted by the global phenomena of the COVID-19 pandemic, explicitly show substantial changes in the online consumers’ behavior as well. More and more specialists are talking about the emergence of a new paradigm in consumption, and of possible evolutions toward sustainable consumption. The purpose of this research is to investigate how the antecedents of e-loyalty have changed their effect on consumer attitudes and perceptions of e-commerce sites. The results, obtained through a structural model of 523 validated questionnaires of Romanian consumers of electronic and household appliances, show the importance of the effect of website accessibility, a factor that, until recently, exerted an influence mediated by other elements grouped either under the umbrella of site quality or site design. The model validated after the analysis shows the direct effect of the accessibility of the websites on e-loyalty, satisfaction and trust of the consumers. Based on the results, the value of this study lies in the fact that it presents arguments on how the boost of e-commerce systems leads to sales processes’ sustainability improvement and, to the same extent, the improvement of the ways for reporting sustainability using IT tools (online databases with customers, software integrated into e-commerce platforms, electronic financial management).
A New Mindset for Circular Economy Strategies: Case Studies of Circularity in the Use of Water
Authors: Viles Elisabeth , Santos Javier , Arévalo Tamara Fernández , Tanco Martín , Kalemkerian Florencia
Published: November 2020

In a circular economy (CE) environment, it is important to make good and efficient use of resources and consider that the waste generated in production processes can be a valuable resource. However, the tools and methodologies conventionally used to analyze and evaluate production systems are based on techniques focused on linear production management models, where the primary purpose is to reduce the treatment and management of waste as much as possible and where productive and environmental efficiency are not evaluated simultaneously. Changing the paradigm from a linear to a circular economy requires that a new strategy for production systems be defined, one that makes production processes simultaneously circular and efficient (in terms of quality and productivity). In this context, a holistic vision is needed when implementing CE strategies. Therefore, the main aim of this paper is to provide evidence, through two real case studies in the use of water, that the management of this resource without considering systemic thinking may not be the most circular solution. Main results showed that improvements based on the traditional approach of reducing resource use cannot provide the best results if they are supported only by current process consumption without considering the circularity of resources.
Soil Organic Carbon and Labile Carbon Pools Attributed by Tillage, Crop Residue and Crop Rotation Management in Sweet Sorghum Cropping System
Authors: Malobane Mashapa Elvis , Nciizah Adornis Dakarai , Mudau Fhatuwani Nixwell , Wakindiki Isaiah Iguna Chabaari
Published: November 2020

Labile organic carbon (LOC) fractions are considered as sensitive indicators of change in soil quality and can serve as proxies for soil organic carbon (SOC). Although the impact of tillage, crop rotation and crop residue management on soil quality is well known, less is known about LOC and SOC dynamics in the sweet sorghum production systems in South Africa. This short-term study tested two tillage levels: no-till and conventional-tillage, two crop rotations: sweet-sorghum/winter grazing vetch/sweet sorghum and sweet-sorghum/winter fallow/sweet sorghum rotations and three crop residue retention levels: 30%, 15% and 0%. Tillage was the main factor to influence SOC and LOC fractions under the sweet sorghum cropping system in South Africa. NT increased SOC and all LOC fractions compared to CT, which concurs with previous findings. Cold water extractable organic carbon (CWEOC) and hot water extractable organic carbon (HWEOC) were found to be more sensitive to tillage and strongly positively correlated to SOC. An increase in residue retention led to an increase in microbial biomass carbon (MBC). This study concludes that CWEOC and HWEOC can serve as sensitive early indicators of change in soil quality and are an ideal proxy for SOC in the sweet-sorghum cropping system in South Africa.
Quasi-Delay-Insensitive Implementation of Approximate Addition
Authors: Balasubramanian Padmanabhan , Mastorakis Nikos E.
Published: November 2020

Asynchronous quasi-delay-insensitive (QDI) implementation of approximate addition is described in this article. The objective is to provide an insight into the optimization in design metrics that can be achieved with approximate addition compared to accurate addition based on a QDI implementation by considering a practical digital image processing application. For the QDI implementation, some approximate adder architectures are considered which are deemed suitable for both ASIC and FPGA based implementations. The accurate and approximate adders considered are of size 32-bits. The delay-insensitive dual-rail code was used for data encoding, and four-phase return-to-zero (RTZ) and return-to-one (RTO) handshake protocols were used separately for data communication. The implementations used a 32/28-nm complementary metal oxide semiconductor (CMOS) technology. The simulation results show that an approximate adder HOERAA achieves a 19.7% reduction in cycle time, a 12.5% reduction in area, and an 17.7% reduction in energy compared to the accurate adder for RTZ handshaking. For RTO handshaking, HOERAA achieves an 18.7% reduction in cycle time, a 12.4% reduction in area, and a 16.6% reduction in energy compared to the accurate adder. Another approximate adder HEAA achieves a 19.7% reduction in cycle time, a 12.9% reduction in area, and a 20.2% reduction in energy, compared to the accurate adder for RTZ handshaking. For RTO handshaking, HEAA achieves an 18.7% reduction in cycle time, a 12.9% reduction in area, and a 19.2% reduction in energy compared to the accurate adder. Nevertheless, the RTO handshaking is preferable to RTZ handshaking as the former facilitates slightly better optimizations in design metrics compared to the latter. The mean absolute error (MAE) and the root mean square error (RMSE), which are popular error metrics used in approximate computing, were calculated for the approximate adders and are given for a comparison. While the MAE of HOERAA and HEAA are comparable, HOERAA has 8.6% reduced RMSE compared to HEAA. Digital image processing results based on accurate and approximate additions are also given, to substantiate the usefulness of approximate addition.
Multistage Estimation of the Scale Parameter of Rayleigh Distribution with Simulation
Authors: Yousef Ali , Hassan Emad E. H. , Amin Ayman A. , Hamdy Hosny I.
Published: November 2020

This paper discusses the sequential estimation of the scale parameter of the Rayleigh distribution using the three-stage sequential sampling procedure proposed by Hall (Ann. Stat.1981, 9, 1229–1238). Both point and confidence interval estimation are considered via a unified optimal decision framework, which enables one to make the maximum use of the available data and, at the same time, reduces the number of sampling operations by using bulk samples. The asymptotic characteristics of the proposed sampling procedure are fully discussed for both point and confidence interval estimation. Since the results are asymptotic, Monte Carlo simulation studies are conducted to provide the feel of small, moderate, and large sample size performance in typical situations using the Microsoft Developer Studio software. The procedure enjoys several interesting asymptotic characteristics illustrated by the asymptotic results and supported by simulation.
Total Domination in Rooted Product Graphs
Authors: Cabrera Martínez Abel , Rodríguez-Velázquez Juan A.
Published: November 2020

During the last few decades, domination theory has been one of the most active areas of research within graph theory. Currently, there are more than 4400 published papers on domination and related parameters. In the case of total domination, there are over 580 published papers, and 50 of them concern the case of product graphs. However, none of these papers discusses the case of rooted product graphs. Precisely, the present paper covers this gap in the theory. Our goal is to provide closed formulas for the total domination number of rooted product graphs. In particular, we show that there are four possible expressions for the total domination number of a rooted product graph, and we characterize the graphs reaching these expressions.