bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Analysis of Effects of Specularity Coefficient and Restitution Coefficient on the Hydrodynamics of Particles in a Rotating Drum
Authors: Rahman Rezwana , Zhu Haiping , Yu Aibing
Published: January 2022

Various simulations have been conducted to understand the macroscopic behavior of particles in the solid-gas flow in rotating drums in the past. In these studies, the no-slip wall boundary condition and fixed restitution coefficient between particles were usually adopted. The paper presents a numerical study of the gas-solid flow in a rotating drum to understand the effect of the specularity coefficient and restitution coefficient on the hydrodynamic behavior of particles in the segregation process. The volume fraction, granular pressure, granular temperature and their relationships are examined in detail. The boundary conditions of the no-slip and specularity coefficient of 1 are compared. In the simulations, two different sizes of particles with the same density are considered and the Eulerian–Eulerian multiphase model and the kinetic theory of granular flow (KTGF) are used. The results reveal that the hydrodynamical behavior of the particles in the rotating drum is affected by the boundary condition and restitution coefficient. In particular, the increase of specularity coefficient can increase the active region depth, angle repose, granular pressure for both small and large particles and granular temperature for large particles. With increasing restitution coefficient, the angle of repose decreases and granular pressure and temperature increase at the same volume fraction for both small and large particles.
Exact Time Evolution of Genuine Multipartite Correlations for N-Qubit Systems in a Common Thermal Reservoir
Authors: Roy Abhinash Kumar , Magare Sourabh , Srivastava Varun , Panigrahi Prasanta K.
Published: January 2022

We investigate the dynamical evolution of genuine multipartite correlations for N-qubits in a common reservoir considering a non-dissipative qubits-reservoir model. We derive an exact expression for the time-evolved density matrix by modeling the reservoir as a set of infinite harmonic oscillators with a bilinear form of interaction Hamiltonian. Interestingly, we find that the choice of two-level systems corresponding to an initially correlated multipartite state plays a significant role in potential robustness against environmental decoherence. In particular, the generalized W-class Werner state shows robustness against the decoherence for an equivalent set of qubits, whereas a certain generalized GHZ-class Werner state shows robustness for inequivalent sets of qubits. It is shown that the genuine multipartite concurrence (GMC), a measure of multipartite entanglement of an initially correlated multipartite state, experiences an irreversible decay of correlations in the presence of a thermal reservoir. For the GHZ-class Werner state, the region of mixing parameters for which there exists GMC, shrinks with time and with increase in the temperature of the thermal reservoir. Furthermore, we study the dynamical evolution of the relative entropy of coherence and von-Neumann entropy for the W-class Werner state.
Along the Rivers and into the Plain: Early Crop Diversity in the Central and Western Balkans and Its Relationship with Environmental and Cultural Variables
Authors: de Vareilles Anne , Filipović Dragana , Obradović Djurdja , Vander Linden Marc
Published: January 2022

Agriculture is a complex and dynamic socio-ecological system shaped by environmental, economic, and social factors. The crop resource pool is its key component and one that best reflects environmental limitations and socio-economic concerns of the farmers. This pertains in particular to small-scale subsistence production, as was practised by Neolithic farmers. We investigated if and how the environment and cultural complexes shaped the spectrum and diversity of crops cultivated by Neolithic farmers in the central-western Balkans and on the Hungarian Plain. We did so by exploring patterns in crop diversity between biogeographical regions and cultural complexes using multivariate statistical analyses. We also examined the spectrum of wild-gathered plant resources in the same way. We found that the number of species in Neolithic plant assemblages is correlated with sampling intensity (the number and volume of samples), but that this applies to all archaeological cultures. Late Neolithic communities of the central and western Balkans exploited a large pool of plant resources, whose spectrum was somewhat different between archaeological cultures. By comparison, the earliest Neolithic tradition in the region, the Starčevo-Körös-Criş phenomenon, seems to have used a comparatively narrower range of crops and wild plants, as did the Linearbandkeramik culture on the Hungarian Plain.
The Special Apparatus (al-Niẓām al-Khāṣṣ): The Rise of Nationalist Militancy in the Ranks of the Egyptian Muslim Brotherhood
Authors: Zalaf Ahmed Abou El
Published: January 2022

Existing scholarship has largely focused on the role of Sayyid Qutb’s ideas when analyzing the Muslim Brotherhood’s violent history. Perceiving Qutb’s ideas as paving the way for radical interpretations of jihad, many studies linked the Brotherhood’s violent history with this key ideologue. Yet, in so doing, many studies overlooked the importance of the Special Apparatus in shaping this violent history of the Brotherhood, long before Qutb joined the organization. Through an in-depth study of memoires and accounts penned by Brotherhood members and leaders, and a systematic study of British and American intelligence sources, I attempt to shed light on this understudied formation of the Brotherhood, the Special Apparatus. This paper looks at the development of anti-colonial militancy in Egypt, particularly the part played by the Brotherhood until 1954. It contends that political violence, in the context of British colonization, antedated the Brotherhood’s foundation, and was in some instances considered as a legitimate and even distinguished duty among anti-colonial factions. The application of violence was on no account a part of the Brotherhood’s core strategy, but the organization, nevertheless, established an armed and secret wing tasked with the fulfillment of what a segment of its members perceived as the duty of anti-colonial jihad.
A Recursive Algorithm for the Forward Kinematic Analysis of Robotic Systems Using Euler Angles
Authors: Gonçalves Fernando , Ribeiro Tiago , Ribeiro António Fernando , Lopes Gil , Flores Paulo
Published: January 2022

Forward kinematics is one of the main research fields in robotics, where the goal is to obtain the position of a robot’s end-effector from its joint parameters. This work presents a method for achieving this using a recursive algorithm that builds a 3D computational model from the configuration of a robotic system. The orientation of the robot’s links is determined from the joint angles using Euler Angles and rotation matrices. Kinematic links are modeled sequentially, the properties of each link are defined by its geometry, the geometry of its predecessor in the kinematic chain, and the configuration of the joint between them. This makes this method ideal for tackling serial kinematic chains. The proposed method is advantageous due to its theoretical increase in computational efficiency, ease of implementation, and simple interpretation of the geometric operations. This method is tested and validated by modeling a human-inspired robotic mobile manipulator (CHARMIE) in Python.
Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?
Authors: Putzenlechner Birgitta , Marzahn Philip , Koal Philipp , Sánchez-Azofeifa Arturo
Published: January 2022

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.
Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing
Authors: Feng Xinxi , Han Le , Dong Le
Published: January 2022

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.
Comparison of Classification Algorithms for Detecting Typical Coastal Reclamation in Guangdong Province with Landsat 8 and Sentinel 2 Images
Authors: Ai Bin , Huang Ke , Zhao Jun , Sun Shaojie , Jian Zhuokai , Liu Xiaoding
Published: January 2022

Coastal reclamation in Guangdong Province is highly concentrated and is growing rapidly. However, intensive reclamation use has resulted in serious influence on the coastal ecosystem, directly and indirectly. The current conditions and spatial distribution of reclamations must be detected for coastal preservation and management using efficient technology. This study aims to find a suitable method and data to map reclamations accurately at a large scale. Pixel-based and object-oriented classification methods were applied in extracting the three typical types of coastal reclamation, namely, ports, aquaculture ponds, and salt pans, in Guangdong Province from Landsat 8 and Sentinel 2 images. The algorithms of a support vector machine, random forest, decision tree, and rule-based algorithm were performed. Classification results were compared with statistical measures to assess the performance of different algorithms. The results indicated that all of the algorithms could obtain classification results with high accuracy, whereas the object-oriented algorithm showed less efficiency than other algorithms in classifying ports with complicated features. High-resolution data were not always superior to lower-resolution data in the reclamation classification. Generally speaking, applying the rule-based object-oriented algorithm in Sentinel 2A MSI images is relatively efficient at detecting the reclamation use in coastal Guangdong considering its actual situation. The mapping of reclamations in the whole of coastal Guangdong shows that they present obvious agglomeration characteristics in the space. The aquaculture ponds are mainly distributed in the coastal zones of western Guangdong and eastern Guangdong, with the largest area of 77,963 ha. The other types of ports are mainly distributed in the coastal zones of the Pearl River Delta, with an area of 8146 ha, while salt pans are mainly distributed in the coastal zones of Jiangmen, Zhuhai, and Zhongshan, with a total area of 4072 ha. The results can provide key supporting data for decision making in coastal management and preservation.
Compressed SAR Interferometry in the Big Data Era
Authors: Ho Tong Minh Dinh , Ngo Yen-Nhi
Published: January 2022

Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long-term monitoring. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator is close to the theoretical Cramer–Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.
Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery
Authors: Shi Yue , Han Liangxiu , Kleerekoper Anthony , Chang Sheng , Hu Tongle
Published: January 2022

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.
Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets
Authors: Tang Xueyuan , Dong Sheng , Luo Kun , Guo Jingxue , Li Lin , Sun Bo
Published: January 2022

The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.
Co-Seismic Ionospheric Disturbances Following the 2016 West Sumatra and 2018 Palu Earthquakes from GPS and GLONASS Measurements
Authors: Cahyadi Mokhamad Nur , Muslim Buldan , Pratomo Danar Guruh , Anjasmara Ira Mutiara , Arisa Deasy , Rahayu Ririn Wuri , Hariyanto Irena Hana , Jin Shuanggen , Muafiry Ihsan Naufal
Published: January 2022

The study of ionospheric disturbances associated with the two large strike-slip earthquakes in Indonesia was investigated, which are West Sumatra on 2 March 2016 (Mw = 7.8), and Palu on 28 September 2018 (Mw = 7.5). The anomalies were observed by measuring co-seismic ionospheric disturbances (CIDs) using the Global Navigation Satellite System (GNSS). The results show positive and negative CIDs polarization changes for the 2016 West Sumatra earthquake, depending on the position of the satellite line-of-sight, while the 2018 Palu earthquake shows negative changes only due to differences in co-seismic vertical crustal displacement. The 2016 West Sumatra earthquake caused uplift and subsidence, while the 2018 Palu earthquake was dominated by subsidence. TEC anomalies occurred about 10 to 15 min after the two earthquakes with amplitude of 2.9 TECU and 0.4 TECU, respectively. The TEC anomaly amplitude was also affected by the magnitude of the earthquake moment. The disturbance signal propagated with a velocity of ~1–1.72 km s−1 for the 2016 West Sumatra earthquake and ~0.97–1.08 km s−1 for the 2018 Palu mainshock earthquake, which are consistent with acoustic waves. The wave also caused an oscillation signal of ∼4 mHz, and their azimuthal asymmetry of propagation confirmed the phenomena in the Southern Hemisphere. The CID signal could be identified at a distance of around 400–1500 km from the epicenter in the southwestern direction.
An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints
Authors: Xu Xinchao , Liu Mingyue , Peng Song , Ma Youqing , Zhao Hongxi , Xu Aigong
Published: January 2022

In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions.
Columnar Aerosol Optical Property Characterization and Aerosol Typing Based on Ground-Based Observations in a Rural Site in the Central Yangtze River Delta Region
Authors: Xie Yong , Su Yi , Gu Xingfa , Chen Tiexi , Shao Wen , Hu Qiaoli
Published: January 2022

Accurate and updated aerosol optical properties (AOPs) are of vital importance to climatology and environment-related studies for assessing the radiative impact of natural and anthropogenic aerosols. We comprehensively studied the columnar AOP observations between January 2019 and July 2020 from a ground-based remote sensing instrument located at a rural site operated by Central China Comprehensive Experimental Sites in the center of the Yangtze River Delta (YRD) region. In order to further study the aerosol type, two threshold-based aerosol classification methods were used to investigate the potential categories of aerosol particles under different aerosol loadings. Based on AOP observation and classification results, the potential relationships between the above-mentioned results and meteorological factors (i.e., humidity) and long-range transportation processes were analyzed. According to the results, obvious variation in aerosol optical depth (AOD) during the daytime, as well as throughout the year, was revealed. Investigation into AOD, single-scattering albedo (SSA), and absorption aerosol optical depth (AAOD) revealed the dominance of fine-mode aerosols with low absorptivity. According to the results of the two aerosol classification methods, the dominant aerosol types were continental (accounting for 43.9%, method A) and non-absorbing aerosols (62.5%, method B). Longer term columnar AOP observations using remote sensing alongside other techniques in the rural areas in East China are still needed for accurate parameterization in the future.
Human Activity Recognition Based on Residual Network and BiLSTM
Authors: Li Yong , Wang Luping
Published: January 2022

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.
Promoting Equity When Using the SNOT-22 Score: A Scoping Review and Literature Review
Authors: Weaver Abigail , Wood Andrew
Published: January 2022

It is established that non-white people experience worse health outcomes than white people within the same population. Equity addresses differences between patient subgroups, allowing needs-based distribution of resources. The use of quality-of-life (QoL) tools to assist clinical decision making such as the SNOT-22 for chronic rhinosinusitis promotes equality, not equity, as quality-of-life (QoL) tools provide the same criteria of symptom scoring across diverse populations. We considered the effects of ethnicity and race on SNOT-22 scores and whether these scores should be adjusted to improve equity. PubMed and MEDLINE provided papers for a scoping review. A combination of the following search terms was used: patient-reported outcome measures (PROM) (OR) quality of life; (AND) race (OR) ethnicity (OR) disparities; (AND) otolaryngology (OR) SNOT-22 (OR) sinusitis. The first study identified no evidence of ethnic variability in SNOT-22 scores. However, the study did not represent the local population, including 86% white people. Other studies identified baseline SNOT-22 disparities with respect to population demographics, gender, and age. Ethnic differences appear to exist in acute sinusitis symptomatology. In other fields both within and outside of otorhinolaryngology, ethnic differences exist with regard to QoL tools. This scoping review identified a paucity of data in rhinology. However, evidence implies some form of correction to QoL scores could help promote equity for non-white patients.
Linked Lives: Does Disability and Marital Quality Influence Risk of Marital Dissolution among Older Couples?
Authors: Latham-Mintus Kenzie , Holcomb Jeanne , Zervos Andrew P.
Published: January 2022

Using fourteen waves of data from the Health and Retirement Study (HRS), a longitudinal panel survey with respondents in the United States, this research explores whether marital quality—as measured by reports of enjoyment of time together—influences risk of divorce or separation when either spouse acquires basic care disability. Discrete-time event history models with multiple competing events were estimated using multinomial logistic regression. Respondents were followed until they experienced the focal event (i.e., divorce or separation) or right-hand censoring (i.e., a competing event or were still married at the end of observation). Disability among wives was predictive of divorce/separation in the main effects model. Low levels of marital quality (i.e., enjoy time together) were associated with marital dissolution. An interaction between marital quality and disability yielded a significant association among couples where at least one spouse acquired basic care disability. For couples who acquired disability, those who reported low enjoyment were more likely to divorce/separate than those with high enjoyment; however, the group with the highest predicted probability were couples with low enjoyment, but no acquired disability.
Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation
Authors: Yang Hyewon , Han Young Jae , Yu Jiwon , Kim Sumi , Lee Sugil , Kim Gildong , Lee Chulung
Published: January 2022

The purpose of this research was to derive promising technologies for the transport of hydrogen fuel cells, thereby supporting the development of research and development policy and presenting directions for investment. We also provide researchers with information about technology that will lead the technology field in the future. Hydrogen energy, as the core of carbon neutral and green energy, is a major issue in changing the future industrial structure and national competitive advantage. In this study, we derived promising technology at the core of future hydrogen fuel cell transportation using the published US patent and paper databases (DB). We first performed text mining and data preprocessing and then discovered promising technologies through generative topographic mapping analysis. We analyzed both the patent DB and treatise DB in parallel and compared the results. As a result, two promising technologies were derived from the patent DB analysis, and five were derived from the paper DB analysis.
Encouraging the Sustainable Adoption of Autonomous Vehicles for Public Transport in Belgium: Citizen Acceptance, Business Models, and Policy Aspects
Authors: Camps-Aragó Pol , Temmerman Laura , Vanobberghen Wim , Delaere Simon
Published: January 2022

Several mobility-related issues persist in and around urban areas. Autonomous vehicles promise substantial environmental, safety, and economic benefits but may also cause unintended adverse effects that stem from single-passenger mobility becoming more affordable and accessible. While using them for public transport (i.e., autonomous shuttles) can help avoid such downsides, there are many challenges to their adoption, particularly ones that are related to citizen acceptance and economic aspects. Based on a novel survey of Brussels’ citizens, we provide insights from user opinions on last-mile autonomous shuttle services and analyze the effect of various attitudinal and socio-demographic factors affecting such acceptance. Our respondents exhibit an overall positive acceptance albeit with a limited willingness to pay for it. In addition, based on expert interviews, we provide a discussion on appropriate business models and policy recommendations to help ensure the timely adoption of AVs in Belgium that adapts to mobility needs and policy goals.
Basin-Scale Approach to Integration of Agro- and Hydroecological Monitoring for Sustainable Environmental Management: A Case Study of Belgorod Oblast, European Russia
Authors: Buryak Zhanna , Lisetskii Fedor , Gusarov Artyom , Narozhnyaya Anastasiya , Kitov Mikhail
Published: January 2022

The quantitative and qualitative depletion of water resources (both surface and groundwater) is closely related to the need to protect soils against degradation, rationalization of land use, and regulation of surface water runoff within the watershed area. Belgorod Oblast (27,100 km2), one of the administrative regions of European Russia, was chosen as the study area. It is characterized by a high activity of soil erosion (the share of eroded soils is about 48% of the total area of arable land). The development phase of the River Basin Environmental Management Projects (217 river basins from the fourth to seventh order) allowed for the proceeding of the development of an integrated monitoring system for river systems and river basin systems. The methods used to establish a geoecological network for regional monitoring include the selection and application of GIS techniques to quantify the main indicators of ecological state and predisposition of river basins to soil erosion (the share of cropland and forestland, the share of the south-oriented slopes, soil erodibility, Slope Length and Steepness (LS) factor, erosion index of precipitation, and the river network density) and the method of a hierarchical classification of cluster analysis for the grouping of river basins. An approach considering the typology of river basins is also used to expand the regional network of hydrological gauging stations to rationalize the national hydrological monitoring network. By establishing 16 additional gauging stations on rivers from the fourth to seventh order, this approach allows for an increase in the area of hydro-agroecological monitoring by 1.26 times (i.e., up to 77.5% of the total area of Belgorod Oblast). Some integrated indicators of agroecological (on the watershed surface) and hydroecological (in river water flow) monitoring are proposed to improve basin environmental management projects. Six-year monitoring showed the effectiveness of water quality control measures on an example of a decrease in the concentrations of five major pollutants in river waters.
The Effect of Social Undermining on Employees’ Emotional Exhaustion and Procrastination Behavior in Deluxe Hotels: Moderating Role of Positive Psychological Capital
Authors: Jung Hyo-Sun , Yoon Hye-Hyun
Published: January 2022

The purpose of this study was to determine if social undermining as perceived by frontline employees significantly affects their emotional exhaustion and procrastination behavior and to clarify the moderating role of positive psychological capital. A total of 310 deluxe hotel employees in South Korea participated in this study by completing a self-administered questionnaire. The study results showed that social undermining perceived by deluxe hotel employees positively influenced their emotional exhaustion. In addition, when emotional exhaustion became severe, employees’ procrastination behavior, which harmed their organizations, increased. Additionally, the findings suggest a mediating effect, thereby indicating that employees’ procrastination behaviors may increase when they experience emotional exhaustion resulting from social undermining. When employees perform their jobs with a positive attitude in a work situation, the negative influence of social undermining and emotional exhaustion may be partially offset. Limitations and future research directions are also discussed.
Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows
Authors: Vrbanić Filip , Miletić Mladen , Tišljarić Leo , Ivanjko Edouard
Published: January 2022

Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm’s effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway.
Social Investing Modeling for Sustainable Development of the Russian Arctic
Authors: Novoselov Andrey , Potravny Ivan , Novoselova Irina , Gassiy Violetta
Published: January 2022

The method of the social investing of the Arctic subsoil users is considered in this article. As the portfolio of social investments is formed based on the interests of indigenous peoples, the authors used expert assessment and sociological research for social investing modeling. A two-stage procedure for forming a portfolio of such projects is proposed. An approach has also been developed for assessing and selecting investment projects for the Arctic sustainable development according to different criteria of optimality. The authors substantiate the need for a new approach to sustainable development of the Arctic, based not on compensation for the negative consequences of industrial development used in many countries, but on social investment. In this article the proposed approach is tested on the case of the Arctic indigenous community in Taimyr and the optimal social investing portfolio is justified.
Biosorption and Bioleaching of Heavy Metals from Electronic Waste Varied with Microbial Genera
Authors: Kaur Preetiman , Sharma Shivani , Albarakaty Fawziah M. , Kalia Anu , Hassan Mohamed M. , Abd-Elsalam Kamel A.
Published: January 2022

Industrialization and technological advancements have led to the exploitation of natural resources and the production of hazardous wastes, including electronic waste (E-waste). The traditional physical and chemical techniques used to combat E-waste accumulation have inherent drawbacks, such as the production of harmful gases and toxic by-products. These limitations may be prudently addressed by employing green biological methods, such as biosorption and bioleaching. Therefore, this study was aimed at evaluating the biosorption and bioleaching potential of seven microbial cultures using E-waste (printed circuit board (PCB)) as a substrate under submerged culture conditions. The cut pieces of PCB were incubated with seven microbial cultures in liquid broth conditions in three replicates. Atomic absorption spectroscopy (AAS) analysis of the culture biomass and culture filtrates was performed to evaluate and screen the better-performing microbial cultures for biosorption and bioleaching potentials. The best four cultures were further evaluated through SEM, energy-dispersive X-ray spectroscopy (EDX), and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) studies to identify the possible culture that can be utilized for the biological decontamination of E-waste. The study revealed the highest and differential ability of Pleurotus florida and Pseudomonas spp. for biosorption and bioleaching of copper and iron. This can be attributed to bio-catalysis by the laccase enzyme. For both P. florida and Pseudomonas spp. on the 20th day of incubation, laccase exhibited higher specific activity (6.98 U/mg and 5.98 U/mg, respectively) than other microbial cultures. The biomass loaded with Cu2+ and Fe2+ ions after biosorption was used for the desorption process for recovery. The test cultures exhibited variable copper recovery efficiencies varying between 10.5 and 18.0%. Protein characterization through SDS-PAGE of four promising microbial cultures exhibited a higher number of bands in E-waste as compared with microbial cultures without E-waste. The surface topography studies of the E-waste substrate showed etching, as well as deposition of vegetative and spore cells on the surfaces of PCB cards. The EDX studies of the E-waste showed decreases in metal element content (% wt/% atom basis) on microbial treatment from the respective initial concentrations present in non-treated samples, which established the bioleaching phenomenon. Therefore, these microbial cultures can be utilized to develop a biological remediation method to manage E-waste.
A Human-Centric Model for Sustainable Asset Management in Railway: A Case Study
Authors: Kour Ravdeep , Castaño Miguel , Karim Ramin , Patwardhan Amit , Kumar Manish , Granström Rikard
Published: January 2022

The ongoing digital transformation is changing asset management in the railway industry. Emerging digital technologies and Artificial Intelligence is expected to facilitate decision-making in management, operation, and maintenance of railway by providing an integrated data-driven and model-driven solution. An important aspect when developing decision-support solutions based on AI and digital technology is the users’ experience. User experience design process aims to create relevance, context-awareness, and meaningfulness for the end-user. In railway contexts, it is believed that applying a human-centric design model in the development of AI-based artefacts, will enhance the usability of the solution, which will have a positive impact on the decision-making processes. In this research, the applicability of such advanced technologies i.e., Virtual Reality, Mixed Reality, and AI have been reviewed for the railway asset management. To carry out this research work, literature review has been conducted related to available Virtual Reality/Augmented Reality/Mixed Reality technologies and their applications within railway industry. It has been found that these technologies are available, but not applied in railway asset management. Thus, the aim of this paper is to propose a human-centric design model for the enhancement of railway asset management using Artificial Intelligence, Virtual Reality, and Mixed Reality technologies. The practical implication of the findings from this work will benefit in increased efficiency and effectiveness of the operation and maintenance processes in railway.
Impacts of Climatic Variation and Human Activity on Runoff in Western China
Authors: Liu Yinge , Yu Keke , Zhao Yaqian , Bao Jiangchuan
Published: January 2022

Hydrological cycle is sensitively affected by climatic variation and human activity. Taking the upper- and middle-stream of the Weihe River in western China as an example, using multiple meteorological and hydrological elements, as well as land-use/land-cover change (LUCC) data, we constructed a sensitivity model of runoff to climatic elements and human activities based on the hydro-thermal coupling equilibrium equation, while a cumulative slope was used to establish a comprehensive estimation model for the contributions of climatic variation and human activities to the changes of runoff. The results showed that the above function model established could be well applied to quantitatively study the elasticity of runoff’s response to climatic variation and human activities. It was found that the annual average precipitation, evaporation, wind velocity, sunshine hours, relative humidity and runoff showed decreasing trends and that temperature increased. While in the hydrological cycle, precipitation and relative humidity had a non-linear positive driving effect on runoff, while temperature, evaporation, sunshine hours, wind velocity, and land-use/land-cover change (LUCC) have non-linearly negatively driven the variation of runoff. Moreover, runoff has a strong sensitive response to precipitation, evaporation and LUCC. In areas with strong human activities, the sensitivity of runoff to climatic change was decreasing, and runoff has a greater elastic response to underlying surface parameters. In addition, the analysis showed that the abrupt years of climate and runoff changes in the Weihe River Basin were 1970, 1985 and 1993. Before 1985, the contribution rate of climatic variation to runoff was 68.3%, being greater than that of human activities to runoff, and then the contribution rates of human activities to runoff reached 75.1%. The impact of natural climate on runoff was weakened, and the effect of human activities on runoff reduction increased. Under 30 hypothetical climatic scenarios, the evaluation of runoff in the future showed that the runoff in the Weihe River Basin will be greatly reduced, and the reduction will be more significant during the flood season. Comparing the geographically fragile environments and intense human activities, it was believed that climatic variation had a dramatic effect on driving the water cycle of precipitation and evaporation and affected regional water balance and water distribution, while human activities had driven the hydrological processes of the underlying surface, thus becoming the main factors in the reduction of runoff. This study provided scientific tools for regional climate change and water resources assessment.
The Utility of Blockchain Technology in the Electronic Commerce of Tourism Services: An Exploratory Study on Romanian Consumers
Authors: Raluca-Florentina Tița
Published: January 2022

Blockchain technology is considered one of the most revolutionary innovations that has much to offer the tourism industry, having a positive impact among consumers with the help of interactive applications but also easy to use. Tourist services must constantly evolve in a society where the consumer has everything a click away and his requirements are demanding when it comes to quality leisure. Blockchain technology has the power to change the course of the travel experience, offering the customer more autonomy, but the applications developed by the providers can offer transparency and trust to the customers from the moment T0, when the desire to go on a trip is born, until the end at which it should provide feedback. So far, digital and tourism specialists have not agreed on the development of blockchain-based applications, although the benefits are great for both consumers and tourism service providers, as in this publication we can see a series of advantages that blockchain technology can offer the tourism field. This paper also investigates the satisfaction that the Romanian consumer has after purchasing tourist services through e-commerce applications, a satisfaction that can be an additional motivation for specialists to implement blockchain technology. Following the research in this paper, it can be seen how important it is to develop a series of easy-to-use applications, because if the consumer does not manage to use the applications, this affects the degree of satisfaction and the intention to continue using the online applications for the purchase of tourism services.
What Makes Companies to Survive over a Century? The Case of Dongwha Pharmaceutical in the Republic of Korea
Authors: Kang Moon Young
Published: January 2022

Considering their significant impacts on national economy, local communities, employees, suppliers, and other stakeholders, firm survival is an important goal for most companies. However, firm longevity is not an easy result to achieve, and it becomes more difficult as business environments shift dramatically. The average life span of companies listed in Standard and Poor’s 500 was 61 years in 1958, but it fell to less than 18 years in 2016. Nevertheless, more than 3000 companies over 200 years old still operate in Japan, mostly family inherited small and medium enterprises (SMEs), and Germany has many centenarian companies, especially in the manufacturing industry. However, in the Republic of Korea, with over 4000 years of history, there are no companies older than 200 years and only seven companies with over 100 years of history. Centenarian companies in Korea are very limited, and even after surviving over 100 years, they continue to face severe challenges they need to overcome in order to survive. However, it is not clearly proven what factors affect the survival of companies for over a century. Since SMEs contribute to the economic development and survival of a country, it is important to investigate the secrets of their longevity, which has become even more difficult than ever. However, as there is very limited research on firm longevity, the findings from this case study of Dongwha Pharmaceutical, balancing the continuity of tradition and innovation, provide significant implications and practical guidelines for Korean SMEs for their survival and sustainable management, which is an economic and business aspect of sustainability.