bluuebunny/crossref_metadata_2025 huggingface dataset:
From Love Jihad to Grooming Gangs: Tracing Flows of the Hypersexual Muslim Male through Far-Right Female Influencers
Authors: Leidig Eviane
Published: December 2021

This article traces the transnational flows of constructions of the hypersexualized Muslim male through a comparative analysis of love jihad in India and the specter of grooming gangs in the UK. While the former is conceived as an act of seduction and conversion, and the latter through violent rape imaginaries, foregrounding both of these narratives are sexual, gender, and family dynamics that are integral to the fear of demographic change. Building upon these narratives, this study analyzes how influential women in Hindu nationalist and European/North American far-right milieus circulate images, videos, and discourses on social media that depict Muslim men as predatory and violent, targeting Hindu and white girls, respectively. By positioning themselves as the daughters, wives, and mothers of the nation, these far-right female influencers invoke a sense of reproductive urgency, as well as advance claims of the perceived threat of, and safety from, hypersexualized Muslim men. This article illustrates how local ideological narratives of Muslim sexuality are embedded into global Islamophobic tropes of gendered nationalist imaginaries.
Saving Nation, Faith and Family. Yoram Hazony’s National Conservativism and Its Theo-Political Mission
Authors: Quast-Neulinger Michaela
Published: December 2021

Particularly pushed by the Edmund Burke Foundation and its president Yoram Hazony, the political movement of National Conservativism is largely based on specific concepts of nation, faith and family. Driven by the mission to overcome the violence of liberalism, identified with imperialism, national conservatives shape potent international and interreligious alliances for a religiously based system of independent national states. The article gives an outline of the main programmatic pillars of National Conservativism at the example of Yoram Hazony’s The Virtue of Nationalism, one of the current ideological key works of the movement. It will show how its political framework is based on a binary frame of liberalism (identified with imperialism) versus nationalism, the latter supported as the way forward towards protecting freedom, faith and family. The analytic part will focus on the use of religious motifs and the construction of a specific kind of Judaeo-Christianism as a means of exclusivist theo-political nationalism. It will be shown that Hazony’s nationalism is no way to overcome violence, but a political theory close to theo-political authoritarianism, based on abridged readings of Scripture, history and philosophy. It severely endangers the foundations of democracies, especially with regard to minority and women’s rights, and delegitimizes liberal democracy and religious traditions positively contributing to it.
All Is Burning: Buddhist Mindfulness as Radical Reflection
Authors: Vörös Sebastjan
Published: December 2021

This paper consists of two parts. In the first part (Section 1, part of Section 2), I put forward a critique of what I refer to as the ‘received’ or ‘standard’ view of mindfulness in the Western cultural milieu. According to the received view, mindfulness is the acontextual ‘core’ of Buddhism whose determining characteristic is bare (present-oriented, non-judgmental) attention to the flow and content of experience. As noted by many researchers, this conception is in stark contrast to the traditional Buddhist understanding, where mindfulness is not only embedded in a broader context that provides it with a specific philosophico-existential orientation (normative aspect) but is also construed as a reflective activity (noetic aspect). In the second part (part of Sections 2–4), I argue that one of the main issues with the standard view is that it frames experience in terms of what Maurice Merleau-Ponty calls ‘objective thought’ (using objectivity, or ‘thinghood’, as an onto-epistemological standard of reality), which makes the two aspects of the traditional conception (normative and noetic) unintelligible. I then provide an alternative view based on the phenomenological work of Maurice Merleau-Ponty that attempts to integrate the two aspects into a broader conception of experience. By drawing on Merleau-Ponty’s notions of ‘phenomenal field’ and ‘radical reflection’, I argue that mindfulness needs to be understood as a reflective attitude that allows one to discern not only the content but also, and primarily, the context of each experience, and that this also includes seeing itself—the act of reflection—as an act that stems from, and returns back into, the pre-reflective current of existence.
Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification
Authors: Reuß Felix , Greimeister-Pfeil Isabella , Vreugdenhil Mariette , Wagner Wolfgang
Published: December 2021

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications.
Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations
Authors: Castelli Elisa , Papandrea Enzo , Di Roma Alessio , Bloise Ilaria , Varile Mattia , Tabani Hamid , Gastellu-Etchegorry Jean-Philippe , Feruglio Lorenzo
Published: December 2021

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.
Extending the DMSP Nighttime Lights Time Series beyond 2013
Authors: Ghosh Tilottama , Baugh Kimberly E. , Elvidge Christopher D. , Zhizhin Mikhail , Poyda Alexey , Hsu Feng-Chi
Published: December 2021

Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights.
A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation
Authors: Prill Robert , Walter Marina , Królikowska Aleksandra , Becker Roland
Published: December 2021

In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation.
Fiber Bragg Grating-Based Sensors and Systems
Authors: Morozov Oleg G.
Published: December 2021

Today, no one doubts that fiber Bragg gratings (FBGs) have become the most used tool for measuring various physical parameters, the structural integrity of engineering systems, and the biological activity of living systems [...]
CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method
Authors: Hu Xinyi , Gu Chunxiang , Chen Yihang , Wei Fushan
Published: December 2021

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.
Internal Damage Detection of Composite Structures Using Passive RFID Tag Antenna Deformation Method: Basic Research
Authors: Pecho Pavol , Hrúz Michal , Novák Andrej , Trško Libor
Published: December 2021

This manuscript deals with the detection of internal cracks and defects in aeronautical fibreglass structures. In technical practice, it is problematic to accurately determine the service life or MTBF (Mean Time Between Failure) of composite materials by the methods used in metallic materials. The problem is mainly the inhomogeneous and anisotropic structure of composites, possibly due to the differences in the macrostructure during production, production processes, etc. Diagnostic methods for detecting internal cracks and damage are slightly different, and in practice, it is more difficult to detect defects using non-destructive testing (NDT). The article deals with the use of Radio frequency identification (RFID) technology integrated in the fibreglass laminates of aircraft structures to detect internal defects based on deformation behaviour of passive RFID tag antenna. The experiments proved the potential of using RFID technology in fibreglass composite laminates when using tensile tests applied on specimens with different structural properties. Therefore, the implementation of passive RFID tags into fibreglass composite structures presents the possibilities of detecting internal cracks and structural health monitoring. The result and conclusion of the basic research is determination of the application conditions for our proposed technology in practice. Moreover, the basic research provides recommendations for the applied research in terms of the use in real composite airframe structures.
Efficient Authentication Protocol and Its Application in Resonant Inductive Coupling Wireless Power Transfer Systems
Authors: Ahene Emmanuel , Ofori-Oduro Mark , Twum Frimpong , Walker Joojo , Missah Yaw Marfo
Published: December 2021

Chaos theory and its extension into cryptography has generated significant applications in industrial mixing, pulse width modulation and in electric compaction. Likewise, it has merited applications in authentication mechanisms for wireless power transfer systems. Wireless power transfer (WPT) via resonant inductive coupling mechanism enables the charging of electronic devices devoid of cords and wires. In practice, the key to certified charging requires the use of an authentication protocol between a transmitter (charger) and receiver (smartphone/some device). Via the protocol, a safe level and appropriate charging power can be harvested from a charger. Devoid of an efficient authentication protocol, a malicious charger may fry the circuit board of a receiver or cause a permanent damage to the device. In this regard, we first propose a chaos-based key exchange authentication protocol and analyze its robustness in terms of security and computational performance. Secondly, we theoretically demonstrate how the protocol can be applied to WPT systems for the purposes of charger to receiver authentication. Finally, we present insightful research problems that are relevant for future research in this paradigm.
Peptide-Based Capture of Chikungunya Virus E2 Protein Using Porous Silicon Biosensor
Authors: Layouni Rabeb , Cao Tengfei , Coppock Matthew B. , Laibinis Paul E. , Weiss Sharon M.
Published: December 2021

The detection of pathogens presents specific challenges in ensuring that biosensors remain operable despite exposure to elevated temperatures or other extreme conditions. The most vulnerable component of a biosensor is typically the bioreceptor. Accordingly, the robustness of peptides as bioreceptors offers improved stability and reliability toward harsh environments compared to monoclonal antibodies that may lose their ability to bind target molecules after such exposures. Here, we demonstrate peptide-based capture of the Chikungunya virus E2 protein in a porous silicon microcavity biosensor at room temperature and after exposure of the peptide-functionalized biosensor to high temperature. Contact angle measurements, attenuated total reflectance—Fourier transform infrared spectra, and optical reflectance measurements confirm peptide functionalization and selective E2 protein capture. This work opens the door for other pathogenic biomarker detection using peptide-based capture agents on porous silicon and other surface-based sensor platforms.
Tracking Systems Used to Monitor the Performance and Activity Profile in Elite Team Sports
Authors: Felipe Jose Luis , Garcia-Unanue Jorge , Gallardo Leonor , Sanchez-Sanchez Javier
Published: December 2021

Technology has become established in elite sport in recent years and is used on a regular basis, especially in team sports [...]
A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation
Authors: Mukherjee Moumita , Banerjee Avijit , Papadimitriou Andreas , Mansouri Sina Sharif , Nikolakopoulos George
Published: December 2021

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.
Corporate Social Responsibility: Organizational Strategy for Sustainable Growth
Authors: Xiao Shufeng(Simon) , Park Byung Il
Published: December 2021

Welcome to the special issue of Sustainability on “corporate social responsibility: organizational strategy for sustainable growth [...]
The Physical Damage of Climbing Activity on Sandstone Lichen Cover
Authors: Schweizer Anne-Maria , Höschler Lucas , Steinbauer Manuel J.
Published: December 2021

Climbing activities affect cliff site species. With cliff sites harbouring unique species communities, the rise in popularity of outdoor climbing activities is a major threat. In this study, we assessed a previously unclimbed boulder before, during and after 500 climbing ascents. We observed an overall reduction in lichen cover by 4.2–9.5%, located around the footholds and combined foot- and handhold but not the handhold. We found the reduction in lichen cover to be strongest at the very start of the climbing treatment and to lessen over time. Therefore, management should focus on directing climbing activities to selected sites, while protecting sites with high conservation value where climbing is prohibited entirely.
Sustainable FinTech Innovation Orientation: A Moderated Model
Authors: Al-Okaily Manaf , Al Natour Abdul Rahman , Shishan Farah , Al-Dmour Ahmed , Alghazzawi Rasha , Alsharairi Malek
Published: December 2021

Financial technology (otherwise known as FinTech) refers to a type of technology and innovation that tries to improve and automate the delivery and use of financial services. Despite the importance of this technology in people’s financial transactions in improving the management of their financial operations, processes, and lives, there is a lack of empirical evidence about sustainable FinTech services in the Jordanian context. Consequently, this research examines the factors that influence the acceptance of FinTech services, which have a variety of social, environmental, and ecological benefits. This study proposes an integrated model by combining the extended technology acceptance model (TAM) with the perceived enjoyment as an independent variable and electronic word of mouth (eWOM) as a moderator variable simultaneously. A total of 304 responses from Jordanian citizens were analyzed by the quantitative method of partial least squares structural equation modelling (PLS-SEM). The result confirmed that perceived usefulness and perceived enjoyment have a significant and positive influence on users’ decision to use FinTech services. Meanwhile, eWOM is found to moderate the relationship between perceived usefulness and Jordanians’ decisions to use FinTech services. Finally, this study provides practical implications for managers to encourage them to provide adequate, reliable, and sustainable services to their customers at a reasonable cost that fit their demands and ultimately improve their living standards. Current study limitations and future research directions are presented in the last section.
A Participatory Approach to Identify Key Areas for Sustainable Work Environment and Health in Employees with Flexible Work Arrangements
Authors: Bjärntoft Sofie , Hallman David M. , Zetterberg Camilla , Larsson Johan , Edvinsson Johanna , Jahncke Helena
Published: December 2021

Flexible work arrangements are common worldwide, but knowledge on how to achieve a sustainable work environment is sparse. The aim of this study was to use a participatory approach to identify concrete suggestions and key areas for improvement that were considered relevant, effective, and feasible for promoting good work environment and health at organizational, work group and individual level (O-G-I), among office employees with flexible work arrangements. Eight focus group interviews (including 45 employees) were conducted in a large Swedish government agency in 2017. By using a Tree diagram approach, employees made a total of 279 suggestions for improvements, which were sorted into O-G-I levels and mapped into 18 key areas. We found that 13 key areas addressed organizational level (e.g., improving leadership, policy, job demands, and work efficiency), two key areas addressed group level (create common rules of availability and activity-based working), and three key areas addressed individual level (e.g., individuals’ responsibility to clearly communicate their availability). The participatory process was effective in obtaining concrete suggestions and key areas in need of improvement, which may provide an action plan that can guide organizations in developing interventions to promote good work environment and health in flexible work.
Twin Transition through the Implementation of Industry 4.0 Technologies: Desk-Research Analysis and Practical Use Cases in Europe
Authors: Ortega-Gras Juan-José , Bueno-Delgado María-Victoria , Cañavate-Cruzado Gregorio , Garrido-Lova Josefina
Published: December 2021

Key Enabling Technologies (KET) support the adoption of Industry 4.0 (I4.0) and are also considered the main drivers of the Circular Economy (CE) transition. In this respect, the guidelines and real use cases to inspire enterprises and industry to lead the twin digital and green transition are still poor. This work is aimed at contributing to this matter, with twofold goals: on the one hand, to show a depth desk-research analysis of the key existing policies at European level that foster this twin digital and green transition; on the other hand, to review practical use cases and international projects where CE practices are boosted through the implementation of KET. From the analysis, a set of recommendations are suggested as a guide for policymakers, researchers, and industry managers on how to foster the CE through the implementation of I4.0 technologies.
Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting
Authors: Salman Diaa , Kusaf Mehmet
Published: December 2021

Unit Commitment (UC) is a complicated integrational optimization method used in power systems. There is previous knowledge about the generation that has to be committed among the available ones to satisfy the load demand, reduce the generation cost and run the system smoothly. However, the UC problem has become more monotonous with the integration of renewable energy in the power network. With the growing concern towards utilizing renewable sources for producing power, this task has become important for power engineers today. The uncertainty of forecasting the output power of renewable energy will affect the solution of the UC problem and may cause serious risks to the operation and control of the power system. In power systems, wind power forecasting is an essential issue and has been studied widely so as to attain more precise wind forecasting results. In this study, a recurrent neural network (RNN) and a support vector machine (SVM) are used to forecast the day-ahead performance of the wind power which can be used for planning the day-ahead performance of the generation system by using UC optimization techniques. The RNN method is compared with the SVM approach in forecasting the wind power performance; the results show that the RNN method provides more accurate and secure results than SVM, with an average error of less than 5%. The suggested approaches are tested by applying them to the standard IEEE-30 bus test system. Moreover, a hybrid of a dynamic programming optimization technique and a genetic algorithm (DP-GA) are compared with different optimization techniques for day ahead, and the proposed technique outperformed the other methods by 93,171$ for 24 h. It is also found that the uncertainty of the RNN affects only 0.0725% of the DP-GA-optimized UC performance. This study may help the decision-makers, particularly in small power-generation firms, in planning the day-ahead performance of the electrical networks.
Challenges and Responses of Agri-Food Activities under COVID-19 Pandemic: The Case of the Spanish Territories Producing Wine and Olive Oil
Authors: Rodríguez-Cohard Juan Carlos , Juste-Carrión Juan José , Vázquez-Barquero Antonio
Published: December 2021

The COVID-19 pandemic has deeply affected economic activities worldwide. The challenge for territories and companies has been how to cope with mobility restrictions. Even in the case of essential activities such as agri-food industries, the adaptation has been a challenging issue to deal with. The paper aims to show how the wine and olive oil industries in Spain have restructured their activities in order to respond to the confinement and the new normalcy, using new technologies as a strategic tool, but also making the most of new actions to keep their presence in the national and international markets. The research was carried out mostly through qualitative analysis, using the in-depth interview as a main tool to gain strategic information from managers of companies and local policymakers. Results show that when local policy makers and managers cooperate, despite the different perceptions they could have, the outcome is positive for facing competitive shocks and carving out new local initiatives, making firms and the territory itself more resilient.
Wind Conditions at Pedestrian Level in Different Types of Residential Urban Development for a High Degree of Land Use Efficiency
Authors: Zielonko-Jung Katarzyna , Poćwierz Marta , Idem Robert
Published: December 2021

This paper addresses wind conditions in urban building development at the pedestrian level. The article aimed to identify aerodynamic phenomena around three types of multi-family housing developments with different forms and the same urban parameters of building development density (high density was taken into account). The aim of the research was mainly to achieve qualitative results that would lead to understanding fundamental processes and phenomena. Wind tunnel experimental studies were conducted on physical models at a scale of 1: 400 using visualization and erosion methods. These experiments yielded data regarding the arrangement of airflow directions and changes in airflow velocity, expressed as the amplification coefficient (α), the occurrence of which was caused by the presence of buildings. An analysis was conducted concerning wind conditions that constitute pedestrian comfort and influence the possibility for ventilation of spaces between buildings for the three selected models. The research results were compared, and an attempt was made to assess the most beneficial and the least favorable building development types in this respect.
Can Industrial Integration Improve the Sustainability of Grain Security?
Authors: Cao Chen , Chen Xueyun
Published: December 2021

The sustainability to ensure grain security is an important issue that China’s government faces. It has become more important to ensure grain security by improving the efficiency of grain production under the constraints of limited resources. At present, industrial integration has become an important trend of economic development in China. This study explores the impact of industrial integration on the sustainability of grain security from the perspective of grain production efficiency. In this paper, the grain production efficiency was measured by stochastic frontier analysis method. The degree of industrial integration is measured by the coupling coordination method. Additionally, the effect of industrial integration on grain production efficiency is empirically studied by the panel tobit model. Empirical results show that industrial integration can promote the improvement of grain production efficiency, which reveals that accelerating industrial integration can also be conducive to grain security.
Teaching Sustainable Development in a Sensory and Artful Way—Concepts, Methods, and Examples
Authors: Heinrichs Harald
Published: December 2021

Despite significant short-term pressures such as the recent Coronavirus pandemic with its economic and social disruptions, longer-term environmental un-sustainability and its projected intergenerational consequences remain a major threat for the future of mankind. More and new efforts are required in all social spheres with regard to the universal Sustainable Development Goals. In this context, the present article makes the argument for teaching sustainable development in higher education with a more sensory and artful approach, in order to raise students’ awareness of the multisensory reality of human existence and develop skills to engage creatively for sustainability transformations. Rooted in the perspective of sensory and arts-based sustainability science, three experimental bachelor courses—designed and conducted by the author of this article in collaboration with artists—with twenty to twenty-five students in each course from diverse disciplinary backgrounds in environmental studies, cultural studies, and social sciences are presented and discussed. It is argued that the specific course design and the scientific-artistic co-teaching provide an innovative way to teach sustainability topics in a more sensory way. The article ends with an outlook on potentials and challenges of this approach.
Optimizing Yield and Quality of Silage and Hay for Pea–Barley Mixtures Ratio under Irrigated Arid Environments
Authors: Soufan Walid , Al-Suhaibani Nasser A.
Published: December 2021

The changing climate, water scarcity, reduction in the land area under forage crops, expanding ruminant population, burgeoning demands for milk and meat production and food security concerns all insist on a necessary increase in forage production and quality. Cereal–legume mixed intercropping offers a biologically superior option to boost herbage yield and quality of preserved fodder (hay and silage), provided that rationalization of the component crop’s proportional ratio is performed. Therefore, the current study was conducted to determine the forage yield and quality of barley (Hordeum vulgare L.) and pea (Pisum sativum L.) mixtures at the ratios 100:0, 75:25, 50:50, 25:75 and 0:100. The experiment was laid out in the regular arrangement of a randomized complete block design with three replicates. The results exhibited that forage quality was significantly affected by the mixture ratio. There was a significant improvement in protein, fiber rate and digestibility from the mixture. The ratios of barley and pea with 50:50 and 25:75 produced the maximum productivity and the best quality of forage. Regarding forage preservation, silage recorded higher contents of crude protein, ash and lower fiber contents than hay. Among seed mixtures, 0:100 ratio of barley–pea yielded the highest quality of silage with the maximum crude protein, ash and digestibility with lowest fiber contents.
Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm
Authors: Montoya Oscar Danilo , Grisales-Noreña Luis Fernando , Perea-Moreno Alberto-Jesus
Published: December 2021

The problem of the optimal siting and sizing of photovoltaic (PV) sources in grid connected distribution networks is addressed in this study with a master–slave optimization approach. In the master optimization stage, a discrete–continuous version of the Chu and Beasley genetic algorithm (DCCBGA) is employed, which defines the optimal locations and sizes for the PV sources. In the slave stage, the successive approximation method is used to evaluate the fitness function value for each individual provided by the master stage. The objective function simultaneously minimizes the energy purchasing costs in the substation bus, and the investment and operating costs for PV sources for a planning period of 20 years. The numerical results of the IEEE 33-bus and 69-bus systems demonstrate that with the proposed optimization methodology, it is possible to eliminate about 27% of the annual operation costs in both systems with optimal locations for the three PV sources. After 100 consecutive evaluations of the DCCBGA, it was observed that 44% of the solutions found by the IEEE 33-bus system were better than those found by the BONMIN solver in the General Algebraic Modeling System (GAMS optimization package). In the case of the IEEE 69-bus system, the DCCBGA ensured, with 55% probability, that solutions with better objective function values than the mean solution value of the GAMS were found. Power generation curves for the slack source confirmed that the optimal siting and sizing of PV sources create the duck curve for the power required to the main grid; in addition, the voltage profile curves for both systems show that voltage regulation was always maintained between ±10% in all the time periods under analysis. All the numerical validations were carried out in the MATLAB programming environment with the GAMS optimization package.
COVID-19 Experience Transforming the Protective Environment of Office Buildings and Spaces
Authors: Phapant Panupant , Dutta Abhishek , Chavalparit Orathai
Published: December 2021

The COVID-19 pandemic has affected human life in every possible way and, alongside this, the need has been felt that office buildings and workplaces must have protective and preventive layers against COVID-19 transmission so that a smooth transition from ‘work from home’ to ‘work from office’ is possible. However, a comprehensive understanding of how the protective environment can be built around office buildings and workspaces, based on the year-long experience of living with COVID-19, is largely absent. The present study reviews international agency regulation, country regulation, updated journal articles, etc., to critically understand lessons learned from the COVID-19 pandemic and evaluate the expected changes in sustainability requirements of office buildings and workplaces. The built environment, control environment, and regulatory environment around office buildings and workplaces have been put under test on safety grounds during the pandemic. Workers switched over to safely work from home. Our findings bring out the changes required to be affected in the three broad environmental dimensions to limit their vulnerability status experienced during the pandemic. Office building designs should be fundamentally oriented to provide certain safety protective measures to the workers, such as touch-free technologies, open working layouts, and workplace flexibilities to diminish the probability of getting infected. Engineering and administrative control mechanisms should work in a complementary way to eliminate the risk of disease spread. Country regulation, agency regulations, and operational guidelines need to bring behavioral changes required to protect workers from the COVID-19 pandemic.
Evaluating the Impact of COVID-19 on Society, Environment, Economy, and Education
Authors: Kumar Vijay , Alshazly Hammam , Idris Sahar Ahmed , Bourouis Sami
Published: December 2021

The COVID-19 pandemic has caused drastic changes across the globe, affecting all areas of life. This paper provides a comprehensive study on the influence of COVID-19 in various fields such as the economy, education, society, the environment, and globalization. In this study, both the positive and negative consequences of the COVID-19 pandemic on education are studied. Modern technologies are combined with conventional teaching to improve the communication between instructors and learners. COVID-19 also greatly affected people with disabilities and those who are older, with these persons experiencing more complications in their normal routine activities. Additionally, COVID-19 provided negative impacts on world economies, greatly affecting the business, agriculture, entertainment, tourism, and service sectors. The impact of COVID-19 on these sectors is also investigated in this study, and this study provides some meaningful insights and suggestions for revitalizing the tourism sector. The association between globalization and travel restrictions is studied. In addition to economic and human health concerns, the influence of a lockdown on environmental health is also investigated. During periods of lockdown, the amount of pollutants in the air, soil, and water was significantly reduced. This study motivates researchers to investigate the positive and negative consequences of the COVID-19 pandemic in various unexplored areas.
Data-Driven Models for Estimating Dust Loading Levels of ERV HEPA Filters
Authors: Park Seung-Hoon , Jo Jae-Hun , Kim Eui-Jong
Published: December 2021

With increasing global concerns regarding indoor air quality (IAQ) and air pollution, concerns about regularly replacing ventilation devices, particularly high-efficiency particulate air (HEPA) filters, have increased. However, users cannot easily determine when to replace filters. This paper proposes models to estimate the dust loading levels of HEPA filters for an energy-recovery ventilation system that performs air purification. The models utilize filter pressure drops, the revolutions per minute (RPM) of supply fans, and rated airflow modes as variables for regression equations. The obtained results demonstrated that the filter dust loading level could be estimated once the filter pressure drops and RPM, and voltage for the rated airflow were input in the models, with a root mean square error of 5.1–12.9%. Despite current methods using fewer experimental datasets than the proposed models, our findings indicate that these models could be efficiently used in the development of filter replacement alarms to help users decide when to replace their filters.
Pessimistic Tone in Earnings Announcement and CSR Disclosure: Exploring the Interacting Role of CEO Busyness
Authors: Ningsih Sri , Harymawan Iman , Fitriani Nurul , Lam Brian
Published: December 2021

This study analyzes the relationship between the pessimistic tone in earnings announcements and CSR disclosures interacted by CEO busyness. This study used 191 observations from 74 firms listed on the Indonesia Stock Exchange and the Global Reporting Initiative (GRI) database from 2016–2019. Grounded in signaling theory, we hypothesize that a pessimistic tone in earning announcements will increase CSR disclosure. We also hypothesize that busy CEOs strengthen this relationship. We use the ordinary least squares to analyze and answer our hypotheses. This study showed that the use of a pessimistic tone in the income statement in the discussion report and management analysis (MD&A) is related positively and significantly to CSR disclosure. This study also found that busy CEOs strengthen those relationships. Our implication is that CSR disclosure in our sample is only measured based on the information presented in the sustainability report. To the best of the authors’ knowledge, this study is the first to investigate the relationship between the pessimistic tone in earning announcements and CSR disclosures interacted by CEO busyness. In addition, this study provides insight into current performance disclosure practices in MD&A reports and CSR reports that managers can use to safeguard the firm’s reputation.
Research on Design of Stalk Furniture Based on the Concept and Application of Miryoku Engineering Theory
Authors: Li Hong , Wen Kuo-Hsun
Published: December 2021

Through discussion on the disposal of crop stalks in rural China, where stalks are usually burned in the open air, resulting in the production of a large amount of smoke and air pollution, this study has been conducted to discuss the reasonable and effective use of waste stalks, and to explain the excellent characteristics of stalks from the perspective of green, ecological sustainable design and user perceptual needs, as well as the application of stalks in furniture design. In view of the accuracy of stalks in the design of furniture products, the paper has adopted the evaluation grid method in Miryoku engineering to guide the design practice. In the research process, interviews were first conducted on the basis of sample selection, then based on the expert opinion survey method, eight testers were selected for in-depth interviews. Key words of charm factors were extracted from 190 picture samples, and then a personal evaluation structure map and statistical data were built to construct an overall evaluation structure of the rural style stalk furniture. Next, the score value of the charm factor of stalk furniture was analyzed through the Likert scale questionnaire, the charm factor and specific performance characteristics of the rural style were sorted, followed by a proposed design method. Finally, the effectiveness of the design strategy was verified with the design of stalk furniture in rural style. The study emerged with the feasibility of the design of furniture products made of stalks. The results of the study have shown the correlation between the elements of furniture products and the perceptual image of users. Furthermore, the results have demonstrated the reuse value of waste stalks and improved the innovative design of stalks in the home furnishing field, which conveys the concept of sustainable development.
Magnetoelastic Materials for Monitoring and Controlling Cells and Tissues
Authors: Meyers Kaylee Marie , Ong Keat Ghee
Published: December 2021

Advances in cell and tissue therapies are slow to be implemented in the clinic due to the limited standardization of safety and quality control techniques. Current approaches for monitoring cell and tissue manufacturing processes are time and labor intensive, costly, and lack commercial scalability. One method to improving in vitro manufacturing processes includes utilizing the coupled magnetic and mechanical properties of magnetoelastic (ME) materials as passive and wireless sensors and actuators. Specifically, ME materials can be used in quantifying cell adhesion, detecting contamination, measuring biomarkers, providing biomechanical stimulus, and enabling cell detachment in bioreactors. This review outlines critical design considerations for ME systems and summarizes recent developments in utilizing ME materials for sensing and actuation in cell and tissue engineering.
Enhanced Optimization of Bioethanol Production from Palm Waste Using the Taguchi Method
Authors: Ragab Tamer I. M. , Alminderej Fahad M. , El-Sayed Wael A. , Saleh Sayed M. , Shalaby Al Shimaa Gamal
Published: December 2021

In the present study, palm fiber (PF) and palm fronds (PFN) were selected as local agricultural wastes for the extraction of different biopolymers (cellulose, hemicelluloses, and lignin) by alkaline sodium hydroxide (PF, 2.37% NaOH at 86.5 °C for 1.6 h; PFN, 6% NaOH at 90 °C for 1 h) and bioethanol production. The processes of extraction were optimized by the experimental design method of Taguchi. The total carbohydrates of PF and PFN obtained were 24.4% and 31.0%, respectively. In addition, the untreated palm fiber (UPF), untreated palm frond (UPFN), cellulose palm fibers (CPF), and cellulose palm fronds (CPFN) were subjected to enzymatic hydrolysis processes using crude enzymes and commercial enzymes at 48 °C and pH 5.5. The results indicate that the maximum reducing sugars used were CPF 229.90, CPFN 243.69, UPF 120.19, and UPFN 100.00 (mg/g), which were obtained at a crude enzyme loading. CPF and CPFN hydrolysates were then successfully converted into bioethanol by a separate enzymatic hydrolysis and fermentation by Saccharomyces cerevisiae. Anaerobic cultivation of the hydrolysates with S.cerevisiae resulted in 0.222 g/g and 0.213 g/g bioethanol in the case of CPF and CPFN, respectively. Optimization processes could be an innovative approach to the sustainable development of bioethanol production.