bluuebunny/crossref_metadata_2025 huggingface dataset:
Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies
Authors: Klimkowska Anna , Cavazzi Stefano , Leach Richard , Grebby Stephen
Published: May 2022

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.
Choosing the Right Horizontal Resolution for Gully Erosion Susceptibility Mapping Using Machine Learning Algorithms: A Case in Highly Complex Terrain
Authors: Yang Annan , Wang Chunmei , Yang Qinke , Pang Guowei , Long Yongqing , Wang Lei , Yang Lijuan , Cruse Richard M.
Published: May 2022

Gully erosion susceptibility (GES) maps are essential for managing land resources and erosion control. Choosing the optimal horizontal resolution in GES mapping is a challenge. In this study, the optimal resolution for GES mapping in a complex loess hilly area on the Chinese Loess Plateau was tested using two machine learning algorithms. Unmanned aerial vehicle (UAV) images with a 9 cm resolution and GNSS RTK field-measured data were employed as base datasets, and 11 factors were used in the machine learning models. A series of horizontal resolutions, from 0.5–30 m, was used to determine which was the optimal level and how the resolution influenced the GES mapping. The results showed that the optimal resolution for GES mapping was 2.5–5 m in the loess hilly area, for both the random forest (RF) and extreme gradient-boosting (XGBoost) machine learning algorithms employed in this study. High resolutions overestimated the probability of gully erosion in stable regions, and it became difficult to identify gully and non-gully regions at too-coarse resolutions. The variable importance for GES mapping changed with the resolution and varied among variables. Slope gradient, land use, and contributing area were, in general, the three most critical factors. Land use remained an important factor at all the tested resolution levels. The importance of the slope gradient was underestimated at coarse resolutions (10–30 m), and the importance of the contributing area was underestimated at resolutions that were comparatively fine (0.5–1 m). This study provides an essential reference for selecting the optimal resolution for gully mapping, and thus, offers support for approaches attempting to map gullies using UAV.
Three-Dimensional Distribution of Biomass Burning Aerosols from Australian Wildfires Observed by TROPOMI Satellite Observations
Authors: Lemmouchi Farouk , Cuesta Juan , Eremenko Maxim , Derognat Claude , Siour Guillaume , Dufour Gaëlle , Sellitto Pasquale , Turquety Solène , Tran Dung , Liu Xiong , Zoogman Peter , Lutz Ronny , Loyola Diego
Published: May 2022

We present a novel passive satellite remote sensing approach for observing the three-dimensional distribution of aerosols emitted from wildfires. This method, called AEROS5P, retrieves vertical profiles of aerosol extinction from cloud-free measurements of the TROPOMI satellite sensor onboard the Sentinel 5 Precursor mission. It uses a Tikhonov–Phillips regularization, which iteratively fits near-infrared and visible selected reflectances to simultaneously adjust the vertical distribution and abundance of aerosols. The information on the altitude of the aerosol layers is provided by TROPOMI measurements of the reflectance spectra at the oxygen A-band near 760 nm. In the present paper, we use this new approach for observing the daily evolution of the three-dimensional distribution of biomass burning aerosols emitted by Australian wildfires on 20–24 December 2019. Aerosol optical depths (AOD) derived by vertical integration of the aerosol extinction profiles retrieved by AEROS5P are compared with MODIS, VIIRS and AERONET coincident observations. They show a good agreement in the horizontal distribution of biomass burning aerosols, with a correlation coefficient of 0.87 and a mean absolute error of 0.2 with respect to VIIRS. Moderately lower correlations (0.63) were found between AODs from AEROS5P and MODIS, while the range of values for this comparison was less than half of that with respect to VIIRS. A fair agreement was found between coincident transects of vertical profiles of biomass burning aerosols derived from AEROS5P and from the CALIOP spaceborne lidar. The mean altitudes of these aerosols derived from these two measurements showed a good agreement, with a small mean bias (185 m) and a correlation coefficient of 0.83. Moreover, AEROS5P observations reveal the height of injection of the biomass burning aerosols in 3D. The highest injection heights during the period of analysis were coincident with the largest fire radiative power derived from MODIS. Consistency was also found with respect to the vertical stability of the atmosphere. The AEROS5P approach provides retrievals for cloud-free scenes over several regions, although currently limited to situations with a dominating presence of smoke particles. Future developments will also aim at observing other aerosol species.
A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data
Authors: Liu Lei , Ye Jin , Li Shulei , Hu Shuai , Wang Qi
Published: May 2022

Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.
Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar
Authors: Zhang Huan , Li Hongyi , Li Haojie
Published: May 2022

River ice on the Tibetan Plateau has important impacts on the ecosystem and hydrology. High-resolution Synthetic Aperture Radar (SAR) is an important tool for monitoring the thickness of river ice in high-altitude areas without ground data. However, due to the complex topography and narrow width, it remains challenging to monitor the ice thickness of high-order rivers (high-level branches in the plateau river system) on the Tibetan Plateau using SAR. Therefore, this paper focuses on inverting the ice thickness by utilizing dual-polarized C-band radar data. We select a typical watershed in the northeastern Tibetan Plateau, namely, the Babao River basin (including the Babao River and Binggou River), as the study area. The results show the following: (1) Dual-polarized C-band radar data have the potential to monitor the ice thickness of high-order rivers. The RMSEs of the Babao and Binggou Rivers are 0.109 m and 0.258 m, respectively. (2) Ascending and descending orbit radar images perform differently in retrieving the ice thicknesses of rivers with different directions. (3) The thickness of river ice affects the inversion accuracy. (4) Polarization parameters have varying explanatory capacities depending on the river characteristics. Our findings can provide a reference for the subsequent development of highly generalizable river ice inversion equations using dual-polarized radar data.
Machine Learning in the Analysis of Multispectral Reads in Maize Canopies Responding to Increased Temperatures and Water Deficit
Authors: Spišić Josip , Šimić Domagoj , Balen Josip , Jambrović Antun , Galić Vlatko
Published: May 2022

Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues.
Nature versus Humans in Coastal Environmental Change: Assessing the Impacts of Hurricanes Zeta and Ida in the Context of Beach Nourishment Projects in the Mississippi River Delta
Authors: Yao Qiang , Cohen Marcelo Cancela Lisboa , Liu Kam-biu , de Souza Adriana Vivan , Rodrigues Erika
Published: May 2022

Hurricanes are one of the most devastating earth surface processes. In 2020 and 2021, Hurricanes Zeta and Ida pounded the Mississippi River Delta in two consecutive years, devastated South Louisiana, and raised tremendous concerns for scientists and stakeholders around the world. This study presents a high-resolution spatial-temporal analysis incorporating planialtimetric data acquired via LIDAR, drone, and satellite to investigate the shoreline dynamics near Port Fourchon, Louisiana, the eye of Ida at landfall, before and after the beach nourishment project and recent hurricane landfalls. The remote sensing analysis shows that the volume of the ~2 km studied beachfront was reduced by 240,858 m3 after consecutive landfalls of Hurricanes Zeta and Ida in 2020 and 2021, while 82,915 m3 of overwash fans were transported to the backbarrier areas. Overall, the studied beach front lost almost 40% of its volume in 2019, while the average dune crest height was reduced by over 1 m and the shoreline retreated ~60 m after the two hurricane strikes. Our spatial-temporal dataset suggests that the Louisiana Coastal Protection and Restoration Authority’s (CPRA’s) beach nourishment effort successfully stabilized the beach barrier at Port Fourchon during the hurricane-quiescent years but was not adequate to protect the shoreline at the Mississippi River Delta from intense hurricane landfalls. Our study supports the conclusion that, in the absence of further human intervention, Bay Champagne will likely disappear completely into the Gulf of Mexico within the next 40 years.
Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation
Authors: McKenzie Len J. , Langlois Lucas A. , Roelfsema Chris M.
Published: May 2022

Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of seagrass meadows in the optically complex waters of the Great Barrier Reef and explored enhanced mapping approaches with a focus on emerging technologies, and key considerations for future mapping. Our review showed that field-based mapping of seagrass has traditionally been the most common approach in the GBRWHA, with few attempts to adopt remote sensing approaches and emerging technologies. Using a series of case studies to harness the power of machine- and deep-learning, we mapped seagrass cover with PlanetScope and UAV-captured imagery in a variety of settings. Using a machine-learning pixel-based classification coupled with a bootstrapping process, we were able to significantly improve maps of seagrass, particularly in low cover, fragmented and complex habitats. We also used deep-learning models to derive enhanced maps from UAV imagery. Combined, these lessons and emerging technologies show that more accurate and efficient seagrass mapping approaches are possible, producing maps of higher confidence for users and enabling the upscaling of seagrass mapping into the future.
Spatiotemporal Change Detection of Coastal Wetlands Using Multi-Band SAR Coherence and Synergetic Classification
Authors: Liu Jie , Li Peng , Tu Canran , Wang Houjie , Zhou Zhiwei , Feng Zhixuan , Shen Fang , Li Zhenhong
Published: May 2022

Synthetic aperture radar (SAR) signal can penetrate clouds and some vegetation canopies in all weather, and therefore, provides an important measurement tool for change detection and sustainable development of coastal wetland environments and ecosystems. However, there are a few quantitative estimations about the spatiotemporal coherence change with multi-band SAR images in complex coastal wetland ecosystems of the Yellow River Delta (YRD). In this study, C-band Sentinel-1 and L-band ALOS-2 PALSAR data were used to detect the spatiotemporal distribution and change pattern of interferometric coherence in the coastal wetlands of the YRD. The results show that the temporal baseline has a greater impact on the interferometric coherence than the perpendicular baseline, especially for short wavelength C-band SAR. Furthermore, the OTSU algorithm was proven to be able to distinguish the changing regions. The coherence mean and standard deviation values of different land cover types varied significantly in different seasons, while the minimum and maximum coherence changes occurred in February and August, respectively. In addition, considering three classical machine learning algorithms, namely naive Bayes (NB), random forest (RF), and multilayer perceptron (MLP), we proposed a method of synergetic classification with SAR coherence, backscatter intensity, and optical images for coastal wetland classification. The multilayer perceptron algorithm performs the best in synergetic classification with an overall accuracy of 98.3%, which is superior to a single data source or the other two algorithms. In this article, we provide an alternative cost-effective method for coastal wetland change detection, which contributes to more accurate dynamic land cover classification and to an understanding of the response mechanism of land features to climate change and human activities.
A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering
Authors: Wang Nan , Zhang Xiaoling , Zhang Tianwen , Pu Liming , Zhan Xu , Xu Xiaowo , Hu Yunqiao , Shi Jun , Wei Shunjun
Published: May 2022

Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are frequently superior to traditional ones. However, most of the existing DL-based methods are purely data-driven and neglect the filtering model, so that they often need to use a large-scale complex architecture to fit the huge training sets. The issue brings a challenge to improve the accuracy of interferometric phase filtering without sacrificing speed. Therefore, we propose a sparse-model-driven network (SMD-Net) for efficient and high-accuracy InSAR phase filtering by unrolling the sparse regularization (SR) algorithm to solve the filtering model into a network. Unlike the existing DL-based filtering methods, the SMD-Net models the physical process of filtering in the network and contains fewer layers and parameters. It is thus expected to ensure the accuracy of the filtering without sacrificing speed. In addition, unlike the traditional SR algorithm setting the spare transform by handcrafting, a convolutional neural network (CNN) module was established to adaptively learn such a transform, which significantly improved the filtering performance. Extensive experimental results on the simulated and measured data demonstrated that the proposed method outperformed several advanced InSAR phase filtering methods in both accuracy and speed. In addition, to verify the filtering performance of the proposed method under small training samples, the training samples were reduced to 10%. The results show that the performance of the proposed method was comparable on the simulated data and superior on the real data compared with another DL-based method, which demonstrates that our method is not constrained by the requirement of a huge number of training samples.
Isotopic Characterization of Italian Industrial Hemp (Cannabis sativa L.) Intended for Food Use: A First Exploratory Study
Authors: Calvi Marco , Bontempo Luana , Pizzini Sarah , Cucinotta Lorenzo , Camin Federica , Stenni Barbara
Published: May 2022

In this study, Italian industrial hemp (Cannabis sativa L.) intended for food use was isotopically characterized for the first time. The stable isotope ratios of five bioelements were analyzed in different parts of the plant (i.e., roots, stems, inflorescences, and seeds) sampled in eight different regions of Italy, and in five hemp seed oils. The values of δ2H, δ13C, δ18O, and δ34S differed according to the latitude and, therefore, to the geographical origin of the samples and the climate conditions of plant growth, while the δ15N values allowed us to distinguish between crops grown under conventional and organic fertilization. The findings from this preliminary study corroborate the reliability of using light stable isotope ratios to characterize hemp and its derived food products and contribute to the creation of a first isotopic database for this plant, paving the way for future studies on authentication, traceability, and verification of organic labeling.
Deployments of Multiracial Masculinity and Anti-Black Violence: The Racial Framings of Barack Obama, George Zimmerman, and Daunte Wright
Authors: Mitchell Jasmine
Published: May 2022

In this article, I examine how political and media discourses of multiraciality are deployed to justify guilt and innocence. I trace the deployment of multiraciality to determine who is deserving of life or death in media coverage, political rhetoric, and court records during Obama’s presidency, in George Zimmerman’s 2013 acquittal, and in the 2021 killing of Daunte Wright. I examine the weaponization of discourses of multiracial identities as tools of white supremacy and anti-Blackness. Through such weaponization, the construction of the multiracial man as an index of racial progress and post-racism evident in the Barack Obama era enabled the violence and miscarriages of justice in the killings of Trayvon Martin and Daunte Wright. I consider how transnational and U.S. narratives of multiraciality, joined with anti-Blackness and white supremacy, enabled the acquittal of George Zimmerman. Furthermore, I examine how white womanhood and fears of Black masculinity facilitated the sympathy garnered towards Kim Potter. In considering the killing of Daunte Wright, this paper shows how multiraciality and racial malleability are valuable only when utilized for preserving racial hierarchies.
Co-Designing Sustainable Coordination to Support Inter-Organizational Decision Making
Authors: Jakubeit Nina , Haanstra Willem , Braaksma Jan , Rajabalinejad Mohammad , van Dongen Leo
Published: May 2022

Processes in inter-organizational projects tend to be complex to coordinate. Within these projects, stakeholders have to make decisions together, despite a limited awareness of the other parties’ interests and views. Frequently, coordination in inter-organizational projects is ineffective and inadequately addressed, despite the investment of considerable effort, which often results in delays and/or unwanted project outcomes. The purpose of this study is to investigate how a shared problem understanding for inter-organizational decision making can be achieved by means of sustainable coordination. In this study, CIMO logic was used to explore the context of the organizational change, followed by the application of design science research in order to develop an intervision process. The findings of this study are twofold. To manage the complex problem context, additional efforts were needed in order to create awareness of the team’s coordination activities. The application of the concept of co-designing resulted in a higher degree of sustainable relational coordination. The resulting intervision process aided the team in gaining a shared problem understanding of the decision making process in the inter-organizational project. The use of the co-designed intervision process can potentially be employed for other complex systematic problems, such as those occurring in the construction industry.
A Comprehensive Review on Utilization of Slaughterhouse By-Product: Current Status and Prospect
Authors: Limeneh Derseh Yilie , Tesfaye Tamrat , Ayele Million , Husien Nuredin Muhammed , Ferede Eyasu , Haile Adane , Mengie Wassie , Abuhay Amare , Gelebo Gemeda Gebino , Gibril Magdi , Kong Fangong
Published: May 2022

The meat processing industry produces a huge quantity of by-products, approximately 150 million tonnes per year. The live weight of the animals is distinguished as edible, inedible, and discardable by-products, with the discardable parts equating to 66%, 52%, and 80% of the overall live weight of cattle, lamb, and pigs, respectively. Only a small percentage of those by-products are nowadays exploited for the production of high added value products such as animal feed, glue, fertilizers, etc., whereas the main management method is direct disposal to landfills. As such, the current disposal methodologies of these by-products are problematic, contributing to environmental contamination, soil degradation, air pollution, and possible health problems. Nevertheless, these by-products are rich in collagen, keratin, and minerals, being thus promising sources of high-value materials such as bioenergy, biochemical and other biomaterials that could be exploited in various industrial applications. In this paper, the possible utilization of slaughterhouse by-products for the production of various high added value materials is discussed. In this context, the various processes presented provide solutions to more sustainable management of the slaughterhouse industry, contributing to the reduction of environmental degradation via soil and water pollution, the avoidance of space depletion due to landfills, and the development of a green economy.
Improving the Lipid Profile of Black Soldier Fly (Hermetia illucens) Larvae for Marine Aquafeeds: Current State of Knowledge
Authors: Rodrigues Daniela P. , Ameixa Olga M. C. C. , Vázquez José Antonio , Calado Ricardo
Published: May 2022

The replacement of fish meal and fish oil by insect-based ingredients in the formulation of marine aquafeeds can be an important step towards sustainability. To pursue this goal, the modulation of the lipid profile of black soldier fly larvae (Hermetia illucens) has received great attention. While its nutritional profile can shift with diet, the ability to modulate its lipidome is yet to be understood. The present work provides an overview of the lipid modulation of H. illucens larvae through its diet, aiming to produce a more suitable ingredient for marine aquafeeds. Marine-based substrates significantly improve the lipid profile of H. illucens larvae, namely its omega-3 fatty acids profile. An improvement of approximately 40% can be achieved using fish discards. Substantial levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), two essential fatty acids for marine fish and shrimp species, were recorded in H. illucens larvae fed on fish discards and coffee silverskin with Schyzochytrium sp. Unfortunately, these improvements are still deeply connected to marine-based bioresources, some still being too costly for use at an industrial scale (e.g., microalgae). New approaches using solutions from the biotechnology toolbox will be decisive to make H. illucens larvae a feasible alternative ingredient for marine aquafeeds without having to rely on marine bioresources.
Lime Mortar, a Boon to the Environment: Characterization Case Study and Overview
Authors: Manoharan Abirami , Umarani C.
Published: May 2022

Lime is an ancient construction material that has been utilized throughout the world in various forms, providing stable construction methods in usable conditions. Lime mortar is well known for its low carbon footprint in production and carbon absorption throughout its lifespan as a hardened material. The significant benefits of lime mortar were analyzed and reviewed for further research. Ancient lime constructions need proper maintenance for aesthetic and structural strengthening to preserve this cultural architecture of national pride. Hence, the characterization of ancient mortars is mandatory for renovation work. Here, we studied the various characterization methods available worldwide. We analyzed samples taken from the 1900-year-old Vedapureeswarar Temple of Thiruvothur, Cheyyar, and the 1800-year-old Lakshmi Narasimhar Temple of Parikkal, located in Tamil Nadu. Hardened samples from these two ancient temples were collected and analyzed. The mineralogical characterization of these mortars using SEM, XRF, FTIR and XRD gave immense knowledge of the mortar matrix. Experimental analysis indicated that using natural organic materials in the lime has made the structures more potent and stable. The characterization study provided information on the ratio of mortar mix used, the presence of organic ingredients, and the need for compatible repair materials for proper maintenance of the temple structures. The characterization study furthers the necessary knowledge to provide a compatible repair material and indicates the need for ancient construction technology in the current highly polluted environment.
Fundraising Appeals for the COVID-19 Epidemic Fight: A Cross-Country Study of Donor Responses
Authors: Bin-Nashwan Saeed Awadh , Sarea Adel , Al-Daihani Meshari , Ado Abdullahi Bala , Begum Halima , Alosaimi Mushari Hamdan , Abdul-Jabbar Hijattulah , Abdelsalam Mohammed Khalifa
Published: May 2022

This research explores the intrinsic and extrinsic motivations driving donors to engage in fundraising appeals launched through social networking sites (SNSs) to mitigate COVID-19’s impact on vulnerable communities from a cross-national perspective. The research adopted a quantitative approach through a web-based survey; a total of 801 donors were obtained from Kuwait and Bahrain and were useable for the analysis. Smart PLS structural equation modelling was used to validate the research model and derive significant insights. In the Kuwaiti sample, we found that humanitarian projects, internet technology, SNSs and religiosity significantly drive donor attitudes towards online donation. All these relationships are indirectly related to the intention to give via SNS through the mediating role of attitudes. As for the Bahraini sample, humanitarian projects, non-profit organizations (NPOs), SNSs, and religiosity significantly influence donors’ attitudes. Attitudes, on the other hand, have a visible mediating role in the relationships between these predictors and behavioral intentions. The findings could be useful for the development of appropriate policies that boost online monetary donations to support emergency aid for communities crushed by the pandemic. This research differs from the existing literature in that its multi-national study scrutinizes the incorporation of both internal and external predictors of fundraising activities into a distinctive related context such as SNSs, particularly in a time of epidemiological crises such as COVID-19.
An Empirical Study of Passengers’ Perceived Satisfaction with Monorail Service Quality: Case of Kuala Lumpur, Malaysia
Authors: Ibrahim Ahmad Nazrul Hakimi , Borhan Muhamad Nazri , Osman Mohd Haniff , Khairuddin Faridah Hanim , Zakaria Nur Mustakiza
Published: May 2022

The aim of the current study was to examine passengers’ perceived satisfaction with a monorail system by determining the factors that dominated their perception. For this study, 417 data points were collected through face-to-face questionnaire surveys in Kuala Lumpur, Malaysia, from 20 September 2019 to 10 December 2019. The study involved the use of several assessments to ascertain how the perceived satisfaction of passengers with the monorail service was influenced. The tests predicted which service factors fuelled this satisfaction and included an exploratory factor analysis, Spearman’s correlation test and an artificial neural network (ANN) model (termed the multilayer perceptron neural networks model) with a feed-forward backpropagation algorithm. The findings that were produced by these methods of analysing factors revealed the extraction of eight service-quality features as the key influences on the perceived satisfaction of passengers. The correlation test results revealed that these factors have a significant and positive relationship with perceived satisfaction. Finally, the ANN model with the optimum neuron number in the hidden layer is seven neurons. This model found that the dominant service quality factors that contributed to influencing passengers’ perceived satisfaction levels were (i) the provision of information, (ii) facilities, (iii) signage. The results of this study will benefit service providers, policymakers and planners in formulating effective strategies to enhance passengers’ satisfaction with the monorail service and increase the ridership.
Integrated Door-to-Door Transport Services for Air Passengers: From Intermodality to Multimodality
Authors: Babić Danica , Kalić Milica , Janić Milan , Dožić Slavica , Kukić Katarina
Published: May 2022

Intermodal and multimodal door-to-door journeys refer to the usage of various transport modes (air, rail, bus, road or maritime) by the traveler to complete a single journey. The main difference between these two approaches is that multimodal transport is executed under a single transport contract (a single ticket) between the passenger, on the one hand, and transport operators, on the other hand. The benefits of this type of service are reflected in the potential to save time and money. Such systems would make the transport sector greener and more sustainable, promote growth and reduce carbon emissions. The purpose of this paper is to define the concept of an air passenger multimodal transport system, identify factors and challenges that determine such a system’s development within Europe and to provide recommendations and directions for future research. The research carried out so far has indicated that market segmentation and transport system characteristics, as well as economic, social and political factors, have direct impacts on system development. This paper provides the basis for introducing single ticket, timetable synchronization and data sharing services, as well as the need to update the related regulations in order to move towards air passenger multimodality in both research and practice.
Digital Economy, R&D Investment, and Regional Green Innovation—Analysis Based on Provincial Panel Data in China
Authors: Dai Debao , Fan Yaodong , Wang Guangyu , Xie Jiaping
Published: May 2022

The digital economy plays an important role in promoting regional green innovation. Based on the panel data of 30 provincial administrative regions in mainland China (except Tibet) from 2011 to 2018, this paper constructs a comprehensive development index of the digital economy and explores the potential path of the digital economy affecting regional green innovation through factor analysis and regression analysis. The results show the following: Firstly, the digital economy can effectively promote regional green innovation capability. The causal relationship is mainly realized through scientific research funds and human resources. Secondly, in terms of regional heterogeneity, the role of the digital economy in promoting green innovation and R&D investment in eastern China is stronger than that in central and western China. Thirdly, further analysis showed that the digital economy has a significant nonlinear influence on regional green innovation capability. This feature is mainly reflected in the influence of R&D personnel on regional green innovation. Therefore, the rational allocation of R&D resources is conducive to the development of regional green innovation. Finally, it is suggested to improve the two mechanisms of R&D funds and personnel investment to actuate regional green innovation development.
How Do Environmental Knowledge, Eco-Label Knowledge, and Green Trust Impact Consumers’ Pro-Environmental Behaviour for Energy-Efficient Household Appliances?
Authors: Hossain Imran , Nekmahmud Md. , Fekete-Farkas Maria
Published: May 2022

Pro-environmental behaviour (PEB) helps individuals to minimize the negative effects of their actions on the environment; for example, by recycling and reducing energy consumption. This study aimed to explore consumers’ attitudes and trust in energy-efficient household appliances and their environmental and eco-label knowledge to determine how these may affect pro-environmental behaviour (PEB). In addition, the study examined the mediating effects of attitude and green trust on the proposed frameworks of environmental and eco-label knowledge in relation to consumer pro-environmental behaviour. PLS-SEM (Partial Least Squares–Structural Equation Modelling) was applied to a study from Bangladesh to examine hypotheses proposed from the data generated by 1510 valid respondents relating to their experiences of using household energy-efficient products. The results showed that environmental knowledge, eco-label knowledge, attitude, and green trust all significantly correlated with consumer pro-environmental behaviour. In addition, attitude and green trust significantly mediated the relationship between environmental knowledge, eco-label knowledge, and pro-environmental behaviour. This study also found that green trust positively affects consumer attitudes, and that those attitudes serve as a significant mediator between green trust and pro-environmental behaviour. An IPMA (importance-performance matrix analysis) map indicated that the total effects of environmental attitude and eco-label knowledge were more important to defining the pro-environmental behaviour of consumers than those of environmental knowledge and green trust. To the best of the authors’ knowledge, this research introduces for the first time an empirical survey that provides a new theoretical framework for consumer pro-environmental behaviour in regard to energy-efficient household appliances in the context of a developing country. Suggestions are provided on ways to promote energy-saving appliances from theoretical and practical perspectives.
Logistics 5.0 Implementation Model Based on Decision Support Systems
Authors: Trstenjak Maja , Opetuk Tihomir , Đukić Goran , Cajner Hrvoje
Published: May 2022

With the introduction of Society 5.0 for the sustainable future, special caution is given to humans’ role within the general system. Similarly, Industry 5.0 as a concept has been presented, followed by Logistics 5.0 in its theoretical framework. The transition towards the new concept of Logistics 5.0 requires an accurate and optimal strategic plan definition for which, in this paper, an implementation model based on decision support systems will be developed. The output data from this model are the priority of Logistics 5.0 elements (from five groups—green warehousing, green transport, green packaging, infrastructure and organization, and human resources) for the optimal implementation, based on three goals (initial investment, return of investment time, implementation and exploitation complexity) that companies aim to achieve in the future. The model is based on the analytic hierarchy process, and data were collected from an expert group and analyzed with several statistical methods. The result is a model that provides an optimal strategy for the implementation of elements of Logistics 5.0. The implementation priority list of elements is very beneficial for the management of many companies from various types of industries.
Exploring the Evolution of the Topics and Research Fields of Territorial Development from a Comprehensive Bibliometric Analysis
Authors: Galeano-Barrera Claudia Jazmín , Arango Ospina María Eugenia , Mendoza García Edgar Mauricio , Rico-Bautista Dewar , Romero-Riaño Efrén
Published: May 2022

Countries with great deficiencies in development, research, and innovation are investing resources to advance in this aspect; meanwhile, it is necessary to advance in initiatives that promote local development, through the correct use and management of endogenous territorial capacities to achieve economic, social, and environmental development, and this is where the territorial development approach has intervened during the last decades. To obtain an understanding of the evolution of the research field on territorial development, a study of research topics and groups of research topics is implemented from subscription-based data sources (Scopus, Science direct, Ebsco, and Web of Science) and open access (Lens and Dimension platforms). Keyword co-occurrence techniques were implemented, and indicators of link strength and density-centrality of clusters were used to abstract patterns of change within the field of study. The evolution of the area, trends, and issues related to territorial development are identified in the different databases, mapping a discipline that still lacks comparative publications between research results at different scales.
Does the Environmental Information Disclosure Promote the High-Quality Development of China’s Resource-Based Cities?
Authors: Xin Chunhua , Lai Xiufeng
Published: May 2022

The high-quality development (HQD) of resource-based cities (RBCs) is the premise on which to ensure the healthy, stable, and sustainable development of China’s economy. In this study, we use the global Malmquist–Luenberger index based on the slacks-based measure of directional distance function (SBM-DDF-GML index), which is an improved data envelopment analysis (DEA) model of the non-radial and non-oriented, to calculate the HQD level of 102 RBCs in China from 2003 to 2019. Then, we empirically evaluate the effect of environmental information disclosure (EID) on HQD improvement in RBCs by adopting the method of time-varying difference-in-difference with propensity score matching (PSM-DID) and investigate the heterogeneous effects of EID. Additionally, the mediating effect model is employed to explore the impact mechanisms of EID on the HQD. The results show that: (1) EID has a significant and positive effect on the HQD of RBCs, and this conclusion is still valid after a series of robustness tests. (2) EID plays a more effective role in the promotion of HQD in central RBCs, resource strong-dependent RBCs, growth RBCs, and regenerative RBCs than in other types of cities. (3) EID promotes the HQD of RBCs through the environmental pollution reduction effect and the industrial structure upgrading effect. These findings enrich the content of the relationship between EID and the HQD and present a feasible path for RBCs in China to achieve the HQD through environmental governance.
Climate Change and Seed System Interventions Impact on Food Security and Incomes in East Africa
Authors: Otieno Gloria , Ogola Robert John O , Recha Tobias , Mohammed Jemal Nurhisen , Fadda Carlo
Published: May 2022

Climate change is challenging agricultural productivity, especially in Africa. Adoption of improved or diverse seed varieties is a promising strategy to achieve increased yields, support food security and reduce poverty under climate change in East Africa. However, rigorous impact evaluations linking the contributions of improved seeds to the welfare of households have been limited. This paper evaluates the impact of diversified seed systems on farm household production, sales, income, consumption and seed storage in Kenya and Uganda. It applies four-cell analysis to explore the intra-specific diversity of crops within farming systems, using primary data obtained from a random sampling of 207 treatment households and 87 control households. Propensity score matching was used to investigate the relationship between adoption of improved seeds and changes in production, sales, income, consumption, seed storage and food security. Econometric results indicate that treatment households using improved seeds saw a significant positive impact on income from bean seed sales, sorghum and millet consumption, bean livestock feed and maize and millet seed stored. We conclude that increasing seed diversity helps farmers cope with climate change and increases productivity, food availability, incomes and food security. Partnerships among seed improvement stakeholders need to be enhanced to ensure a continued supply of appropriate seeds to farmers.
Financial-Economic Analysis of Cultural Companies in Small and Medium-Sized Cities in Spain
Authors: Cea-D’Ancona Francisca , Sáez-Cala Antonia , Palacios Antonio
Published: May 2022

Cultural enterprises are today an important component in the development processes of many territories. In urban centers—particularly those located within metropolitan areas—cultural companies constitute a main element of the economy, due to their ability to activate new production chains and revitalize local regions. The objective of this work is to examine the traditional cultural sector (subsectors 18, 59, 60, 90 and 91 of NACE-09) of small and medium-sized cities from the perspective of the financial conditions of the particular companies involved. The use of multivariate techniques for reducing dimensions enables the specification of key factors within the financial structures of companies to assemble homogeneous groups of firms, in order to then determine which factors influence their profitability in both intra- and extra-metropolitan contexts. The results of the applied method indicate heterogeneity in the financial situations of these cultural industries in the activities and cities analyzed. Differences are marked in the financial profiles of cultural companies in intra-metropolitan environments in terms of business profitability, debt quality, financial stability, investment capacity, and costs of personnel. On the other hand, the economic-financial structures of companies in extra-metropolitan cities are determined through efficiency in the allocation of economic-financial resources, profit margins, and investment capacity. Belonging to one cultural sub-sector or another and a particular location within an intra- or extra-metropolitan urban environment also play some part in a company’s economic and financial structure. Hence there is a need to differentiate the design of intervention strategies (instruments) aimed at promoting the system/environment to which the company belongs as well as the company itself.
Characteristics of Hydro-Geochemistry and Groundwater Pollution in Songnen Plain in Northeastern China
Authors: Chen Ruihui , Liu Linmei , Li Yi , Zhai Yuanzheng , Chen Haiyang , Hu Bin , Zhang Qianru , Teng Yanguo
Published: May 2022

Agricultural production may cause groundwater pollution. This study investigated the characteristics of shallow groundwater pollution in a typical black land agricultural production area in Northeastern China and the geochemical behavior of major pollutants. A total of 27 and 23 shallow groundwater samples were collected for measuring on-site parameters and major components in 2000 and 2014, respectively. The improved integrated approach was used to assess groundwater contamination. The results showed that the groundwater was slightly polluted by agricultural activities. The average concentrations of major ions of shallow groundwater were found to be in the following order: Ca2+ > Na+ > Mg2+ > K+ for cations and HCO3− > SO42− > Cl− > NO3− for anions. Percentages of 7.4% and 34.8% of the total groundwater sample in 2000 and 2014, respectively, indicated that the shallow groundwater quality has gradually worsened in the past few decades. The concentration of NO3− was a major factor that influenced the observed groundwater quality changes. Scientific and effective fertilization of rice cultivation is an effective way to avoid groundwater pollution, and the improved groundwater quality evaluation methods can further improve the standard of groundwater resource management effectively.
Sustainable Indicators for Integrating Renewable Energy in Bahrain’s Power Generation
Authors: Alabbasi Abdulla , Sadhukhan Jhuma , Leach Matthew , Sanduk Mohammed
Published: May 2022

The selection of sustainable indicators is crucial in measuring and understanding the required targets within the theme of sustainability for an energy system. This is because sustainability, as a term, is used in several fields and covers a variety of indicators based on the problem’s context and identity. Each researcher looks at sustainability from their own perspective and selects the indicators which align best with their objectives and their understanding of the topic. This paper aims to implement a systematic approach to choosing the sustainable indicators for Bahrain’s electrical production with renewables. The proposed framework analyses the frequency of indicators in a sample of 73 studies and screens them in accordance with the selection principles and experts’ views. The results reveal 15 indicators with strong relevance to sustainable growth for the power sector with renewables. These indicators are classified as either qualitative or quantitative, depending on our case study’s context and the appropriate practice according to the literature. Finally, each of the selected indicators was defined to reflect its intended purpose in our study, since the common practice within the present literature is to provide such indicators without explaining their actual purpose.