bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm
Authors: Qian Chunhua , Qiang Hequn , Wang Feng , Li Mingyang
Published: December 2021

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.
SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
Authors: Xiong Pan , Marchetti Dedalo , De Santis Angelo , Zhang Xuemin , Shen Xuhui
Published: December 2021

Low Earth orbit satellites collect and study information on changes in the ionosphere, which contributes to the identification of earthquake precursors. Swarm, the European Space Agency three-satellite mission, has been launched to monitor the Earth geomagnetic field, and has successfully shown that in some cases it is able to observe many several ionospheric perturbations that occurred as a result of large earthquake activity. This paper proposes the SafeNet deep learning framework for detecting pre-earthquake ionospheric perturbations. We trained the proposed model using 9017 recent (2014–2020) independent earthquakes of magnitude 4.8 or greater, as well as the corresponding 7-year plasma and magnetic field data from the Swarm A satellite, and excellent performance has been achieved. In addition, the influence of different model inputs and spatial window sizes, earthquake magnitudes, and daytime or nighttime was explored. The results showed that for electromagnetic pre-earthquake data collected within a circular region of the epicenter and with a Dobrovolsky-defined radius and input window size of 70 consecutive data points, nighttime data provided the highest performance in discriminating pre-earthquake perturbations, yielding an F1 score of 0.846 and a Matthews correlation coefficient of 0.717. Moreover, SafeNet performed well in identifying pre-seismic ionospheric anomalies with increasing earthquake magnitude and unbalanced datasets. Hypotheses on the physical causes of earthquake-induced ionospheric perturbations are also provided. Our results suggest that the performance of pre-earthquake ionospheric perturbation identification can be significantly improved by utilizing SafeNet, which is capable of detecting precursor effects within electromagnetic satellite data.
Evaluating Image Normalization via GANs for Environmental Mapping: A Case Study of Lichen Mapping Using High-Resolution Satellite Imagery
Authors: Jozdani Shahab , Chen Dongmei , Chen Wenjun , Leblanc Sylvain G. , Lovitt Julie , He Liming , Fraser Robert H. , Johnson Brian Alan
Published: December 2021

Illumination variations in non-atmospherically corrected high-resolution satellite (HRS) images acquired at different dates/times/locations pose a major challenge for large-area environmental mapping and monitoring. This problem is exacerbated in cases where a classification model is trained only on one image (and often limited training data) but applied to other scenes without collecting additional samples from these new images. In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one area to a source WV2 image of another area on which a lichen detector model was trained. In this regard, we considered an extreme case where the classifier was not fine-tuned on the normalized images. We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2 panchromatic band and Sentinel-2 surface reflectance (SR) imagery. Our experiments showed that normalizing even based only on the WV2 panchromatic band led to a significant lichen-detection accuracy improvement compared to the use of original pansharpened target images. However, we found that conditioning the cGAN on both the WV2 panchromatic band and auxiliary information (in this case, Sentinel-2 SR imagery) further improved normalization and the subsequent classification results due to adding a more invariant source of information. Our experiments showed that, using only the panchromatic band, F1-score values ranged from 54% to 88%, while using the fused panchromatic and SR, F1-score values ranged from 75% to 91%.
Detecting Phenological Development of Winter Wheat and Winter Barley Using Time Series of Sentinel-1 and Sentinel-2
Authors: Harfenmeister Katharina , Itzerott Sibylle , Weltzien Cornelia , Spengler Daniel
Published: December 2021

Monitoring the phenological development of agricultural plants is of high importance for farmers to adapt their management strategies and estimate yields. The aim of this study is to analyze the sensitivity of remote sensing features to phenological development of winter wheat and winter barley and to test their transferability in two test sites in Northeast Germany and in two years. Local minima, local maxima and breakpoints of smoothed time series of synthetic aperture radar (SAR) data of the Sentinel-1 VH (vertical-horizontal) and VV (vertical-vertical) intensities and their ratio VH/VV; of the polarimetric features entropy, anisotropy and alpha derived from polarimetric decomposition; as well as of the vegetation index NDVI (Normalized Difference Vegetation Index) calculated using optical data of Sentinel-2 are compared with entry dates of phenological stages. The beginning of stem elongation produces a breakpoint in the time series of most parameters for wheat and barley. Furthermore, the beginning of heading could be detected by all parameters, whereas particularly a local minimum of VH and VV backscatter is observed less then 5 days before the entry date. The medium milk stage can not be detected reliably, whereas the hard dough stage of barley takes place approximately 6–8 days around a local maximum of VH backscatter in 2018. Harvest is detected for barley using the fourth breakpoint of most parameters. The study shows that backscatter and polarimetric parameters as well as the NDVI are sensitive to specific phenological developments. The transferability of the approach is demonstrated, whereas differences between test sites and years are mainly caused by meteorological differences.
Simulation of External Stray Light for FY-3C VIRR Combined with Satellite Orbit Attitude Model
Authors: Zhao Junyuan , He Yuqing , Hu Xiuqing , Jin Weiqi , Zhang Lijun , Zhang Dan
Published: December 2021

Optical imaging systems mounted on a Sun-synchronous satellite are probably disturbed by stray light when working in the space environment. Existing research has shown that the Visible Infrared Radiometer (VIRR) onboard the FY-3C satellite is affected by external solar stray light radiation when imaging the ground. In this paper, based on analyzing the solar stray light generation mechanism, we propose a simulation and analysis method combined with the given satellite orbit attitude model to investigate the influence of external solar stray light on VIRR’s imaging quality. We use the FY-3C orbit parameters to obtain the variation pattern of the angles between the solar vector and the payload. Based on the VIRR mechanical structure and optical scattering model, light tracing is performed to investigate the spatial distribution of irradiation on the primary mirror. The results of the occurrence time and intensity of stray light obtained by the simulation are consistent with the actual data when imaging the ground, which verifies that the proposed method is a correct and effective way to investigate the regularity of the external stray light of on-orbit payload.
High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning
Authors: Li Xianghua , Hou Jinliang , Huang Chunlin
Published: December 2021

Accurate high-resolution gridded livestock distribution data are of great significance for the rational utilization of grassland resources, environmental impact assessment, and the sustainable development of animal husbandry. Traditional livestock distribution data are collected at the administrative unit level, which does not provide a sufficiently detailed geographical description of livestock distribution. In this study, we proposed a scheme by integrating high-resolution gridded geographic data and livestock statistics through machine learning regression models to spatially disaggregate the livestock statistics data into 1 km × 1 km spatial resolution. Three machine learning models, including support vector machine (SVM), random forest (RF), and deep neural network (DNN), were constructed to represent the complex nonlinear relationship between various environmental factors (e.g., land use practice, topography, climate, and socioeconomic factors) and livestock density. By applying the proposed method, we generated a set of 1 km × 1 km spatial distribution maps of cattle and sheep for western China from 2000 to 2015 at five-year intervals. Our projected cattle and sheep distribution maps reveal the spatial heterogeneity structures and change trend of livestock distribution at the grid level from 2000 to 2015. Compared with the traditional census livestock density, the gridded livestock distribution based on DNN has the highest accuracy, with the determinant coefficient (R2) of 0.75, root mean square error (RMSE) of 9.82 heads/km2 for cattle, and the R2 of 0.73, RMSE of 31.38 heads/km2 for sheep. The accuracy of the RF is slightly lower than the DNN but higher than the SVM. The projection accuracy of the three machine learning models is superior to those of the published Gridded Livestock of the World (GLW) datasets. Consequently, deep learning has the potential to be an effective tool for high-resolution gridded livestock projection by combining geographic and census data.
From Local to Global: Class Feature Fused Fully Convolutional Network for Hyperspectral Image Classification
Authors: Liu Qian , Wu Zebin , Jia Xiuping , Xu Yang , Wei Zhihui
Published: December 2021

Current mainstream networks for hyperspectral image (HSI) classification employ image patches as inputs for feature extraction. Spatial information extraction is limited by the size of inputs, which makes networks unable to perform effective learning and reasoning from the global perspective. As a common component for capturing long-range dependencies, non-local networks with pixel-by-pixel information interaction bring unaffordable computational costs and information redundancy. To address the above issues, we propose a class feature fused fully convolutional network (CFF-FCN) with a local feature extraction block (LFEB) and a class feature fusion block (CFFB) to jointly utilize local and global information. LFEB based on dilated convolutions and reverse loop mechanism can acquire the local spectral–spatial features at multiple levels and deliver shallower layer features for coarse classification. CFFB calculates global class representation to enhance pixel features. Robust global information is propagated to every pixel with low computational cost. CFF-FCN considers a fully global class context and obtains more discriminative representation by concatenating high-level local features and re-integrated global features. Experimental results conducted on three real HSI data sets demonstrate that the proposed fully convolutional network is superior to multiple state-of-the-art deep learning-based approaches, especially in the case of a small number of training samples.
Observations on Earthworm Communities and Soils in Various Natural and Man-Affected Ecosystems
Authors: Chalkia Christina , Vavoulidou Evangelia , Csuzdi Csaba , Emmanouil Christina , Dritsoulas Alexandros , Katsileros Anastasios
Published: December 2021

Earthworms are important members of the soil fauna since they regulate fundamental soil functions such as organic matter breakdown, fertility, structure and water balance. However, so far, their community assemblages have been poorly investigated in Greece. In this context, the earthworm communities of forty five sites in Greece, belonging to three types of ecosystems—terrestrial water bodies, undisturbed (natural) sites and cultivated (agricultural and urban) fields—were investigated using the combined method of digging and hand sorting followed by the application of a 0.4% formaldehyde solution. Specific soil parameters and various environmental characteristics were examined as potential factors affecting the abundance and species richness. The results showed no statistically significant difference between ecosystem densities. The species number was significantly different between ecosystems, with the cultivated fields exhibiting richer communities, with a mean of 5.3 ± 0.6 species per site compared to the natural areas with 2.4 ± 0.5 species per site, while the water bodies showed intermediate numbers (3.6 ± 0.5 species per site). Finally, earthworm densities were positively correlated with species number and percentage vegetation cover and negatively with clay. These results may contribute to understanding how different land uses affect earthworm communities.
Annual Baseline King-Devick Oculomotor Function Testing Is Needed Due to Scores Varying by Age
Authors: Gallagher Dearbhla , King Doug , Hume Patria , Clark Trevor , Pearce Alan , Gissane Conor
Published: December 2021

Objective: To document baseline King-Devick (K-D) oculomotor function scores for male and female participants aged between 4 and 20 years old. Methods: Utilising a cross section of schools, rugby clubs and gymnastic clubs, 1936 participants (1300 male, 636 female) completed the spiral-bound K-D test for the identification of disturbed oculomotor function. Results: This study identified that overall, the baseline scores of the K-D test became faster by 1.4 (0.3 to 4.5) s per year, when compared with the previous age group in the same number of reading card groups. When comparing normative values of the original K-D validation study with the same age groups of the current cohort, participants aged 6 to 11 years recorded a faster baseline time (range 3.5 to 8.6 s), while those in the 12 to 14 years. age group recorded slower baseline times (range −3.9 to −7.9 s). Discussion: In general, there were age group differences, but not sex differences, for K-D test times in the current cohort. Analysis of single card times, across all age groups, showed changes likely due to improved reading time. Conclusion: The results support the need for individualised annual pre-injury baseline testing of the K-D test.
Born or Grown: Enablers and Barriers to Circular Business in Europe
Authors: Briguglio Marie , Llorente-González Leandro J. , Meilak Christopher , Pereira Ángeles , Spiteri Jonathan , Vence Xavier
Published: December 2021

Circular economy goals have made their way towards the very heart of EU policy, promising the delivery of both economic and environmental goals, but key to their achievement is the active involvement and participation of businesses. Scholarly literature has made considerable headway in describing the diverse CE business model archetypes and the enablers and barriers that can nurture the transition toward them. However, little work has been done to assess a more profound distinction—that between enterprises that are born circular in contrast with incumbent businesses that grow into circularity. We review 18 case studies of businesses in Europe, which shed light on this distinction. A systematic analysis of their internal, contextual, and policy issues results in the identification of ten key enablers (including business targets, cost reduction potential, loyal customers, demographic aspects, growing waste flows, environmentalism, EU policy, circular policy, and dis/incentives as well as sectoral considerations) and ten key barriers (including bottom line concerns, problematic consumer preferences, lack of infrastructure, technological barriers, poor access to finance, competition, lack of EU harmonisation, uncertainty and lack of internalisation of externalities, and the presence of obstructive policy). We observe that businesses which are born circular seem to face fewer barriers than those seeking to grow into circularity, a finding which offers hope for the transition to a circular economy. Our analysis also suggests that while some enablers and barriers cut across different types of businesses, others tend to be more prevalent among enterprises of a certain size or sector.
Fabrication and Performance of Low-Fouling UF Membranes for the Treatment of Isolated Soy Protein Solutions
Authors: Garcia-Castello Esperanza M. , Rodriguez-Lopez Antonio D. , Barredo-Damas Sergio , Iborra-Clar Alicia , Pascual-Garrido Jairo , Iborra-Clar Maria Isabel
Published: December 2021

Consumers are becoming more conscious about the need to include functional and nutritional foods in their diet. This has increased the demand for food extracts rich in proteins and peptides with physiological effects that are used within the food and pharmaceutical industries. Among these protein extracts, soy protein and its derivatives are highlighted. Isolated soy protein (ISP) presents a protein content of at least 90%. Wastewaters generated during the production process contain small proteins (8–50 kDa), and it would be desirable to find a recovery treatment for these compounds. Ultrafiltration membranes (UF) are used for the fractionation and concentration of protein solutions. By the appropriate selection of the membrane pore size, larger soy proteins are retained and concentrated while carbohydrates and minerals are mostly recovered in the permeate. The accumulation and concentration of macromolecules in the proximity of the membrane surface generates one of the most important limitations inherent to the membrane technologies. In this work, three UF membranes based on polyethersulfone (PES) were fabricated. In two of them, polyethylene glycol (PEG) was added in their formulation to be used as a fouling prevention. The membrane fouling was evaluated by the study of flux decline models based on Hermia’s mechanisms.
Performance Optimization and Economic Evaluation of CO2 Heat Pump Heating System Coupled with Thermal Energy Storage
Authors: Wang Zhihua , Zhang Yujia , Wang Fenghao , Li Guichen , Xu Kaiwen
Published: December 2021

CO2 air source heat pump (ASHP), as a kind of clean and efficient heating equipment, is a promising solution for domestic hot water and clean heating. However, the promotion of CO2 ASHP encounters a great resistance when it is used for space heating; namely, the return water temperature is too high that cased higher throttle loss, which decreases the COP of the CO2 ASHP unit. To solve this problem, a heating system of CO2 ASHP coupled with thermal energy storage (TES) is developed in this work. The simulation model of the studied system is established using TRNSYS software, and the model is verified by experimental data. Additionally, the performance of the studied system is optimized, and its economy is analyzed by life cycle cost (LCC). The results showed that, compared with the system before optimization, the cost of the optimized system increased, the annual operating cost of the system was reduced, and the COP of the system (COPsys) increased by 7.4%. This research is helpful in improving the application of the CO2 ASHP unit in cold server and cold areas.
Solar Photovoltaic Power Prediction Using Big Data Tools
Authors: Arias Mariz B. , Bae Sungwoo
Published: December 2021

Solar photovoltaic (PV) installation has been continually growing to be utilized in a grid-connected or stand-alone network. However, since the generation of solar PV power is highly variable because of different factors, its accurate forecasting is critical for a reliable integration to the grid and for supplying the load in a stand-alone network. This paper presents a prediction model for calculating solar PV power based on historical data, such as solar PV data, solar irradiance, and weather data, which are stored, managed, and processed using big data tools. The considered variables in calculating the solar PV power include solar irradiance, efficiency of the PV system, and characteristics of the PV system. The solar PV power profiles for each day of January, which is a summer season, were presented to show the variability of the solar PV power in numerical examples. The simulation results show relatively accurate forecasting with 17.57 kW and 2.80% as the best root mean square error and mean relative error, respectively. Thus, the proposed solar PV power prediction model can help power system engineers in generation planning for a grid-connected or stand-alone solar PV system.
Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion
Authors: Mohammed Marwan Qaid , Kwek Lee Chung , Chua Shing Chyi , Aljaloud Abdulaziz Salamah , Al-Dhaqm Arafat , Al-Mekhlafi Zeyad Ghaleb , Mohammed Badiea Abdulkarem
Published: December 2021

In robotic manipulation, object grasping is a basic yet challenging task. Dexterous grasping necessitates intelligent visual observation of the target objects by emphasizing the importance of spatial equivariance to learn the grasping policy. In this paper, two significant challenges associated with robotic grasping in both clutter and occlusion scenarios are addressed. The first challenge is the coordination of push and grasp actions, in which the robot may occasionally fail to disrupt the arrangement of the objects in a well-ordered object scenario. On the other hand, when employed in a randomly cluttered object scenario, the pushing behavior may be less efficient, as many objects are more likely to be pushed out of the workspace. The second challenge is the avoidance of occlusion that occurs when the camera itself is entirely or partially occluded during a grasping action. This paper proposes a multi-view change observation-based approach (MV-COBA) to overcome these two problems. The proposed approach is divided into two parts: 1) using multiple cameras to set up multiple views to address the occlusion issue; and 2) using visual change observation on the basis of the pixel depth difference to address the challenge of coordinating push and grasp actions. According to experimental simulation findings, the proposed approach achieved an average grasp success rate of 83.6%, 86.3%, and 97.8% in the cluttered, well-ordered object, and occlusion scenarios, respectively.
Transformation of the Business Process Manager Profession in Poland: The Impact of Digital Technologies
Authors: Pilipczuk Olga
Published: December 2021

The increasing role of emerging technologies, such as big data, the Internet of Things, artificial intelligence (AI), cognitive technologies, cloud computing, and mobile technologies, is essential to the business process manager profession’s sustainable development. Nevertheless, these technologies could involve new challenges in labor markets. The era of intelligent business process management (BPM) has begun, but how does it look in real labor markets? This paper examines the hypothesis that the transformation of the business process manager profession has been caused by certain determinants that involve the need for an improvement in BPM skills. The main contribution is a model of the dimensions of the impact of digital technologies on business process management supplemented with skills that influence the business process manager profession. The paper fills the gap in research on perspectives of the impact of digital technologies on business process management, considering both a literature analysis and labor market research. The purpose of the literature review was to identify the core dimensions that drive the use of emerging technologies in business process management. The labor market study was conducted in order to analyze the current demand for core skills of business process managers in the Polish labor market with a particular emphasis on the intelligent BPM concept. Additionally, to study the determinants that slow down the iBPM concept’s development, the digital intensity level of the enterprises and public administration units in Poland was studied. Finally, a fuzzy cognitive map presenting the core determinants of the business process manager profession’s transformation is described.
Marginal Abatement Cost of Carbon Emissions under Different Shared Socioeconomic Pathways
Authors: Liu Na , Song Futie
Published: December 2021

Future emissions scenarios have served as a primary basis for assessing climate change and formulating climate policies. To explore the impact of uncertainty in future emissions scenarios on major outcomes related to climate change, this study examines the marginal abatement cost (MAC) of carbon emissions under the latest Shared Socioeconomic Pathways (SSPs) subject to the economic optimum and the 1.5 °C temperature increase constraint using the Epstein-Zin (EZ) climate model. Taking the “Regional Rivalry” (SSP3) scenario narrative under the economic optimum as a representative case, the expected MACs per ton CO2 equivalent (CO2e) emissions in the years 2015, 2030, 2060, 2100, and 2200 are: $102.08, $84.42, $61.19, $10.71, and $0.12, respectively. In parallel, the associated expected average mitigation rates (AMRs) are 0%, 63%, 66%, 81%, and 96%, respectively. In summary, in a world developing towards regional rivalry (SSP3) or fossil-fueled development (SSP5) with high mitigation pressure, the MAC values have approximately doubled, compared with the sustainability (SSP1) and inequality (SSP4) storylines with low mitigation pressure levels. The SSP2 (Middle of the Road) shows a moderate MAC decreasing trend with moderate mitigation pressure. The results provide a carbon price benchmark for policy makers with different attitudes towards the unknown future and can be used to formulate carbon mitigation strategy to respond to specific climate goals.
Analyzing Cost and Schedule Growths of Road Construction Projects, Considering Project Characteristics
Authors: Lee Kang-Wook , Kim Kyong-Hoon
Published: December 2021

The development of road infrastructure is closely related to national competitiveness and presents significant socioeconomic impacts. However, road construction involves a large budget and is vulnerable to political, economic, social, and project-specific risks, which often result in cost overruns and schedule delays. Assessing the gap between the final performance and the planned performance, and providing feedback to similar projects in the future is essential for successful project planning and management. The aim of this study is to empirically analyze the cost and schedule growth of road construction projects, considering project characteristics. Using the national-level project performance data, the primary goal is to answer, “Do project characteristics influence the road project performance? If so, how different is the performance because of the project characteristics?” To this end, this study analyzes the cost and schedule growth of 423 road construction projects, considering five project characteristics: facility type, construction type, bid type, contract type, and project size. Non-parametric tests (the Mann–Whitney U test and the Kruskal–Wallis test) are used to analyze the differences between sample groups. The results demonstrate (1) better management of the performance of the highway when compared to the national and provincial roads; (2) higher schedule growth of the expansion and renovation than that of the new construction; (3) lower cost growth of the design-build method (turnkey and alternative) than the design-bid-build methods (qualification examination and lowest price); and (4) relatively larger cost and schedule growth for projects over $50 million than those of smaller projects. These results present empirical references from the Korean construction industry that can help construction-related entities (clients, design consultants, and contractors) to estimate and manage the cost and schedule buffers of future projects by considering different project characteristics. Discussions and suggestions connected with the findings are also provided. Future research will continue to shed light on the critical factors affecting the cost and schedule growth.
Study on Increase in Stability of Floating and Underground Extension Method through Slab Pre-Construction
Authors: Kim Uiseok , Min Byungchan , Kim Junghoon , Choi Hangseok , Park Sangwoo
Published: December 2021

The development of underground spaces in urban areas plays a crucial role in the regeneration and sustainability of cities. However, the conventional underground excavation works in metropolises limit the use of the ground facilities owing to stability, noise, and vibration problems, which may cause huge economic damage. In this study, a method of pre-constructing slabs of underground facilities was analyzed to improve the stability of the floating and underground extension method, even during the use of the target building. First, a numerical simulation was conducted to compare the stability of the top-down method with slab pre-construction with that of the conventional top-down method. Then, the stability of the test bed was checked by applying this construction method to the field. As a result, the top-down method with slab pre-construction significantly improved the stability of the target building by preventing the buckling of the columns and supporting members. The increase in the displacement of existing columns and supporting members was controlled after the pre-construction of the slab. In addition, the crack width and ground settlement were stable within the management standards at the field. Thus, this construction method is expected to be crucial in pursuing urban regeneration and sustainability through the efficient development of underground spaces.
The Analysis of the Ski Slopes and the Degree of Economic Dependence Induced by Winter Sports Tourism. The Case of Romania
Authors: Herman Grigore Vasile , Grama Vasile , Buhaș Sorin , Garai Lavinia Daiana , Caciora Tudor , Grecu Alexandra , Gruia Andreea Karina , Hudea Oana Simona , Peptenatu Daniel
Published: December 2021

Winter sports are the main attraction for many tourist areas in Romania, contributing significantly to the development of local economies. Based on this, the study aims to analyze the ski areas in Romania, as well as the extent to which they contribute to the sustainable development of the local economy. This is particularly important as, in recent decades, climate change has significantly affected winter sports, especially skiing. Thus, an analysis of the physical characteristics of ski runs in Romania (number, length, width, level difference and slope) is accompanied with an analysis on the dynamics of the share of tourism in the local economy of winter sport locations, based on tourism turnover relative to total turnover in the local economy. Both graphic and cartographic methods were used in this study, based on an analysis of quantitative and qualitative data available for ski slopes in Romania and the host localities. We used ArcGIS 10.6 software for the preparation of graphical representations and other software to process large databases. The research results showed a great diversity regarding counties, localities and ski slopes depending on the characteristic features of ski slopes (number, length, width, level difference and slope). In our study, the evolution of the share of turnover in tourism in terms of total turnover presented several categories of localities based on their economic dependence on winter sports; the impact in this regard was found to be very differentiated between localities.
Addressing Water Security: An Overview
Authors: Marcal Juliana , Antizar-Ladislao Blanca , Hofman Jan
Published: December 2021

For the last two decades, water security has been in the spotlight as a key concept for sustainable development. However, due to its wide interpretation range, the understanding of what water security is and what it encompasses can change considerably with different perspectives. By means of a review of both academic and grey literature, this paper presents an in-depth global overview of what water security means and how assessment is being carried out. These aspects are put together in the present work with the aim to facilitate access to this complex concept for academics but also policymakers and other stakeholders involved in water management and governance. Aiming to provide a groundwork for water security understanding, we examine definitions, scales of application, frequent approaches and methodologies used to study water security. We also present indicators and aspects being included in water security frameworks. A summary of important actions towards water security improvement is also presented. As a dynamic and multi-faceted concept, water security requires an equally multi-dimensional and flexible interpretation. Understanding and measuring are key to improving water security levels. Bringing attention to how climate change, environmental needs, demographics, economics and governance are linked to water security can boost impact by prompting science, policies and innovation to come together.
The Influence of Habitat Changes on Elephant Mortality Associated with Human–Elephant Conflict: Identifying Areas of Concern in the North Central Dry Zone of Sri Lanka
Authors: LaDue Chase A. , Farinelli Sarah M. , Eranda Imira , Jayasinghe Chandana , Vandercone Rajnish P. G.
Published: December 2021

Human–wildlife conflict (HWC) is becoming increasingly prevalent as human activity expands, and monitoring the impact of habitat quality on wildlife mortality related to HWC is critical for the well-being of wildlife and people. Using ten years of necropsies from free-ranging Asian elephants in the Northwestern Wildlife Region (NWR) of Sri Lanka, we quantified the effect of habitat quality on human–elephant conflict (HEC) (i.e., human-caused elephant mortality), hypothesizing that both artificial (e.g., forest cover loss) and natural (e.g., water availability, temperature) changes would be associated with elephant mortality. We collated necropsies from 348 elephants that died due to human activity from 2009 to 2018, comparing the results with data on forest cover loss, perennial water, rainfall, temperature, and human population sizes. Over the study period, we found that forest cover loss was significantly correlated with human-caused mortality in a district-specific manner. Similarly, access to perennial water and precipitation levels appeared to influence mortality, but not temperature, human population density, or percent land cover used for agriculture. We conducted emerging hot spot analyses to identify areas within the NWR that should be prioritized for protection, which included landscapes that are not currently protected (approximately 43% of the hot spots we identified). Similarly, areas that we identified as cold spots included many areas with minimal forest cover loss. Together, our results emphasize the impact that human activity can have on the measurable outcomes of HEC. We suggest that adaptive HWC management strategies that use retrospective analyses should inform any potential changes to the protection of vital wildlife habitats, such as the north central dry zone of Sri Lanka.
The Gender and Education of Parents as Factors That Influence Their Views on Physical Education
Authors: Ružbarská Beáta , Antala Branislav , Gombár Miroslav , Tlučáková Lenka
Published: December 2021

(1) The aim of this paper was to explore the views of primary and secondary school children’s parents on physical education (PE) and to subsequently analyse the differences in their views. The gender of the parent and child was taken into consideration as well as the parents’ levels of education. (2) The research sample consisted of 821 participants 668 of which were female (81.4%) and 153 of which were male (18.6%). The views of the children’s parents were collected through a non-standardized questionnaire. The questionnaire was divided into four areas (1—attitudes towards the physical education teaching process, 2—physical activity as part of lifestyle, 3—physical education during the COVID-19 pandemic, 4—social capital), with a total of 18 items in the questionnaire. The reliability of the questionnaire was analysed using Cronbach’s coefficient reached 0.835. Confirmatory factor analysis was also used to test the predicted factor structure. (3) In general, this research found that parents were more likely to express dissatisfaction if their child was only provided with 2 PE classes per week and would accept an increase in this number. Those parents whose children do not participate in organized physical activities were shown to be unsatisfied with their children’s physical fitness. A more significant limitation of children’s physical activity (PA) was observed among parents with secondary education. Parents with secondary education also feel that their child partaking in PA when they have free time is not so important, whereas parents with a higher level of education feel that it is. (4) Parents expressed positive evaluations of PE and recognized the importance it has in their child’s education. In the questionnaire, questions were related to physical fitness and PA specifically during the COVID-19 pandemic and this research observes the differences in opinions of parents depending on the level of education they attained and the gender of their child.
Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique
Authors: Venkatesan Chandrasekaran , Kannadasan Raju , Ravikumar Dhanasekar , Loganathan Vijayaraja , Alsharif Mohammed H. , Choi Daeyong , Hong Junhee , Geem Zong Woo
Published: December 2021

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.
How Switching Costs with Process Monitoring and Operational Integration Affect Sustainable Supplier Relationship
Authors: Lee Sunghee , Park Jinsoo
Published: December 2021

Building on sustainable supply chain management and operations strategy literature, our study seeks to identify structural relationships between switching cost and sustainable supplier relationships from a demand-side perspective. More specifically, this study looks at the impact of the switching cost on process monitoring, operation integration, and sustainable supplier relationships. To test the structural relationships in our research model, we used Manufacturing Productivity Survey data from Korea to conduct an empirical analysis based on 351 data that fit our study’s purpose. The results show that the indirect effect of switching cost on sustainable supplier relationships through process monitoring and operational integration is positively valid. Additionally, the results emphasize that the social exchange theory can be explained in the perspective of the switching cost.
Wind Farm Site Selection Using WAsP Tool for Application in the Tropical Region
Authors: Kamdar Ismail , Ali Shahid , Taweekun Juntakan , Ali Hafiz Muhammad
Published: December 2021

Wind energy is one of the most promising renewable energy technologies worldwide; however, assessing potential sites for wind energy exploitation is a challenging task. This study presents a site suitability analysis to develop a small–scale wind farm in south–eastern Thailand. To this aim, the most recent available data from 2017 to 2019, recorded near the surface, at nine weather stations of the Thai Meteorological Department (TMD) were acquired. The analysis was conducted using standard wind–industry software WAsP. It was found that the mountain peaks and ridges are highly suitable for small–scale wind farm development. Nevertheless, the wind data analysis indicates that regions fall in low–to–moderate wind classes. The selected sites in south–eastern Thailand have mean wind speeds ranging from 5.1 m/s to 9.4 m/s. Moreover, annual energy production (AEP) of 102 MWh to 311 MWh could be generated using an Enercon E–18 wind turbine with a rated power of 80-kW at the hub height of 28.5 m. The Levelized Cost of Energy (LCOE) reveals that the development cost of a small–scale wind farm is lowest in the Songkhla and Yala provinces of Thailand, therefore these two locations from the investigated study region are financially most suitable. The findings could encourage researchers to further investigate low–speed wind energy mechanisms in tropical regions, and the demonstrated approach could be reused for other regions.
The Land Snail, Eobania vermiculata, as a Bioindicator of the Heavy Metal Pollution in the Urban Areas of Sulaimani, Iraq
Authors: Salih Aso H. Saeed H. , Hama Abdullah A. , Hawrami Karzan A. M. , Ditta Allah
Published: December 2021

Land snails are crucial consumers in the terrestrial environment and beneficial significant bioindicators to evaluate the chemical impact in the ecosystem, especially on urban lands. The present study aimed to investigate the concentration of heavy metals such as As, Cr, Ni, Pb, and Zn in urban soil and study whether Eobania vermiculata acts as a bioindicator for heavy metal contamination in an urban area. Thirty soil and snail samples in triplicate from each sampling site were taken from the urban areas of Suliamani. After a microwave-assisted digestion procedure, every sample was analyzed by inductively coupled plasma-optical emission spectrometry. Results showed that the concentration of chromium (Cr) in each snail sample was significantly high. The maximum Cr concentration (15.87 mg kg−1) was recorded in the snail sample collected from Ali Kamal Park, which was adjacent to a very crowded traffic road. The As concentration in snail samples ranged from 0.08 to 1.004 mg kg−1, and it was below the permissible limits. However, the concentrations of heavy metals in urban soil locations were below their background measurements, except for nickel (Ni) which was above the permissible limits. The safest site in the study area was Chaviland 1, while the most contaminated site was the Ha-wary Shar Park. The snails bioaccumulated metals in their tissues in the following order, Cr > Zn > Ni, and this bioaccumulation occurred more on the main road locations, which represented potentially contaminated places due to anthropogenic activities. Moreover, there was no correlation among the heavy metals within the soil samples when compared to the similar metals in the snail samples, due to the low concentration of heavy metals in soil, excluding Ni, from where the snail samples were collected. Consequently, the land snail, E. vermiculata, is an appropriate sentinel organism for some metals, mainly for Cr, and the bioindicator monitoring with this snail should be extended to mixtures of heavy metals, since such relationships frequently occur in soil.
The Need for A Multidisciplinary Approach to Face Challenges Related to Food, Health, and Sustainability: The Contribution of CRC I-WE
Authors: Martini Daniela , Ragone Giada , Cazzini Francesco , Cheli Federica , Formici Giulia , La Porta Caterina A. M. , Pinotti Luciano , Pomodoro Livia , Restani Patrizia , Scaffardi Lucia , Tedeschi Gabriella , Riso Patrizia , Violini Lorenza
Published: December 2021

The importance of the impact of the food system not only on human health but also on planet health is gaining growing attention, and there is now an urgent call to action for developing multi-stakeholder strategies able to end poverty and maintain prosperity and health for people and for the planet. To provide a better understanding of the complex relationships between food, well-being and environment, it is pivotal to generate multidisciplinary knowledge on the promotion of human well-being in relation to multiple interconnected factors such as diet and nutrition, environment, economic, social, and legal aspects. Based on these premises, the present paper aims at describing the proposed role of the Joint Research Center “Innovation for Well-Being and Environment” (CRC I-WE) that was brought to light in 2019 with a strong interdisciplinary nature at the University of Milan, Italy. In 2021, the Center hosted its first annual conference aimed at identifying case studies from the food, health, and sustainability fields particularly deserving an interdisciplinary approach, and which may provide the basis for opening a wider discussion with the scientific community.