bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data
Authors: Xu Wenlong , Wang Guifen , Jiang Long , Cheng Xuhua , Zhou Wen , Cao Wenxi
Published: December 2020

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.
Editorial for Special Issue “Remote Sensing in Applications of Geoinformation”
Authors: Michaelides Silas
Published: December 2020

The diffusion of knowledge and information is currently more forceful than ever [...]
A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles
Authors: Rodrigo Marco Vicent , Kalkkuhl Jens , Raisch Jörg , Seel Thomas
Published: December 2020

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.
MUP: Simplifying Secure Over-The-Air Update with MQTT for Constrained IoT Devices
Authors: Sahlmann Kristina , Clemens Vera , Nowak Michael , Schnor Bettina
Published: December 2020

Message Queuing Telemetry Transport (MQTT) is one of the dominating protocols for edge- and cloud-based Internet of Things (IoT) solutions. When a security vulnerability of an IoT device is known, it has to be fixed as soon as possible. This requires a firmware update procedure. In this paper, we propose a secure update protocol for MQTT-connected devices which ensures the freshness of the firmware, authenticates the new firmware and considers constrained devices. We show that the update protocol is easy to integrate in an MQTT-based IoT network using a semantic approach. The feasibility of our approach is demonstrated by a detailed performance analysis of our prototype implementation on a IoT device with 32 kB RAM. Thereby, we identify design issues in MQTT 5 which can help to improve the support of constrained devices.
Localization of Scattering Objects Using Neural Networks
Authors: Haffner Domonkos , Izsák Ferenc
Published: December 2020

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.
Amine Detection Using Organic Field Effect Transistor Gas Sensors
Authors: Mougkogiannis Panagiotis , Turner Michael , Persaud Krishna
Published: December 2020

Low power gas sensors with high sensitivity and selectivity are desired for many practical applications. Devices based on organic field effect transistors are promising because they can be fabricated at modest cost and are low power devices. Organic field effect transistors fabricated in bottom-gate bottom-contact configuration using the organic semiconductor [2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno] [3,2-b]thiophene) (DPP-T-TT) were systematically investigated to determine the response characteristics to a series of alkylamines and ammonia. The highest sensitivity was to dibutylamine with a limit of detection of 0.025 ppb, followed by n-butylamine, 0.056 ppb, and ammonia, 2.17 ppb. A model was constructed based on the Antoine equation that successfully allows the empirical prediction of the sensitivity and selectivity of the gas sensor to various analytes including amines and alcohols based on the Antoine C parameter and the heat of the vaporization of the analyte.
Implementation of a Load Sensitizing Bridge Spherical Bearing Based on Low-Coherent Fiber-Optic Sensors Combined with Neural Network Algorithms
Authors: Guo Jingjing , Geng Tiesuo , Yan Huaizhi , Du Lize , Zhang Zhe , Sun Changsen
Published: December 2020

Low-coherent fiber-optic sensors combined with neural network algorithms were designed to carry out a load-sensitizing spherical bearing. Four sensing fibers were wound around the outside of the pot support of the spherical bearing uniformly deployed from upper to bottom. The upper three were configured in a distributed way to respond to the applied load as a function of the three strain sensors. The bottom one was employed as a temperature compensation sensor. A loading experiment was implemented to test the performance of the designed system. The results showed that there was a hysteresis in all the three sensors between loading and unloading process. The neural network algorithm is proposed to set up a function of the three sensors, treated as a set of input vectors to establish the input-output relationship between the applied loads and the constructed input vectors, in order to overcome the hysteresis existing in each sensor. An accuracy of 6% for load sensing was approached after temperature compensation.
On-Device Deep Personalization for Robust Activity Data Collection
Authors: Mairittha Nattaya , Mairittha Tittaya , Inoue Sozo
Published: December 2020

One of the biggest challenges of activity data collection is the need to rely on users and keep them engaged to continually provide labels. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. This study proposes a novel on-device personalization for data labeling for an activity recognition system using mobile sensing. The key idea behind this system is that estimated activities personalized for a specific individual user can be used as feedback to motivate user contribution and improve data labeling quality. First, we exploited fine-tuning using a Deep Recurrent Neural Network to address the lack of sufficient training data and minimize the need for training deep learning on mobile devices from scratch. Second, we utilized a model pruning technique to reduce the computation cost of on-device personalization without affecting the accuracy. Finally, we built a robust activity data labeling system by integrating the two techniques outlined above, allowing the mobile application to create a personalized experience for the user. To demonstrate the proposed model’s capability and feasibility, we developed and deployed the proposed system to realistic settings. For our experimental setup, we gathered more than 16,800 activity windows from 12 activity classes using smartphone sensors. We empirically evaluated the proposed quality by comparing it with a baseline using machine learning. Our results indicate that the proposed system effectively improved activity accuracy recognition for individual users and reduced cost and latency for inference for mobile devices. Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection with on-device personalization.
Quantitative Analysis of Metallographic Image Using Attention-Aware Deep Neural Networks
Authors: Xu Yifei , Zhang Yuewan , Zhang Meizi , Wang Mian , Xu Wujiang , Wang Chaoyong , Sun Yan , Wei Pingping
Published: December 2020

As a detection tool to identify metal or alloy, metallographic quantitative analysis has received increasing attention for its ability to evaluate quality control and reveal mechanical properties. The detection procedure is mainly operated manually to locate and characterize the constitution in metallographic images. The automatic detection is still a challenge even with the emergence of several excellent models. Benefiting from the development of deep learning, with regard to two different metallurgical structural steel image datasets, we propose two attention-aware deep neural networks, Modified Attention U-Net (MAUNet) and Self-adaptive Attention-aware Soft Anchor-Point Detector (SASAPD), to identify structures and evaluate their performance. Specifically, in the case of analyzing single-phase metallographic image, MAUNet investigates the difference between low-frequency and high-frequency and prevents duplication of low-resolution information in skip connection used in an U-Net like structure, and incorporates spatial-channel attention module with the decoder to enhance interpretability of features. In the case of analyzing multi-phase metallographic image, SASAPD explores and ranks the importance of anchor points, forming soft-weighted samples in subsequent loss design, and self-adaptively evaluates the contributions of attention-aware pyramid features to assist in detecting elements in different sizes. Extensive experiments on the above two datasets demonstrate the superiority and effectiveness of our two deep neural networks compared to state-of-the-art models on different metrics.
Promising Anti-MRSA Activity of Brevibacillus sp. Isolated from Soil and Strain Improvement by UV Mutagenesis
Authors: Songnaka Nuttapon , Lertcanawanichakul Monthon , Atipairin Apichart
Published: December 2020

Antibiotic-resistant infection is a major health problem, and a limited number of drugs are currently approved as antibiotics. Soil bacteria are promising sources in the search for novel antibiotics. The aim of the present study is to isolate and assess soil bacteria with anti-MRSA activity and improve their capabilities by UV mutagenesis. Soil samples from the upper south of Thailand were screened for antibacterial activity using the cross-streak method. Agar well diffusion was used to examine the activity of isolates against a spectrum of human pathogens. The most active isolate was identified by 16S rRNA sequencing, and the production kinetics and stability were investigated. The most promising isolate was mutated by UV radiation, and the resulting activity and strain stability were studied. The results show that isolates from the cross-streak method could inhibit Staphylococcus aureus TISTR 517 (94 isolates) and Escherichia coli TISTR 887 (67 isolates). Nine isolates remained active against S. aureus TISTR 517 and MRSA, and eight isolates inhibited the growth of E. coli TISTR 887 as assessed using agar well diffusion. The most active strain was Brevibacillus sp. SPR-20, which had the highest activity at 24 h of incubation. The active substances in culture supernatants exhibited more than 90% activity when subjected to treatments involving various heat, enzymes, surfactants, and pH conditions. The mutant M201 showed significantly higher activity (109.88–120.22%) and strain stability compared to the wild-type strain. In conclusion, we demonstrate that soil Brevibacillus sp. is a potential resource that can be subjected to UV mutagenesis as a useful approach for improving the production of anti-MRSA in the era of antibiotic resistance.
Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data
Authors: Niu Haoran , Omitaomu Olufemi A. , Cao Qing C.
Published: December 2020

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.
The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review
Authors: Patella Sergio Maria , Grazieschi Gianluca , Gatta Valerio , Marcucci Edoardo , Carrese Stefano
Published: December 2020

Widespread adoption of green vehicles in urban logistics may contribute to the alleviation of problems such as environmental pollution, global warming, and oil dependency. However, the current adoption of green vehicles in the last mile logistics is relatively low despite many actions taken by public authorities to overcome the negative externalities of distributing goods in cities. This paper presents a comprehensive literature review on studies investigating the adoption of green vehicles in urban freight transportation, paying specific attention to e-commerce. To shed light on the adoption of green vehicles in city logistics, the paper conducts a systematic review of the empirical literature on the topic. The 159 articles reviewed were classified into the following: (a) Optimization and scheduling (67 papers); (b) policy (55 papers); (c) sustainability (37 papers). Among the 159 articles, a further selection of 17 papers dealing with e-commerce, i.e., studies that highlight the most relevant aspects related to the integration of green vehicles in e-commerce urban logistics, was performed. Our findings indicate that green vehicles are competitive in urban deliveries characterized by frequent stop-and-go movements and low consolidation levels while incentives are still necessary for their adoption. The use of autonomous vehicles results the most promising and challenging solution for last-mile logistics.
Sustainability of Impact Sourcing Initiatives in Higher Education for Graduates’ Employability
Authors: Aman Aini
Published: December 2020

Impact sourcing (ImS) initiatives in higher education play important roles in the employability of graduates, especially in the global business services (GBS) industry. This study explores the sustainability of ImS in higher education institutions (HEIs) for graduates’ employability. This study used qualitative research methodology, collecting data from related documents, observations, interviews, and focus groups with selected students, lecturers, and industry practitioners. The data were analyzed using Sen’s capability framework, with the primary objective of analyzing individual capabilities—specifically, conversions, resources, and capabilities. The findings show that ImS could improve future graduates’ technical and soft skills, thereby enhancing their employability in the GBS industry. Some constraints that need to be overcome include the infrastructure, rewards, and incentives, as well as system reliability. This study contributes to the development of the capability framework for ImS initiatives in relation to the employability of graduates and other marginalized communities. The conversions, resources, and capabilities should be considered when assessing the impact of ImS. In addition, this study contributes to the talent pool agenda with a specific focus on ImS and graduates’ employability.
RETRACTED: Soil Microbiome Manipulation Gives New Insights in Plant Disease-Suppressive Soils from the Perspective of a Circular Economy: A Critical Review
Authors: De Corato Ugo
Published: December 2020

This review pays attention to the newest insights on the soil microbiome in plant disease-suppressive soil (DSS) for sustainable plant health management from the perspective of a circular economy that provides beneficial microbiota by recycling agro-wastes into the soil. In order to increase suppression of soil-borne plant pathogens, the main goal of this paper is to critically discuss and compare the potential use of reshaped soil microbiomes by assembling different agricultural practices such as crop selection; land use and conservative agriculture; crop rotation, diversification, intercropping and cover cropping; compost and chitosan application; and soil pre-fumigation combined with organic amendments and bio-organic fertilizers. This review is seen mostly as a comprehensive understanding of the main findings regarding DSS, starting from the oldest concepts to the newest challenges, based on the assumption that sustainability for soil quality and plant health is increasingly viable and supported by microbiome-assisted strategies based on the next-generation sequencing (NGS) methods that characterize in depth the soil bacterial and fungal communities. This approach, together with the virtuous reuse of agro-wastes to produce in situ green composts and organic bio-fertilizers, is the best way to design new sustainable cropping systems in a circular economy system. The current knowledge on soil-borne pathogens and soil microbiota is summarized. How microbiota determine soil suppression and what NGS strategies are available to understand soil microbiomes in DSS are presented. Disturbance of soil microbiota based on combined agricultural practices is deeply considered. Sustainable soil microbiome management by recycling in situ agro-wastes is presented. Afterwards, how the resulting new insights can drive the progress in sustainable microbiome-based disease management is discussed.
Analysis of the State of the Art of International Policies and Projects on CCU for Climate Change Mitigation with a Focus on the Cases in Korea
Authors: Jung Seok-ho , Lee Seong-ho , Min Jihee , Lee Mee-hye , Ahn Ji Whan
Published: December 2020

In 2016, the Korean government selected carbon capture and utilization (CCU) as one of the national strategic projects and presented a detailed roadmap to reduce greenhouse gas emissions and to create new climate industries through early demonstration of CCU technology. The Korean government also established the 2030 Greenhouse Gas Reduction Roadmap in 2016 and included carbon capture, utilization, and storage (CCUS) technology in the new energy industry sector as a CCU technology. The Korean government recognizes the importance of CCUS technology as a mid- to long-term measure to reduce greenhouse gas emissions and implements policies related to technological development. The United States (U.S.), Germany, and China also expect CCUS technology to play a major role in reducing greenhouse gases in the industrial sector in terms of climate and energy policy. This study analyzed the CCU-related policies and technological trends in the U.S., Germany, and China, including major climate and energy plans, driving roadmaps, some government-led projects, and institutional support systems. This work also statistically analyzed 447 CCU and CCUS projects in Korea between 2010 and 2017. It is expected to contribute to responding to climate change, promoting domestic greenhouse gas reduction, and creating future growth engines, as well as to be used as basic data for establishing CCU-related policies in Korea.
AdRem: An Integrated Approach for Adaptive Remediation
Authors: Palma Valerio , Accorsi Federico , Casasso Alessandro , Bianco Carlo , Cutrì Sarah , Robiglio Matteo , Tosco Tiziana
Published: December 2020

Abandoned industrial sites are generally characterized by soil and subsoil contamination. The paradigm currently employed for their remediation is “tabula rasa”, i.e., remediation of the entire site before its repurpose. However, this method is not economically, socially, or technologically sustainable: it delays the reuse of large areas, often well-connected to infrastructures, whose reuse may prevent further soil consumption. A possible solution to this problem is the application of adaptive reuse principles. This study, conducted at FULL (Future Urban Legacy Lab) in Politecnico di Torino, presents an interdisciplinary approach to spatialize, visualize, and manage interactions between reclamation and urban design for the transformation of contaminated urban areas. The core is based on a decision support parametric toolkit, named AdRem, developed to compare available remediation techniques and schematic urban design solutions. AdRem uses a 3D modeling interface and VPL scripting. Required input data are a geometric description of the site, data on the contamination status, viable remediation techniques, and associated features, and schematic urban design recommendations. A filtering process selects the techniques compatible with the site use foreseen. The output is an optimized remediation and reuse plan that can support an interdisciplinary discussion on possible site regeneration options.
Investigating the Potential Impact of Future Climate Change on UK Supermarket Building Performance
Authors: Hasan Agha , Bahadori-Jahromi Ali , Mylona Anastasia , Ferri Marco , Tahayori Hooman
Published: December 2020

The large-scale shifts in weather patterns and an unprecedented change in climate have given rise to the interest in how climate change will affect the carbon emissions of supermarkets. This study investigates the implications of future climatic conditions on the operation of supermarkets in the UK. The investigation was conducted by performing a series of energy modelling simulations on a LIDL supermarket model in London, based on the UK Climate Projections (UKCP09) future weather years provided by the Chartered Institution of Building Services Engineers (CIBSE). Computational fluid dynamic (CFD) simulations were used to perform the experiment, and the baseline model was validated against the actual data. This investigation ascertains and quantifies the annual energy consumption, carbon emissions, and cooling and heating demand of the supermarket under different climatic projections, which further validate the scientific theory of annual temperature rise as a result of long-term climatic variation. The maximum percentage increase for the annual energy consumption for current and future weather data sets observed was 7.01 and 6.45 for the 2050s medium emissions scenario, (90th) percentile and high emissions scenario, (90th) percentile, respectively, and 11.05, 14.07, and 17.68 for the 2080s low emissions scenario, (90th) percentile, medium (90th) percentile and high emissions scenario (90th) percentile, respectively. A similar inclining trend in the case of annual CO2 emissions was observed where the peak increase percentage was 6.80 and 6.24 for the 2050s medium emissions scenario, (90th) percentile and high (90th) percentile, respectively and 10.84, 13.84, and 17.45 for the 2080s low emissions scenario, (90th) percentile, medium emissions scenario (90th) percentile and high emissions scenario (90th) percentile, respectively. The study also analyses the future heating and cooling demands of the three warmest months and three coldest months of the year, respectively, to determine future variance in their relative values.
Toward a New Way for the Valorization of Miscanthus Biomass Produced on Metal-Contaminated Soils Part 2: Miscanthus-Based Biosourced Catalyst: Design, Preparation, and Catalytic Efficiency in the Synthesis of Moclobemide
Authors: Guérin Théo , Ghinet Alina , Waterlot Christophe
Published: December 2020

The conception of two biosourced catalysts (biocatalysts) using stems of miscanthus from the first part of this study are described herein. The temperature and the process used to extract metals from plant as mixture of Lewis acids were investigated in detail and proved to be essential in the design of the biosourced catalysts and their catalytic efficiency. One part of the crude mixture of Lewis acids extracted from the aerial parts of miscanthus plants was used without further treatment as a homogeneous biocatalyst (M1), and the other part was supported on montmorillonite K10 to provide a heterogeneous biocatalyst (MM1). M1 and MM1 were next tested in the synthesis of moclobemide (main ingredient of a drug used to treat depression) and led to excellent yield. Additional comparative experiments with different commercial metallic salts (NaCl, KCl, CaCl2, MgCl2, CuCl2, ZnCl2, FeCl2, FeCl3, MnCl2, and AlCl3) and their mixtures were carried out and underlined the importance of the multimetallic synergy on catalytic activity. Finally, a comparison of this new synthetic method assisted by the biosourced catalyst with the previously described procedures to access moclobemide was realized by calculating their green chemistry metrics. This study revealed that the use of the biosourced catalyst led to one of the greenest synthetic methods described today to produce moclobemide.
Toward the Dynamic Modeling of Transition Problems: The Case of Electric Mobility
Authors: Zolfagharian Mohammadreza , Walrave Bob , Romme A. Georges L. , Raven Rob
Published: December 2020

Transportation systems are transitioning to e-mobility, but scholars and policymakers are struggling to understand how to accomplish this transition effectively. In response, we draw on the technological innovation systems perspective and the unified theory of acceptance and use of technology to develop a theory-guided and entity-based simulation model to better understand, among others, electric vehicle (EV) adoption processes as a specific yet core element driving business innovation. By doing so, our model is among the first to capture and combine the macro-and micro-level interactions associated with the EV transition process. Our simulation results shed light on the impact of alternative innovation policies, notably by explaining relations between EV market dynamics and changes in e-mobility policies, such as EV-related subsidies and resource mobilization. As such, the simulation modeling approach adopted in this paper enables a more in-depth study of transition problems related to e-mobility. Notably, the resulting modular model can be adjusted to other e-mobility transition problems by changing the specified entities.
Gender Differences in Engagement in Unethical Pro-Organizational Behavior—Two Studies in Poland
Authors: Gigol Tomasz
Published: December 2020

The objective of this article is to present the results of research on the differences in unethical pro-organizational behavior between men and women. Enterprises run the risk of loss of reputation as a result of unethical pro-organizational behavior on the part of their staff. Such behavior also stands in opposition to sustainable enterprise development. However, an employee who engages in this type of behavior may suffer guilt and embarrassment. In the long term, this hinders employee well-being. The correlation between engagement in unethical pro-organizational behavior and the respondents’ gender was examined in two empirical studies. The first one was carried out among full-time employees of companies operating in various sectors in Poland, who were extramural students (N = 786). The second study was conducted half among employees of three large holding companies and half among working students of postgraduate studies (N = 389). The t-Student test was employed in the study. Statistical analysis was performed with the use of the IBM SPSS Amos 25.0.0 software. The theoretical framework of gender socialization theory was employed. The main conclusion is that women are less inclined to display unethical behavior for the sake of an organization than men. This is the first—or one of the first articles—devoted to studying the correlations between unethical pro-organizational behavior and gender. The results of the study can be applied in practice as they support increased participation of women in management as well as in creation and implementation of ethical codes in organizations.
Schools: An Untapped Opportunity for a Carbon Neutral Future
Authors: Odell Portia , Rauland Vanessa , Murcia Karen
Published: December 2020

School buildings, like many buildings around the world, are rapidly aging and becoming increasingly inefficient, leading to unnecessary carbon emissions and high utility bills. School buildings can offer some of the most cost-effective carbon abatement opportunities; however, very few schools focus on quantifiable carbon reduction. This is despite the growing emphasis on school sustainability and there is a notable gap in the literature in this area. This study examined 13 schools that participated in an innovative 2-year Low Carbon Schools Pilot Program (LCSPP) in Perth, Western Australia and explores how schools can effectively reduce their carbon emissions and operational costs associated with their buildings and infrastructure. Utility data from electricity, gas and water from the schools were analysed in conjunction with the initiatives each school implemented to identify the highest impact initiatives. The study showed that schools reduced their carbon emissions on average by 20% on a per student basis and saved an average of 15% in costs. More than 70% of the actions identified by participating schools were low or zero cost. This study demonstrates the abundant opportunities available for schools to reduce their consumption of resources, carbon emissions and utility costs with minimal to no cost outlay.
Cultural and Creative Ecosystems in Medium-Sized Cities: Evolution in Times of Economic Crisis and Pandemic
Authors: Escalona-Orcao Ana , Barrado-Timón Diego A. , Escolano-Utrilla Severino , Sánchez-Valverde Belén , Navarro-Pérez Maricruz , Pinillos-García Mariola , Sáez-Pérez Luis Antonio
Published: December 2020

This article seeks to forecast the short- and medium-term impacts of the coronavirus pandemic on the cultural and creative ecosystems of the 81 cities in Spain with between 50,000 and 100,000 inhabitants. Data on employment in nine sectors (per NACE Rev. 2) support the characterization of cultural ecosystems based on their dynamism, specialization, and propensity to form clusters (thanks to the co-location of certain sectors, meant to generate inter-sectoral spillovers and cross-sector synergies). The applied methodology consists of comparing these three attributes during and following the 2008 financial crisis. Then, any changes observed are interpreted in light of arguments from the COVID-19 literature, and from our own analysis, in order to assess the probability of recurrence (or nonrecurrence) during the current pandemic. Throughout this process, the metropolitan or non-metropolitan position of cities is taken into consideration. A first conclusion is that, as in the financial crisis, the behavior of ecosystems during the pandemic will be asymmetric. Secondly, metropolitan and non-metropolitan cities will maintain their distinctive sectoral specializations. Non-metropolitan cities appear to be more vulnerable for their strong connection to creative sectors most affected by the pandemic, although some can take advantage of good cultural supply and proximity to metropolitan centers. Metropolitan cities seem more secure, thanks to the higher presence of less vulnerable sectors (due to elevated and accelerating digitization). Nevertheless, most functional clusters were diminished during the financial crisis, and it seems unlikely that sectoral co-locations will re-emerge in a post-pandemic scenario as a business strategy, at least in the short term. Beyond these forecasts, we recommend dealing with certain structural failures of these ecosystems, especially the vulnerability and precariousness of most cultural and creative companies and workers.
Analysis of Ownership Data from Consolidated Land Threatened by Water Erosion in the Vlára Basin, Slovakia
Authors: Pagáč Mokrá Alexandra , Pagáč Jakub , Muchová Zlatica , Petrovič František
Published: December 2020

Water erosion is a phenomenon that significantly damages agricultural land. The current land fragmentation in Slovakia and the complete ambiguity of who owns it leads to a lack of responsibility to care for the land in its current condition, which could affect its sustainability in the future. The reason so much soil has eroded is obvious when looking at current land management, with large fields, a lack of windbreaks between them, and no barriers to prevent soil runoff. Land consolidation might be the solution. This paper seeks to evaluate redistributed land and, based on modeling by the Universal Soil Loss Equation (USLE) method, to assess the degree of soil erosion risk. Ownership data provided information on how many owners and what amount of area to consider, while taking into account new conditions regarding water erosion. The results indicate that 2488 plots of 1607 owners which represent 12% of the model area are still endangered by water erosion, even after the completion of the land consolidation project. The results also presented a way of evaluating the territory and aims to trigger a discussion regarding an unambiguous definition of responsibility in the relationship between owner and user.
Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest
Authors: Mielcarz Paweł , Osiichuk Dmytro , Cymerski Jarosław
Published: December 2020

The paper postulates that enhanced informational efficiency and signal processing capacity, which have characterized the evolution of commodity markets’ architecture during the last two decades, have rendered commodity prices more robust with respect to external shocks. Our econometric analysis of times series over 2001–2015 revealed a persistent decline in the responsiveness of crude oil prices to inflows of information concerning potentially supply-disruptive events. International news on terrorist attacks involving damage to oil infrastructure including those occurring in proximity to oil extraction sites, political unrest, and conflicts of rivaling factions are all documented to exercise a decreasing impact on oil price volatility both over short and medium observation spans. The previously observed spikes in oil prices accompanying similar disruptive events in OPEC countries are also shown to flatten over time as price sensitivity to information shocks declines. The discovered weakening of market response becomes more pronounced from the mid-2000s, which corresponds to the period of rapid algorithmization of commodity trading.
Evaluation of Joint Management of Pine Wood Waste and Residual Microalgae for Agricultural Application
Authors: Rosas José Guillermo , Gómez Natalia , Cara-Jiménez Jorge , González-Arias Judith , Olego Miguel Ángel , Sánchez Marta E.
Published: December 2020

This work addresses the joint management of residual microalgae and pine wood waste through pyrolysis to obtain a solid product for its use as soil amendment and two other by-products (liquid and gaseous) that can be used for energy purposes. Two management routes have been followed. The first route is through the co-pyrolysis of mixtures of both residual materials in several proportions and the later use of their solid fraction for soil amendment. The second route is the pyrolysis of pine wood waste and its direct combination with dried residual microalgae, also using it as soil amendment. The solid fraction assessment shows that from seven solid products (biochar) three stand out for their positive applicability in agriculture as soil amendment. In addition, they also present the benefit of serving as carbon sink, giving a negative balance of CO2 emissions. However, caution is suggested due to biochar applicability being subject to soil characteristics. To ensure the sustainability of the overall process, the energy available in liquid and gaseous fractions has been assessed for covering the drying needs of the residual microalgae in both cases. These results suggest that the pyrolysis process is a sustainable way to manage specific evaluated residues and their products.
Multidimensional Inventory of Students Quality of Life—MIS-QOL
Authors: Szydło Robert , Wiśniewska Sylwia , Ćwiek Małgorzata
Published: December 2020

An analysis of the literature on the quality of life and tools for its measurement revealed a gap in the field of research tools to study the quality of life of the so-called young adults. According to the theory of emerging adulthood, people aged 18–25 significantly differ from people of other age groups, including their self-perception. The aim of this article is to present the process of creating an inventory for multidimensional research on students’ quality of life, its structural and theoretical validity as well as reliability of the tool. Structural validity of Multidimensional Inventory of Students Quality of Life (MIS-QOL) is measured by three different indicators. Comparative fit index (CFI), PCLOSE and the root mean square error (RMSEA) show good or very good structural validity of every dimension and general score. Additionally, theoretical validity of general score measured as a correlation with commonly used questionnaires show correlation on level of 0.52 with somatic subscale of WHO, 0.631 with environmental subscale of WHO, 0.657 with Satisfaction with Life Scale (SWLS), 0.703 with psychological subscale of WHO, and 0.786 with Quality of Life Scale (QOLS). Reliability of each dimension and general score measured by Cronbach alfa and split-half coefficients are above 0.7. The multidimensional construction of MIS-QOL allows researchers to compare acquired data with international research conducted by Eurostat or the OECD.
Seismic Vulnerability Assessment Methodologies for Roadway Assets and Networks: A State-of-the-Art Review
Authors: El-Maissi Ahmad Mohamad , Argyroudis Sotirios A. , Nazri Fadzli Mohamed
Published: December 2020

Road networks are considered as one of the most important transport infrastructure systems, since they attain the economic and social prosperity of modern societies. For this reason, it is vital to improve the resiliency of road networks in order to function normally under daily stressors and recover quickly after natural disasters such as an earthquake event. In the last decades, vulnerability assessment studies for road networks and their assets gained great attention among the research community. This literature review includes a brief introduction about seismic vulnerability assessment, followed by the roadway assets damage and their damage states, and then the main typologies for the vulnerability assessment of roadway assets. Moreover, it focuses on available assessment methods, which were proposed to quantify the vulnerability of road networks and its assets. These methods are divided into two main categories, physical and traffic-based approaches. Methods based on fragility functions and vulnerability indexes were investigated in physical approach for roadways and its assets. On the other hand, accessibility and link importance index were explored in traffic-based approach for road networks. This paper reviews and comments the most common vulnerability assessment methods for road networks and its assets and points out their advantages and disadvantages. The main gaps and needs are identified and recommendations for future studies are provided.
A Textual and Visual Analysis of the Intrinsic Value Dimensions of Romania: Towards a Sustainable Destination Brand
Authors: Lupu Cristina , Rodrigues Ana Isabel , Stoleriu Oana Mihaela , Gallarza Martina G.
Published: December 2020

This work examines the projected image of Romania as an emerging tourism destination. Computed content-analysis was applied to the photos, text and video materials promoted online in Romania’s last international tourism campaign. The conceptual framework used corresponds to intrinsic values (play, aesthetics, ethics and spirituality), from Holbrook’s typology of value. Being more difficult to apprehend and therefore studied less, intrinsic values allow a more sophisticated approach to value creation. The purpose here is to identify the main attributes that are promoted about Romania by destination marketing organizations. The content analysis of text (last international promotion campaign Explore the Carpathian Garden) and visual data (27 photos from the official Facebook webpage and 7 TV videos) allow to depict an experiential view of Romania’s image: natural resources (coded as aesthetics with 29% of references), epistemic value of discovery (play 25.8%), authentic and historical traditions (ethics 25.8%) and wellness and therapeutic activities (spirituality, 19.3%). Destination marketing organizations have the potential to develop some distinctive aspects such as authenticity (as an ethical value dimension) and play (as an active, self-oriented value). Findings also highlight that a complimentary approach using textual and visual data might be a suitable option to research destination brand image.
The Regional Determinants of the New Venture Formation in China’s Car-Sharing Economy
Authors: Zhou Yan , Park Sangmoon
Published: December 2020

New ventures play an important role in promoting regional economic growth, employment and innovative development. In China, the new business model centered on the sharing economy has grown rapidly in a short time, especially with regard to car-sharing, which has become one of the new governmental strategies to promote economic development in China. New car-sharing ventures have been recognized as the leading sector in sustainable development for the circular economy and resource reuse. This paper explores the regional determinants of new firm formation in the nascent car-sharing industry in China. We used panel data from 449 car-sharing new ventures established in 257 cities in China from 2011 to 2019 to conduct an empirical analysis. The findings show that the urbanization economy, human capital and venture capital in the region have a positive impact on the establishment of new ventures. At the same time, the regional population density, localization economy, innovation capacity and competitive environment have no significant relationships with the establishment of new car-sharing firms. This paper provides insights for startups entering the field of car-sharing and a theoretical basis for the subsequent research on startups in the sharing economy industry.
Towards a Taxonomy for Design Options of Social Networking Technologies in Sustainable Business Models
Authors: Jacob Axel , Teuteberg Frank
Published: December 2020

Social networking technologies such as social media, crowd concepts, or gamification represent key resources for the integration of customers, value network partners, and the community into sustainable business models. However, there is a lack of understanding of how sustainable enterprises apply such technologies. To close this gap, we propose a taxonomy of design options for social networking technologies in sustainable business models. Our taxonomy comprises eight dimensions that deal with relevant questions of the design of social networking technologies. When creating our taxonomy, we built on existing literature and use cases and involved experienced practitioners in the field of sustainable business models for the validation of our taxonomy. In this way, our study contributes to knowledge on the use of social networking technologies in sustainable business models and how such technologies influence the boundaries of sustainable business models. Likewise, we provide practical insights into the use of social networking technologies in sustainable business models.