bluuebunny/crossref_metadata_2025 huggingface dataset:
Acoustic Environmental Conditions (Do Not?) Affect the Static Posturography Diagnostic Accuracy: A Test–Retest Reliability Study
Authors: Calvo-Moreno Sofía Olivia , Rodríguez-López Elena Sonsoles , Varol Umut , Benito-de-Pedro María , Anós-Merino Elena , Conde-Vázquez Orlando , Fernández-de-las-Peñas César , Valera-Calero Juan Antonio
Published: March 2022

Static posturography assessed with force platforms is a procedure used to obtain objective estimates related to postural adjustments. However, controlling multiple intrinsic and extrinsic factors influencing the diagnostic accuracy is essential to obtain reliable measurements and recommend its use with clinical or research purposes. We aimed to analyze how different environmental acoustic conditions affect the test–retest reliability and to analyze the most appropriate number of trials to calculate a valid mean average score. A diagnostic accuracy study was conducted enrolling 27 healthy volunteers. All procedures were taken considering consistent device settings, posture, feet position, recording time, and illumination of the room. Three trials were recorded in a silent environment (35–40 dB) and three trials were recorded in a noisy environment (85–90 dB). Results showed comparable reliability estimates for both acoustic conditions (ICC = 0.453–0.962 and 0.621–0.952), but silent conditions demonstrated better sensitivity to changes (MDC = 13.6–76%). Mean average calculations from 2 and 3 trials showed no statistically significant differences (p > 0.05). Cross-sectional studies can be conducted under noisy or silent conditions as no significantly different scores were obtained (p > 0.05) and ICC were comparable (except oscillation area). However, longitudinal studies should consider silent conditions as they demonstrated better sensitivity to real changes not derived from measurement errors.
A Hierarchical Path Planning Approach with Multi-SARSA Based on Topological Map
Authors: Wen Shiguang , Jiang Yufan , Cui Ben , Gao Ke , Wang Fei
Published: March 2022

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.
Development and Evaluation of a Slip Detection Algorithm for Walking on Level and Inclined Ice Surfaces
Authors: Cen Jun-Yu , Dutta Tilak
Published: March 2022

Slip-resistant footwear can prevent fall-related injuries on icy surfaces. Winter footwear slip resistance can be measured by the Maximum Achievable Angle (MAA) test, which measures the steepest ice-covered incline that participants can walk up and down without experiencing a slip. However, the MAA test requires the use of a human observer to detect slips, which increases the variability of the test. The objective of this study was to develop and evaluate an automated slip detection algorithm for walking on level and inclined ice surfaces to be used with the MAA test to replace the need for human observers. Kinematic data were collected from nine healthy young adults walking up and down on ice surfaces in a range from 0° to 12° using an optical motion capture system. Our algorithm segmented these data into steps and extracted features as inputs to two linear support vector machine classifiers. The two classifiers were trained, optimized, and validated to classify toe slips and heel slips, respectively. A total of approximately 11,000 steps from 9 healthy participants were collected, which included approximately 4700 slips. Our algorithm was able to detect slips with an overall F1 score of 90.1%. In addition, the algorithm was able to accurately classify backward toe slips, forward toe slips, backward heel slips, and forward heel slips with F1 scores of 97.3%, 54.5%, 80.9%, and 86.5%, respectively.
Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings
Authors: Elkhoukhi Hamza , Bakhouya Mohamed , El Ouadghiri Driss , Hanifi Majdoulayne
Published: March 2022

Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration, which usually do not match the occupants’ preferences. Therefore, occupancy detection is imperative for energy use management mainly in residential and industrial buildings. Most works related to data-driven-based occupancy detection have used batch learning techniques, which need to store first and then train the data. It is not appropriate for a non-stationary environment. Therefore, this work sheds more light on the use of non-stationary machine learning techniques. To this end, three machine learning algorithms for stream data processing are presented, tested, and evaluated in term of accuracy and resources performance (i.e., RAM, CPU), with the aim of predicting the number of occupants in smart buildings. A platform architecture that integrates IoT technologies with stream machine learning is implemented and deployed. The experimental results show the effectiveness of this approach and illustrate that the number of occupants can be predicted with an accuracy of more than 83% and without resource wasting (i.e., time of CPU use varied between 0.04s and 3.85 ⋅ 10−11 GB of RAM could be exploited per hour).
A Proactive Recognition System for Detecting Commercial Vehicle Driver’s Distracted Behavior
Authors: Yan Xintong , He Jie , Wu Guanhe , Zhang Changjian , Wang Chenwei
Published: March 2022

Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver’s distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver’s distracted behavior recognition systems. The driver’s posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver’s real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver’s microscopic behavior to establish a more comprehensive proactive surveillance system.
Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning
Authors: Habeeb Fawzy , Szydlo Tomasz , Kowalski Lukasz , Noor Ayman , Thakker Dhaval , Morgan Graham , Ranjan Rajiv
Published: March 2022

Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device.
Design and Test of an Integrated Measurement System for Multi-Hole Probe Calibration and Vortex Measurement
Authors: Yao Tao , Zhou Shudao , Ye Song
Published: March 2022

Multi-hole probes can simultaneously measure the velocity and direction of a flow field, obtain the distribution of the flow field in a three-dimensional space, and obtain the vortex information in the flow field. Moreover, a multi-hole probe needs to be calibrated while in use; therefore, a three-coordinate, multi-directional rotatable testing system, which can measure the flow field at any position and at any angle, was designed herein. A hemispherical seven-hole probe was calibrated with this test system, and the flow field around cylinders of different diameters was measured to obtain the pressure distribution and vortex shedding frequency. Furthermore, the designed test system’s ability to perform a multi-angle and multi-azimuth testing during the calibration of a multi-hole probe was verified. Simultaneously, through data mining of the multi-hole probe, vortices were measured, and periodic vortices were detected.
Review of Intentional Electromagnetic Interference on UAV Sensor Modules and Experimental Study
Authors: Kim Sung-Geon , Lee Euibum , Hong Ic-Pyo , Yook Jong-Gwan
Published: March 2022

With the advancement of technology, Unmanned Aerial Vehicles (UAVs), also known as drones, are being used in numerous applications. However, the illegal use of UAVs, such as in terrorism and spycams, has also increased, which has led to active research on anti-drone methods. Various anti-drone methods have been proposed over time; however, the most representative method is to apply intentional electromagnetic interference to drones, especially to their sensor modules. In this paper, we review various studies on the effect of intentional electromagnetic interference (IEMI) on the sensor modules. Various studies on IEMI sources are reviewed and classified on the basis of the power level, information needed, and frequency. To demonstrate the application of drone-sensor modules, major sensor modules used in drones are briefly introduced, and the setup and results of the IEMI experiment performed on them are described. Finally, we discuss the effectiveness and limitations of the proposed methods and present perspectives for further research necessary for the actual application of anti-drone technology.
Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear
Authors: Tan Qipeng , Zhang Tiandong , Wu Shaocheng , Gao Jiachen , Song Bin
Published: March 2022

Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear.
Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach
Authors: Yang Hailu , Yang Yue , Hou Yue , Liu Yue , Liu Pengfei , Wang Linbing , Ma Yuedong
Published: March 2022

Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10−2/°C to 1.896 × 10−4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.
Adverse Effects of Arsenic Uptake in Rice Metabolome and Lipidome Revealed by Untargeted Liquid Chromatography Coupled to Mass Spectrometry (LC-MS) and Regions of Interest Multivariate Curve Resolution
Authors: Pérez-Cova Miriam , Tauler Romà , Jaumot Joaquim
Published: March 2022

Rice crops are especially vulnerable to arsenic exposure compared to other cereal crops because flooding growing conditions facilitates its uptake. Besides, there are still many unknown questions about arsenic’s mode of action in rice. Here, we apply two untargeted approaches using liquid chromatography coupled to mass spectrometry (LC-MS) to unravel the effects on rice lipidome and metabolome in the early stages of growth. The exposure is evaluated through two different treatments, watering with arsenic-contaminated water and soil containing arsenic. The combination of regions of interest (ROI) and multivariate curve resolution (MCR) strategies in the ROIMCR data analyses workflow is proposed and complemented with other multivariate analyses such as partial least square discriminant analysis (PLS-DA) for the identification of potential markers of arsenic exposure and toxicity effects. The results of this study showed that rice metabolome (and lipidome) in root tissues seemed to be more affected by the watering and soil treatment. In contrast, aerial tissues alterations were accentuated by the arsenic dose, rather than with the watering and soil treatment itself. Up to a hundred lipids and 40 metabolites were significantly altered due to arsenic exposure. Major metabolic alterations were found in glycerophospholipids, glycerolipids, and amino acid-related pathways.
Fractional Separation and Characterization of Cuticular Waxes Extracted from Vegetable Matter Using Supercritical CO2
Authors: Scognamiglio Mariarosa , Baldino Lucia , Reverchon Ernesto
Published: March 2022

Cuticular waxes can be used in high-value applications, including cosmetics, foods and nutraceuticals, among the others. The extraction process determines their quality and purity that are of particular interest when biocompatibility, biodegradability, flavor and fragrance are the main features required for the final formulations. This study demonstrated that supercritical fluid extraction coupled with fractional separation can represent a suitable alternative to isolate cuticular waxes from vegetable matter that preserve their natural properties and composition, without contamination of organic solvent residues. Operating in this way, cuticular waxes can be considered as a fingerprint of the vegetable matter, where C27, C29 and C31 are the most abundant compounds that characterize the material; the differences are mainly due to their relative proportions and the presence of hydrocarbon compounds possessing other functional groups, such as alcohols, aldehydes or acids. Therefore, selectivity of supercritical fluid extraction towards non-polar or slightly polar compounds opens the way for a possible industrial approach to produce extracts that do not require further purification steps.
Identifying Depression-Related Behavior on Facebook—An Experimental Study
Authors: Kmetty Zoltán , Bozsonyi Károly
Published: March 2022

Depression is one of the major mental health problems in the world and the leading cause of disability worldwide. As people leave more and more digital traces in the online world, it becomes possible to detect depression-related behavior based on people’s online activities. We use a novel Facebook study to identify possible non-textual elements of depression-related behavior in a social media environment. This study focuses on the relationship between depression and the volume and composition of Facebook friendship networks and the volume and temporal variability of Facebook activities. We also tried to establish a link between depression and the interest categories of the participants. The significant predictors were partly different for cognitive-affective depression and somatic depression. Earlier studies found that depressed people have a smaller online social network. We found the same pattern in the case of cognitive-affective depression. We also found that they posted less in others’ timelines, but we did not find that they posted more in their own timeline. Our study was the first to use the Facebook ads interest data to predict depression. Those who were classified into the less interest category by Facebook had higher depression levels on both scales.
From Performance to Prison: The Case of Vera ‘Jacko’ Holme Analyzing Body Politics, Gendered Transgression and Socio-Political Identity of a Stage Actress, 1890–1914
Authors: Singh Twisha
Published: March 2022

In Britain, during the nineteenth century the process of identity formation of a female performer was conditioned by a challenge towards the pre-existing gender norms that underwent a paradigmatic shift owing to the ‘new performative turn’. The story of Vera Holme popularly known as ‘Jacko’, emanates from this very shift. Holme was an actress born in Lancashire in 1881. Often known as the ‘first female chauffeur of Britain’, she not only embodied the social, political and the sexual, but also imparted it through personal and dramatic means thereby challenging the ideology of separate gendered spheres much before the ideas of the ‘new Sapphic woman’ and ‘female masculinity’ came into being. Using the case study of Vera Holme, I study the socio-political identity of a stage actress who embodied gendered transgression across a time that permeated from her public life to private life and vice versa. Themes such as performativity, political theatre, deconstruction of sexuality and body politics become intrinsic in order to decipher the sexual lexicon of the time that fettered women on the condition of being socially and morally deviant. Utilizing the personal papers of Vera Holme, I posit her forays into theatre and politics, within this context, that will not only complicate the understanding of gendered transgressions but simultaneously will throw light on how theatricality socio-economically ‘enabled’ many women to break away from the existing normative patriarchal structures including Holme.
Biopolitical Responses to the COVID-19 Pandemic in Russia, France, Germany, and the UK: The “Post-Truth” Coverage by RT
Authors: Yatsyk Alexandra
Published: March 2022

This paper seeks to examine the COVID-19 crisis in Russia, France, Germany, and the UK, as covered by the Russian state media outlet RT (formerly Russia Today). I view the RT coverage through the prism of biopolitics and critical discourse analysis (CDA) to demonstrate multiple discrepancies in its “post-truth” knowledge production strategies. I argue that these strategies aim to expose the hybrid and controversial nature of biopolitical governance in Western democracies during the COVID-19 pandemic as they struggle to strike a balance between imposing social restrictions and safeguarding public health. I also show how the (post)liberal biopolitical debate on personal responsibility and state resilience in times of emergency could be applied by authoritarian regimes for self-description.
Green Supply Chain Management Implemented by Suppliers as Drivers for SMEs Environmental Growth with a Focus on the Restaurant Industry
Authors: Shin Soomin , Cho Meehee
Published: March 2022

This study was designed to better understand how restaurants can achieve effective environmental performance by focusing on their business relationships with suppliers that implement green supply chain management (GSCM). Restaurant suppliers’ GSCM was particularly assessed as a two-dimensional concept (external and internal) to explore if those two GSCM practices could possibly encourage restaurant ethical attitudes, thereby encouraging cooperative behaviors (‘joint action’, ‘information sharing’ and ‘flexibility in arrangement’) toward green suppliers. A total of 259 responses obtained from restaurant owners/managers were used for our analysis. Results revealed a significant effect of external GSCM on restaurant ethical attitudes, while internal GSCM was found to have no measurable effect. Further it was found that restaurant ethical attitudes facilitated cooperative behaviors toward green suppliers. More specifically, restaurant ‘information sharing’ and ‘flexibility in arrangement’ significantly improved their environmental performance; however, ‘joint action’ had no such effect. Based on our findings, several important theoretical and practical implications are proposed for restaurants to “go green” more effectively.
Smart City 4.0: Sustainable Urban Development in the Metropolis GZM
Authors: Makieła Zbigniew J. , Stuss Magdalena M. , Mucha-Kuś Karolina , Kinelski Grzegorz , Budziński Marcin , Michałek Janusz
Published: March 2022

This article presents the development of the Metropolis GZM cities within the scope of the concept of a Smart City and 4T capitals. The subject matter discussed herein is also related to the search for municipal authorities’ new models and tools to shape sustainable development in order to improve citizens’ access to municipal services and facilities, as well as to increase citizens’ impact on the future of their cities. The main objective of the research was to identify the way in which authorities of the selected cities consider the subject matter related to the Smart City and 4T capitals when formulating local policies. The aim of this article is to analyse the innovative potential in selected cities of the Metropolis GZM, in the light of academic entrepreneurship, the innovativeness of citizens, entities supporting innovativeness, and soliciting talent. The research was based on a systematic literature review and the analysis of local documents, supplemented by direct interviews with local managers and participant observation.
Intense Pasture Management in Brazil in an Integrated Crop-Livestock System Simulated by the DayCent Model
Authors: Silva Yane Freitas , Valadares Rafael Vasconcelos , Dias Henrique Boriolo , Cuadra Santiago Vianna , Campbell Eleanor E. , Lamparelli Rubens A. C. , Moro Edemar , Battisti Rafael , Alves Marcelo R. , Magalhães Paulo S. G. , Figueiredo Gleyce K. D. A.
Published: March 2022

Process-based models (PBM) are important tools for understanding the benefits of Integrated Crop-Livestock Systems (ICLS), such as increasing land productivity and improving environmental conditions. PBM can provide insights into the contribution of agricultural production to climate change and help identify potential greenhouse gas (GHG) mitigation and carbon sequestration options. Rehabilitation of degraded lands is a key strategy for achieving food security goals and can reduce the need for new agricultural land. This study focused on the calibration and validation of the DayCent PBM for a typical ICLS adopted in Brazil from 2018 to 2020. We also present the DayCent parametrization for two forage species (ruzigrass and millet) grown simultaneously, bringing some innovation in the modeling challenges. We used aboveground biomass to calibrate the model, randomly selecting data from 70% of the paddocks in the study area. The calibration obtained a coefficient of determination (R2) of 0.69 and a relative RMSE of 37.0%. During the validation, we used other variables (CO2 flux, grain biomass, and soil water content) measured in the ICLS and performed a double validation for plant growth to evaluate the robustness of the model in terms of generalization. R2 validations ranged from 0.61 to 0.73, and relative RMSE from 11.3 to 48.3%. Despite the complexity and diversity of ICLS results show that DayCent can be used to model ICLS, which is an important step for future regional analyses and large-scale evaluations of the impacts of ICLS.
Is Outdoor Recreation Associated with Greater Climate Change Concern in the United States?
Authors: Knight Kyle W. , Hao Feng
Published: March 2022

There has been extensive research on the association between environmental attitudes and outdoor recreation (or nature-based leisure activities) since the 1970s. There is now considerable evidence to support the claim that spending time in nature leads to greater connectedness to nature and thereby greater pro-environmental attitudes and behavior. However, there is an absence of research focused specifically on the association between outdoor recreation and concern for climate change, which is arguably the most pressing environmental problem facing the world today. We build on previous research by using the 2021 General Social Survey and structural equation modeling to analyze the association between frequency of engaging in outdoor recreation and concern for climate change among adults in the United States, with special attention to the role of enjoying being in nature. Controlling for other factors, we find that frequency of outdoor recreation has a positive, significant effect on climate change concern, but only indirectly via enjoyment of nature. Individuals who more frequently engage in outdoor recreation activities tend to report a greater sense of enjoyment of being outside in nature, and this enjoyment of nature is associated with a higher level of concern for climate change.
Effect of Environmental Planning on Elderly Individual Quality of Life in Severe Cold Regions: A Case Study in Northeastern China
Authors: Leng Hong , Han Bingbing
Published: March 2022

With the development of urbanization and the ageing population, the improvement of the urban environment and the quality of life (QOL) of the elderly in cities with a cold climate have become critical issues to be addressed. However, only a few studies have focused on this aspect. According to a review of the literature, the contents of the built environment (BE) in severe cold regions are defined as thirteen key factors of four categories (density, environmental aesthetics, outdoor environment, and accessibility) and the QOL of old people consists of three aspects (residential, health, and social satisfaction). This study explores how BE variables are associated with the QOL of older adults by using ordered logit and gologit2 models. The data consist of the results of 1945 questionnaires from field surveys in 11 different residential areas, across two cities in northeastern China. The results show: (1) Walkability in winter, distance to a public park of 400–1000 m, outdoor shelters and seating, less than five buses available, and a mixture of evergreen and deciduous trees are five of the most important variables of BE that have a significant positive impact on QOL compared with other climatic regions; (2) “Pocket parks” and pedestrian walkway safety are appropriate approaches to improve wellbeing under local economic conditions. For public transportation, metro and rail transit systems are encouraged, and some rules are needed to reduce the number of buses in harsh weather conditions; (3) Compared with spatial distribution in other climates, the scope of a 15 min city should be less than 1km in severe cold areas. From the findings, we conclude that there are six possible pattern languages to improve the urban environment, and they can provide information for further study on environmental planning in severe cold regions.
Asphaltene or Polyvinylchloride Waste Blended with Cement to Produce a Sustainable Material Used in Nuclear Safety
Authors: Saleh Hosam M. , Bondouk Ibrahim I. , Salama Elsayed , Mahmoud Hazem H. , Omar Khalid , Esawii Heba A.
Published: March 2022

The current research uses sustainable methods to preserve the environment, such as exploiting municipal or industrial waste that may harm the environment. The wreckage of polyvinyl chloride (PVC) pipes and asphaltene are used as additives to cement to improve its mechanical properties, while stabilizing the radioactive waste resulting from the peaceful uses of nuclear materials, or enhancing its radiation shielding efficiency. New composites of Portland cement with ground PVC or asphaltene up to 50% are investigated. Fast neutron removal cross-section (ƩR) and gamma shielding parameters, such as mass attenuation coefficient (MAC), half-value layer (HVL), effective atomic number (Zeff), and exposure build-up factor (EBF) at wide energy range and thickness, are determined. The compressive strength and apparent porosity of the examined composites are examined to test the durability of the prepared composites as stabilizers for radioactive waste. The obtained results show that the bulk density of hardened cementitious composites was slightly increased by increasing the additive amount of PVC or asphaltene. The compressive strength of cement composites reached more than 4.5 MP at 50 wt.% PVC and 8.8 MPa at 50 wt.% asphaltene. These values are significantly higher than those recommended by the US Nuclear Regulatory Commission (3.4 MPa). Additionally, the obtained results demonstrate that although the gamma MAC is slightly decreased by adding asphaltene or PVC, the neutron removal cross-section was highly increased, reaching 171% in the case of 50 wt.% asphaltene and 304% in the case of 50 wt.% PVC. We can conclude that cement composites with PVC or asphaltene have optimized radiation shielding properties and can stabilize radioactive waste.
Socio-Economic Factors Influencing Agritourism Farm Stays and Their Safety during the COVID-19 Pandemic: Evidence from Poland
Authors: Zawadka Jan , Jęczmyk Anna , Wojcieszak-Zbierska Monika Małgorzata , Niedbała Gniewko , Uglis Jarosław , Pietrzak-Zawadka Joanna
Published: March 2022

The article presents reasons for the choices and opinions of tourists regarding rest and its safety in agritourism farms in Poland during the COVID-19 pandemic in 2020. We used the diagnostic survey method. We distributed the questionnaire via the Internet on Facebook in thematic groups related to tourism. Facebook respondents were also asked to provide a link to the questionnaire to their tourism-active friends (snowball sampling). We conducted the survey in January 2021. Research has shown that well-educated and well-off residents of cities most often chose agritourism. The main reason for the choice is the possibility of rest in peace and quiet, natural values, and an attractive price. Notably, during the pandemic, an important reason for choosing agritourism was to convince tourists that the risk of coronavirus infection was relatively low. This is also evidenced by the fact that most of them visited an agritourism farm for the first time during the pandemic. The attractiveness of staying on an agritourism farm was assessed highly (almost all respondents expressed their willingness to take advantage of this form of leisure again). This is due to the high evaluation of the quality of services provided, the offer’s attractiveness, and price competitiveness.
The Power of Sustainability in the “Black Swan” Event: Entrepreneurial Cognition of Top Management Team and Dual Business Model Innovation
Authors: Ni Yuan , Wang Jia , Li Cui
Published: March 2022

In the dynamic environment where “Black Swan” events occur frequently, the dual business model innovation (DBMI) which has both proactive and reactive characteristics is the core force for the enterprises to turn crises into opportunities and achieve their survival and sustainability. However, prevailing views do not clearly explain how to drive dual business model innovation. Based on the upper echelon theory, this study developed a multiple mediation model, which links entrepreneurial cognition of the top management team (TMT), knowledge search with dual business model innovation. By taking the data of 217 TMTs, the hypotheses are verified. The results show that TMT’s configuration cognition, willing cognition, and ability cognition all have a positive effect on both proactive and reactive business model innovation. Knowledge search acts as a “bridge” between TMT’s entrepreneurial cognition and DBMI. Greater entrepreneurial cognition can guide exploratory and exploitative knowledge searches and promote the DBMI. The results also show the mediating effect between different entrepreneurial cognition and DBMI is not completely consistent, and a partial mediation effect exists associating configuration cognition with DBMI, but a full mediation effect is present between other cognitions and DBMI. These results provide more understanding to the formation of dual business model innovation under the impact of COVID-19.
Topic Modelling for Ski Resorts: An Analysis of Experience Attributes and Seasonality
Authors: Shang Ziye , Luo Jian Ming , Kong Anthony
Published: March 2022

Knowing how to improve skiers’ experiences in ski resorts is vital for developing the ski industry. This study aims to provide a holistic understanding of the key attributes of skiers’ experiences and explore them in the context of seasonality. Based on the user-generated content of 14 ski resorts and the topic modelling and sentiment analysis method, a framework of skiing experience attributes was built. Compared with the seasonal data, the dynamic of skiers’ concerns and perceived performance was revealed. The skiers’ concerns in peak seasons and off seasons manifested different orientations. The results show that the relatively important attributes tend to have relatively low performance in the peak seasons. In off seasons, skiers emphasise non-skiing-oriented attributes. This study showcases that skier’s interests and evaluations of various experience attributes vary with seasons. The findings help to understand the skiers’ peak and supporting experiences, which could be used to build ski resorts management and seasonal hedging strategies.
Impact of the Manufacturing Processes of Aromatic-Polymer-Based Carbon Fiber on Life Cycle Greenhouse Gas Emissions
Authors: Sakamoto Kaito , Kawajiri Kotaro , Hatori Hiroaki , Tahara Kiyotaka
Published: March 2022

Carbon fibers (CFs) are promising lightweight materials to reduce vehicle fuel consumption. However, the most widely used polyacrylonitrile (PAN)-based CF production process consumes a considerable amount of energy. A novel production process for CFs from aromatic polymers (APs) is proposed as an alternative. In this study, the greenhouse gas (GHG) emissions from PAN-based CFs, from APs using the classical benzidine method, and from APs using the coupling method on a cradle-to-gate basis, were analyzed. The results indicate that the AP CFs with the classical benzidine method generated 11% fewer GHG emissions compared with the conventional PAN CFs. Emissions were further reduced by 42% using a large-tow production process. As the classical benzidine method for manufacturing CFs from APs uses a monomer synthesized via benzidine, which is carcinogenic, we examined a different synthetic route using the coupling method for monomer synthesis to avoid the benzidine intermediate. The GHG emissions from the AP CFs manufactured by the coupling method showed a 51% increase compared with PAN-based CFs, indicating a trade-off between GHG emissions and carcinogenicity. However, with proper chemical management, the classical method of CF manufacturing from APs via benzidine showed reduced GHG emissions.
Impact of Gulf Cooperation Countries’ Foreign Direct Investment on Sudan’s Agricultural Exports
Authors: Alnafissa Mohamad , Abdeen Mohammed , Bashir Kamaleldin , Alamri Yosef , Alagsam Fuad , Al-Duwais Abdulaziz
Published: March 2022

Agricultural foreign direct investment (AFDI) contributes to the long-term growth of developing countries. Sudan has rich agricultural resources with great potential for AFDI. However, so far, Sudan has not exploited this potential to attract investors from other countries, having less of a competitive advantage in agricultural production owing to local and international problems. In this study, we examined the effects of foreign investments of Gulf Cooperation Council (GCC) countries on agricultural exports in Sudan, in addition to other economic factors during 1990–2016 using the two-stage least squares (2SLS) model. The results showed that the investments of GCC countries in Sudan’s agricultural sector boosted agricultural exports. The exchange rate was found to be a key determinant factor of GCC countries deciding to invest in Sudan. We recommend that the Central Bank of Sudan encourage policies to stabilize the exchange rate to attract more agricultural investment from GCC countries.
Investigating the Role of Green Hotel Sustainable Strategies to Improve Customer Cognitive and Affective Image: Evidence from PLS-SEM and fsQCA
Authors: Salem Islam Elbayoumi , Elbaz Ahmed Mohamed , Al-Alawi Alamir , Alkathiri Nasser Alhamar , Rashwan Kareem A.
Published: March 2022

Partial least square structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used to uncover the determinants of customers’ overall positive image of green hotels, which can affect behavioral intentions. A sample of 323 customers of 54 four- and five-star “green” hotels in the Sultanate of Oman were surveyed. The findings can explain the complex process shaping customers’ perceived image. The fsQCA analysis shows that the overall positive image of green hotels was driven by the interaction of two core conditions, environmental values and cognitive image, and one peripheral condition, low-carbon knowledge. Together, these factors are sufficient to form a favorable image of eco-hotels and could make a favorable affective image unnecessary. The PLS-SEM approach clarifies that this process takes place through an indirect mechanism wherein environmental values and low-carbon knowledge shape customers’ cognitive image, which then boosts the overall positive image of eco-hotels. Such comprehensive insights address the gap in the body of literature vis à vis the influence of customers’ psychological factors through cognitive and affective images. Theoretical and practical implications are examined.
Multi-Criteria Assessment of the Economic and Environmental Sustainability Characteristics of Intermediate Wheatgrass Grown as a Dual-Purpose Grain and Forage Crop
Authors: Law Eugene P. , Wayman Sandra , Pelzer Christopher J. , Culman Steven W. , Gómez Miguel I. , DiTommaso Antonio , Ryan Matthew R.
Published: March 2022

Kernza® intermediate wheatgrass [IWG; Thinopyrum intermedium (Host) Barkworth & Dewey] is a novel perennial cool-season grass that is being bred for use as a dual-purpose grain and forage crop. The environmental benefits of perennial agriculture have motivated the development of IWG cropping systems and markets for perennial grain food products made with Kernza, but the economic viability and environmental impact of IWG remain uncertain. In this study, we compared three-year cycles of five organic grain production systems: an IWG monoculture, IWG intercropped with medium red clover, a continuous winter wheat monoculture, a wheat–red clover intercrop, and a corn–soybean–spelt rotation. Economic and environmental impacts of each cropping system were assessed using enterprise budgets, energy use, greenhouse gas (GHG) emissions, and emergy indices as indicators. Grain and biomass yields and values for production inputs used in these analyses were obtained from experimental data and management records from two separate field experiments conducted in New York State, USA. Grain yield of IWG averaged 478 kg ha−1 yr−1 over three years, equaling approximately 17% of winter wheat grain yield (2807 kg ha−1 yr−1) over the same period. In contrast, total forage harvested averaged 6438 kg ha−1 yr−1 from the IWG systems, approximately 160% that of the wheat systems (4024 kg ha−1 yr−1). Low grain yield of IWG greatly impacted economic indicators, with break-even farm gate prices for Kernza grain calculated to be 23% greater than the current price of organic winter wheat in New York. Energy use and GHG emissions from the IWG systems were similar to the annual systems when allocated per hectare of production area but were much greater when allocated per kg of grain produced and much lower when allocated per kg of biomass harvested inclusive of hay and straw. Emergy sustainability indices were favorable for the IWG systems due to lower estimated soil erosion and fewer external inputs over the three-year crop cycle. The results show that the sustainability of IWG production is highly dependent on how the hay or straw co-product is used and the extent to which external inputs can be substituted with locally available renewable resources. Integrated crop–livestock systems appear to be a viable scenario for the adoption of IWG as a dual-use perennial grain and forage crop.
Exploring the Roles of Renewable Energy, Education Spending, and CO2 Emissions towards Health Spending in South Asian Countries
Authors: Mehmood Usman , Agyekum Ephraim Bonah , Kamel Salah , Shahinzadeh Hossein , Moshayedi Ata Jahangir
Published: March 2022

This research is mainly aimed at determining the effect of renewable energy (RE), education expenditures, and CO2 emissions on health expenditures in selected South Asian countries. There is an insufficient number of studies that investigate the linkages between health expenditures (HE) and CO2 emissions in South Asian countries. This study combined RE and gross domestic product (GDP) to identify their effect on health spending. We utilized the annual data of 1990–2018, and applied FMOLS and DOLS estimators over the panel data of five South Asian countries. According to the DOLS and FMOLS long-run results, GDP, RE, and education expenditures are negatively associated with health expenditures. This suggests that renewable energy puts less pressure on environmental quality, which leads to less health spending in the five South Asian countries studied. The empirical results also show that HE and CO2 emissions are positively and significantly related, which implies that an increase in CO2 emissions increases the financial burden on the various countries’ health sector. This study, therefore, recommends the usage of renewable sources to improve public health and to help lower health expenditures. To achieve sustainable development, it is also important to increase investment in the educational sector in the various countries.
Rethinking Assessment: The Future of Examinations in Higher Education
Authors: Gamage Kelum A. A. , Pradeep Roshan G. G. R. , de Silva Erandika K.
Published: March 2022

The global higher education landscape is significantly impacted as a result of the COVID-19 pandemic and the majority of the universities now follow an online or hybrid mode of delivery. This presents substantial challenges for universities, particularly to conduct examinations, as traditionally most exams were conducted physically on campus. During the first wave of the pandemic, many universities had no option and were forced to move online in a very short period of time, causing universities also to conduct exams online without transforming pedagogy and the structure/s of closed-book exams. Inevitably, in non-proctored and unregulated examinations, this allowed room for students to collaborate and share material during online exams without being noticed by an invigilator as in the case of physical exams. Online exams, also leave room for students to find information online which made preventing plagiarism a significant challenge. This paper investigates the practices used in both closed-book and open-book exams and identifies the challenges associated with the transition to online exams. It also identifies potential ways forward for future online exams, while minimizing opportunities for students to collaborate, plagiarise and use online material. The findings of this study reveal that online examinations affect teachers and students differently: while teachers have mixed feelings about online exams, students are anxious about their grades and the technical hassle they experience in online exams. While viva has emerged as a popular form of alternative assessment, students still feel the need of returning to physical exams. None of the teachers who participated in this study discussed a psychosocial approach to education and exams in this pandemic. We conclude this study on the note that there is a need for the collaboration of social scientists, psychologists, psychosocial specialists, educationists, and humanities scholars/humanists for better educational policy and pedagogical practices during the pandemic.