bluuebunny/crossref_metadata_2025 huggingface dataset:
A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data
Authors: Dong Zhaoqing , Shi Lijian , Lin Mingsen , Zeng Tao
Published: February 2022

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.
Multitemporal Change Detection Analysis in an Urbanized Environment Based upon Sentinel-1 Data
Authors: Gruenhagen Lars , Juergens Carsten
Published: February 2022

The German Ruhr area is a highly condensed urban area that experienced a tremendous structural change over recent decades with the replacement of the coal and steel industries by other sectors. Consequently, a lot of major land cover changes happened. To retrospectively quantify such land cover changes, this study analysed synthetic aperture radar images of the Sentinel-1 satellites by applying the Google Earth Engine. Three satellite images are analysed by the multitemporal difference-adjusted dispersion threshold approach to capture land cover changes such as demolished buildings and new buildings by applying a threshold. This approach uses synthetic aperture radar data that are rarely considered in previously existing land cover change services. Urbanization or urban sprawl leads to changes in the urban form globally. These can be caused, for example, by migration or regionally by structural change, etc., such as in the study area presented here. The results are validated with reference data sets, which are publicly available nationally (e.g., house contour lines, normalized digital terrain model, digital orthophotos) or which are publicly available globally like the Global Urban Footprint and the World Settlement Footprint. Based on this, land cover changes could be identified for 21 locations within the study area of the city of Bochum.
Application of Machine Learning for Simulation of Air Temperature at Dome A
Authors: Pang Xiaoping , Liu Chuang , Zhao Xi , He Bin , Fan Pei , Liu Yue , Qu Meng , Ding Minghu
Published: February 2022

Dome A is the summit of the Antarctic plateau, where the Chinese Kunlun inland station is located. Due to its unique location and high altitude, Dome A provides an important observatory site in analyzing global climate change. However, before the arrival of the Chinese Antarctic expedition in 2005, near-surface air temperatures had not been recorded in the region. In this study, we used meteorological parameters, such as ice surface temperature, radiation, wind speed, and cloud type, to build a reliable model for air temperature estimation. Three models (linear regression, random forest, and deep neural network) were developed based on various input datasets: seasonal factors, skin temperature, shortwave radiation, cloud type, longwave radiation from AVHRR-X products, and wind speed from MERRA-2 reanalysis data. In situ air temperatures from 2010 to 2015 were used for training, while 2005–2009 and 2016–2020 measurements were used for model validation. The results showed that random forest and deep neural network outperformed the linear regression model. In both methods, the 2005–2009 estimates (average bias = 0.86 °C and 1 °C) were more accurate than the 2016–2020 values (average bias = 1.04 °C and 1.26 °C). We conclude that the air temperature at Dome A can be accurately estimated (with an average bias less than 1.3 °C and RMSE around 3 °C) from meteorological parameters using random forest or a deep neural network.
A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin
Authors: Parcerisas Adrià , Contreras Ivan , Delecourt Alexia , Bertachi Arthur , Beneyto Aleix , Conget Ignacio , Viñals Clara , Giménez Marga , Vehi Josep
Published: February 2022

Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives.
An Optical Fiber Sensor for Axial Strain, Curvature, and Temperature Measurement Based on Single-Core Six-Hole Optical Fiber
Authors: Li Yujian , Yu Changyuan , Lu Ping
Published: February 2022

In this paper, the field distribution and effective refractive index of transmission modes in single-core six-hole optical fiber were researched by modeling and simulation experiments. Based on the simulation results, a new type of sensor for axial strain, curvature, and temperature applications measurement was designed and fabricated. The experimental results showed that the axial strain sensitivities at different dips were −0.97 pm/με and −1.05 pm/με in the range from 0 to 2000 με, and the temperature sensitivities were 35.17 pm/°C and 47.27 pm/°C in the range from 25 to 75 °C. In addition, the proposed sensor also detected the curvature change with sensitivities of 7.36 dB/m−1 and 20.08 dB/m−1 from −2.582 m−1 to −1.826 m−1, respectively. Finally, through theoretical analysis, it can be deduced that this has potential application in the field of simultaneous measurement of strain and temperature.
Improving Depth Estimation by Embedding Semantic Segmentation: A Hybrid CNN Model
Authors: Valdez-Rodríguez José E. , Calvo Hiram , Felipe-Riverón Edgardo , Moreno-Armendáriz Marco A.
Published: February 2022

Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a 3D Convolutional Neural Network (CNN)—segmentation is coded as one-hot planes representing categories of objects. We explore 2D and 3D models. Particularly, we propose a hybrid 2D–3D CNN architecture capable of obtaining semantic segmentation and depth estimation at the same time. We tested our procedure on the SYNTHIA-AL dataset and obtained σ3=0.95, which is an improvement of 0.14 points (compared with the state of the art of σ3=0.81) by using manual segmentation, and σ3=0.89 using automatic semantic segmentation, proving that depth estimation is improved when the shape and position of objects in a scene are known.
Asynchronous Federated Learning System Based on Permissioned Blockchains
Authors: Wang Rong , Tsai Wei-Tek
Published: February 2022

The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault tolerance. In this paper, we propose an asynchronous federated learning system based on permissioned blockchains, using permissioned blockchains as the federated learning server, which is composed of a main-blockchain and multiple sub-blockchains, with each sub-blockchain responsible for partial model parameter updates and the main-blockchain responsible for global model parameter updates. Based on this architecture, a federated learning asynchronous aggregation protocol based on permissioned blockchain is proposed that can effectively alleviate the synchronous federated learning algorithm by integrating the learned model into the blockchain and performing two-order aggregation calculations. Therefore, the overhead of synchronization problems and the reliability of shared data is also guaranteed. We conducted some simulation experiments and the experimental results showed that the proposed architecture could maintain good training performances when dealing with a small number of malicious nodes and differentiated data quality, which has good fault tolerance, and can be applied to edge computing scenarios.
The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds
Authors: Huang Chao , Zhang Fuping , Xu Zhengyi , Wei Jianming
Published: February 2022

Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms.
Water Cloud Detection with Circular Polarization Lidar: A Semianalytic Monte Carlo Simulation Approach
Authors: Ahmad Wiqas , Zhang Kai , Tong Yicheng , Xiao Da , Wu Lingyun , Liu Dong
Published: February 2022

This work presents polarization property studies of water clouds using a circular polarization lidar through a simulation approach. The simulation approach is based on a polarized, semianalytic Monte Carlo method under multiple-scattering conditions and considers three types of water clouds (namely homogeneous, inhomogeneous and partially inhomogeneous). The simulation results indicate that the layer-integrated circular depolarization ratios show similar variation trends as those of layer-integrated linear depolarization ratios. The Mishchenko–Hovenier relationship is validated to correlate the simulated layer-integrated circular and linear depolarization ratios. In addition, the cloud droplet effective radius, extinction coefficient, lidar field-of-view (FOV) and height of the cloud bottom are all found to affect the layer-integrated depolarization ratio. The current work theoretically indicates that a circular polarization lidar can efficiently perform measurements of water clouds, enjoying the advantage of higher sensitivity compared to a traditional linear polarization lidar. Hence, it should be of interest to researchers in fields of polarization lidar applications.
Student’s t-Kernel-Based Maximum Correntropy Kalman Filter
Authors: Huang Hongliang , Zhang Hai
Published: February 2022

The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is sensitive to large outliers. In many areas of engineering, the noise may be non-Gaussian and outliers may arise naturally. Therefore, the performance of the Kalman filter may deteriorate significantly in non-Gaussian noise environments. To improve the accuracy of the state estimation in this case, a novel filter named Student’s t kernel-based maximum correntropy Kalman filter is proposed in this paper. In addition, considering that the fixed-point iteration method is used to solve the optimal estimated state in the filtering algorithm, the convergence of the algorithm is also analyzed. Finally, comparative simulations are conducted and the results demonstrate that with the proper parameters of the kernel function, the proposed filter outperforms the other conventional filters, such as the Kalman filter, Huber-based filter, and maximum correntropy Kalman filter.
Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning
Authors: Nolazco-Flores Juan Arturo , Faundez-Zanuy Marcos , Velázquez-Flores Oliver Alejandro , Del-Valle-Soto Carolina , Cordasco Gennaro , Esposito Anna
Published: February 2022

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.
A Machine Learning Approach for an Improved Inertial Navigation System Solution
Authors: Mahdi Ahmed E. , Azouz Ahmed , Abdalla Ahmed E. , Abosekeen Ashraf
Published: February 2022

The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.
Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies
Authors: Manivasagam Karnica , Yang Liyun
Published: February 2022

Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against an electrogoniometer for measuring wrist flexion velocity. Twelve participants performed standard wrist movements and simulated work tasks while equipped with both systems. Two computational algorithms for the IMU-based system, i.e., IMUnorm and IMUflex, were used. For wrist flexion/extension, the mean absolute errors (MAEs) of median wrist flexion velocity compared to the goniometer were <10.1°/s for IMUnorm and <4.1°/s for IMUflex. During wrist deviation and pronation/supination, all methods showed errors, where the IMUnorm method had the largest overestimations. For simulated work tasks, the IMUflex method had small bias and better accuracy than the IMUnorm method compared to the goniometer, with the MAEs of median wrist flexion velocity <5.8°/s. The results suggest that the IMU-based method can be considered as a convenient method to assess wrist motion for occupational studies or ergonomic evaluations for the design of workstations and tools by both researchers and practitioners, and the IMUflex method is preferred. Future studies need to examine algorithms to further improve the accuracy of the IMU-based method in tasks of larger variations, as well as easy calibration procedures.
Subsidy Reform and the Transformation of Social Contracts: The Cases of Egypt, Iran and Morocco
Authors: Vidican Auktor Georgeta , Loewe Markus
Published: February 2022

After independence, subsidies have been a cornerstone of the social contracts in the Middle East and North Africa. Governments spent heavily to reduce poverty and strengthen their legitimacy. Yet, subsidies became financially unsustainable and donors pressed for reforms. This article assesses reform processes in Morocco, Egypt and Iran between 2010 and 2017, thus before sanctions against Iran were further tightened and before the COVID-19 pandemic. We show that even though the three countries had similar approaches to subsidisation, they have used distinct strategies to reduce subsidies and minimise social unrest—with the effect that their respective social contracts developed differently. Morocco tried to preserve its social contract as much as possible; it removed most subsidies, explained the need for reform, engaged in societal dialogue and implemented some compensatory measures, preserving most of its prevailing social contract. Egypt, in contrast, dismantled subsidy schemes more radically, without systematic information and consultation campaigns and offered limited compensation. By using repression and a narrative of collective security, the government transformed the social contract from a provision to a protection pact. Iran replaced subsidies with a more cost-efficient and egalitarian quasi-universal cash transfer scheme, paving the way to a more inclusive social contract. We conclude that the approach that governments used to reform subsidies transformed social contracts in fundamentally different ways and we hypothesize on the degree of intentionality of these differences.
Addressing Climate Change Resilience in Pavements: Major Vulnerability Issues and Adaptation Measures
Authors: Saleh Mohamed , Hashemian Leila
Published: February 2022

Climate change is the one of the greatest challenges of our time, and it poses a threat to the surrounding built and natural environments. This review paper addresses climate change resilience in pavements by considering major vulnerability issues and adaptation measures. First, a review on foundational information of climate change related to transportation infrastructure is provided to bring all transportation professionals and practitioners to the same knowledge base on climate change terminology. Such information includes sources of climate information, climate scenarios, downscaling climate data, and uncertainty in climate projection information. Relevant climate stressors to pavements are discussed in some depth, including the most significant ones, which are increases in temperature and precipitation intensity. Thus, the proposed different engineering-informed adaptation measures relevant to the climate stressors of interest were evidence-based with reference to published peer-reviewed articles and case studies. Such adaptation solutions are related to monitoring pavement key performance parameters and pavement adaptations in structural design, robust materials and mix design, along with adaptation in maintenance, regulation, and construction. Efforts to adapt pavement systems to climate change are ongoing. In addition to such research works, this study concludes that impacts of adaptation measures on pavement and environment should be incorporated in the decision-making process in planning and design. This makes it important to integrate practical adaptation strategies in design and construction standards and guides, and implement awareness and education of climate change adaptation among engineers and practitioners.
Estimating China’s Population over 21st Century: Spatially Explicit Scenarios Consistent with the Shared Socioeconomic Pathways (SSPs)
Authors: Chen Jie , Liu Yujie , Zhang Ermei , Pan Tao , Liu Yanhua
Published: February 2022

Accurate and reliable subnational and spatially explicit population projections under shared socioeconomic pathways (SSPs) for China will be helpful for understanding long-term demographic changes and formulating targeted mitigation and adaptation policies under climate change. In this study, national and provincial populations for China by age, sex, and education level to 2100 under five SSPs were estimated using the population-development-environment model. These parameters include fertility, mortality, migration, and education and consider the most recent birth policy in China. To quantify these projections spatially, the gridded population was provided at 1 km × 1 km by spatial downscaling. Results show the national population is highest under SSP3, with 1.71 × 109 people in 2100. Guangdong, Henan, and Shandong are the most populous in SSP1, 2, 4, 5, while Guangxi is the most populous province in SSP3, reaching 1.54 × 108 people. The differences in education level among scenarios are obvious, especially in 2100 where education level for SSP1 and SSP5 is the highest. The spatial distribution of population varies across the country, with the majority of the population concentrated in southern and eastern China, especially in the coastal regions. Our results under different SSPs could provide a reference to project disaster risks, formulate relevant policies and guide sustainable development from a long-term perspective.
Models Underlying the Success Development of Family Farms in Romania
Authors: Micu Marius Mihai , Dumitru Eduard Alexandru , Vintu Catalin Razvan , Tudor Valentina Constanta , Fintineru Gina
Published: February 2022

A family farm is an important part of the countryside, both economically and especially socially. In recent years, legislative measures have encouraged the development of large farms, ensuring food security while neglecting family farms, which are characterised by a lack of organisation, the absence of proper classification and the absence of measures to encourage their development and protection. In order to identify the premises underlying the development of family farms, and to create a development model for those family farms in a poor economic situation, the BMC method and SWOT analysis of seven family farms considered successful models were used. The development of these types of farms could provide a solution to a major problem facing the Romanian countryside: the depopulation of rural areas. The results of the study show that the development of these types of farms is based on joining an associative form (cooperatives) and/or making investments in the processing/storage line of agricultural products.
The “A2UFood Training Kit”: Participatory Workshops to Minimize Food Loss and Waste
Authors: Ioannou Thanos , Bazigou Katerina , Katsigianni Afroditi , Fotiadis Michalis , Chroni Christina , Manios Thrassyvoulos , Daliakopoulos Ioannis , Tsompanidis Christos , Michalodimitraki Eleni , Lasaridi Katia
Published: February 2022

The A2UFood project aspires to design and implement a holistic scheme for the management of food loss and waste, covering all aspects of the ‘reduce–reuse–recycle’ philosophy. An integral part of an efficient strategy to combat food wastage is raising awareness and informing the public. For this reason, among the designed direct, in person, communication activities of the A2UFood project, the ‘A2Food training kit’ has a key role. The kit includes a short theoretical background and nine sets of participatory activities, and it is the first of its kind implemented in Greece. After their evaluation through workshops, all proposed activities were included in an e-book for further use. Activities are based on the Education for Sustainable Development framework and the Sustainable Development Goals. The dissemination model employed draws on adult education theory, in the form of participatory workshops and also follows the ‘train the trainers’ principle. All the workshops are based on the principles of active learning, related to real life experience and cooperative learning. Following these principles, the activities designed for the workshops aimed to bring participants’ pre-existing experience, values, and beliefs into confrontation with a new context. Responding to COVID-19 pandemic limitations, necessary adjustments to distant training requirements were also implemented. Through the training kit, we have trained 270 trainers and, by the end of June 2021, 19 of them had implemented selected workshops for about 600 students, all over Greece. In conclusion, the multiplicative power of the kit is considered satisfactory under the given pandemic-induced social-distancing conditions, and it will have a lasting footprint alongside the informative campaign, since it will be available for use in the future, either as a tool for the training of trainers, or as material to be used by the trainers.
Analysis of Crisis Management for Sustainable Development of Fitness Center during the COVID-19 Pandemic
Authors: Park Tae-Seung , Kwon Jae-Yoon
Published: February 2022

The purpose of this research was to verify the importance and performance of sustainable crisis management in Korean fitness centers using Importance-Performance Analysis (IPA). For this study, 304 fitness center executives and managers in Seoul and Gyeonggi region were selected from 21 March to 17 May 2020. Frequency analysis was performed using SPSS 24.0, and exploratory factor analysis was conducted to verify the validity and reliability. Priority analysis and IPA analysis were performed to compare the mean values, and the following results were obtained. In the first quadrant, there were six attributes besides keeping social distancing between employees and customers. In the second quadrant, there were four attributes in addition to regular disinfection of the gymnasium. In the third quadrant, there were six attributes besides maintain clean furniture. Third, there were six attributes other than maintaining clean furniture in the third quadrant. Lastly, in the fourth quadrant, there were three attributes in addition to the restriction of face-to-face meetings. The conclusion was as follows. First, equip supplies for the prevention of COVID-19, keep social distance, and check government support policies. Second, analyze economic support policies, and research their application methods. Third, prepare various non-face-to-face communication methods and Untact (noncontact) marketing strategies. Fourth, make a checklist for factors with relatively little importance.
Developing a Behavior Change Framework for Pandemic Prevention and Control in Public Spaces in China
Authors: Liu Jing , Kamarudin Khairul Manami , Liu Yuqi , Zou Jinzhi , Zhang Jiaqi
Published: February 2022

Preventive behavior, such as hand hygiene, facemask wearing, and social distancing, plays a vital role in containing the spread of viruses during pandemics. However, people in many parts of the world usually encounter difficulties adhering to this behavior due to various causes. Thus, this article aims to develop a research framework and propose design strategies to prompt individuals’ behavior change during pandemics. Initially, we integrated a literature review and a structured interview (n = 22) to ascertain the core factors impacting behavior change during pandemics. These factors were categorized into four aspects: perceptional factors (attitudes, subjective norms and perceived behavior control, risk perceptions); social factors (knowledge and information dissemination, governmental regulations); physical factors (tools and facilities and surveillance); and sociocultural factor (cultural contexts). Then, a theoretical framework with antecedents was developed to reveal behavior intention and formation process. After that, an empirical study was carried out to test the research framework through a questionnaire survey (n = 549). The research findings indicated that all derived factors could directly or indirectly affect individuals’ preventive behavior during pandemics. This article strives to provide valuable insights for different stakeholders when coping with pandemic situations.
Global Challenges and Responses: Agriculture, Economic Globalization, and Environmental Sustainability in Central Asia
Authors: Batmunkh Altanshagai , Nugroho Agus Dwi , Fekete-Farkas Maria , Lakner Zoltan
Published: February 2022

Economic globalization (EG) accelerates very fast in Central Asia. This could cause environmental degradation, according to the environmental Kuznets curve (EKC) hypothesis. The study aims to determine how the EG of agriculture impacts environmental sustainability, and to test the EKC hypothesis on the agricultural sector in six Central Asian countries. Particularly, some main hypotheses were proposed using secondary data from Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan from 1994 to 2019. This study uses five explanatory variables: agricultural exports value (EXP), agriculture forestry and fishing value-added (AVA), the exchange rate (EXR), total natural resource rents (RENT), and external debt stocks (DEBT), while the dependent variable in this study is the CO2 emissions from on-farm energy use (EMS), temperature changes (TEMP), and forest fires (FIRE). These data are analyzed using panel data regression. As a result, AVA and RENT raise EMS; EXC raises TEMP but lowers EMS; DEBT raises TEMP but can lower FIRE. Hence, we propose recommendations to improve this condition, including a clear roadmap, enhanced partnerships, and regional and international support.
In Vitro Corrosion Resistance of a Layer-by-Layer Engineered Hybrid Coating on ZK60 Magnesium Alloy
Authors: Rahman Mostafizur , Balu Rajkamal , Dutta Naba Kumar , Roy Choudhury Namita
Published: February 2022

Magnesium alloys are next generation biodegradable implants for clinical applications. However, their medical applications are currently hampered by their rapid corrosion rate in the physiological environment. To overcome such limitations, we have applied a novel layer-by-layer engineering approach of introducing anodization-induced microrough oxidized surface on ZK60 magnesium alloy, followed by surface mineralization with natural calcium apatite (hydroxyapatite, HA), and surface coating with natural protein (silk fibroin, SF); which, effectively reduces corrosion and degradation rate of ZK60 in simulated body fluid. Anodization of ZK60 improved the surface adhesion strength of HA layer; HA layer increased the surface roughness, hydrophilicity and micro-hardness, whereas decreased ionic release; SF layer decreased surface microroughness and hydrophilicity, whereas improved the stability of HA layer. The SF + HA coating on anodized ZK60 effectively decreased the in vitro weight loss (degradation) by almost six times, whereas corrosion rate by more than two orders in magnitude. Such interfacial coatings, with biocompatible SF on the outer surface, could potentially expand the application of ZK60 in the field of biomedical engineering.
Military Food Supply Chain during the COVID-19 Pandemic
Authors: Wincewicz-Bosy Marta , Sadowski Adam , Wąsowska Katarzyna , Galar Zbigniew , Dymyt Małgorzata
Published: February 2022

The outbreak of the COVID-19 pandemic in Wuhan, China, disrupted international trade and changed the global food supply chain. Disruptions affected all food supply chains, including those critical to national security. To the best of our knowledge, this study is the first to reveal the impact of the pandemic on the military food supply chain in Poland. In the analysed single case study, we used process mapping to show the diversity of the types of military food supply chains operating in military units. The attention was focused on the internal and external food supply chains in the context of the disruptions and dysfunctions that appeared in the sphere of management. The contribution of the article is twofold. First, we used a situational crisis management approach to isolate four types of internal military food supply chains. Practical guidance concerns showing actions influencing the modification of system solutions in the military sphere in peacetime in connection with the pandemic. As a member of the North Atlantic Treaty Organization (NATO), Poland applies system solutions similar to those in place in other allied countries. The results of our research can be used as recommendations for solving problems in the military food supply chain in other countries within NATO.
Impacts of the COVID-19 Pandemic on Rural Residents of Japan and Their Interactions with the Outside World
Authors: Nakamura Momoka , Hattori Toshihiro
Published: February 2022

In this study, we clarified the impact of the pandemic on the daily lives of rural Japanese residents who experienced the pandemic and on their attitudes toward relationships with people in the broader world. From July to August 2021, an anonymous questionnaire survey was conducted in three rural and fishing community districts (Oshio, Sugane, and Ozushima) in Shunan City, Yamaguchi Prefecture, Japan, among the residents of each district. We found that the survey respondents had decreased their frequency of engaging in activities that were not essential to daily life. During the pandemic, people consciously avoided visitors from outside their districts, but after the pandemic restrictions were lifted, many people welcomed visitors the same way they had before the pandemic; indeed, people welcomed migrants who would increase their populations. As long as COVID-19 infections are under control, residents of the three districts have one thing in common: they want to interact with people outside their districts. It is necessary to consider how to continue community activities under the pandemic so that rural Japanese citizens can maintain interactions with the outside world after the pandemic.
Working through Disaster Risk Management to Support Regional Food Resilience: A Case Study in North-Eastern Australia
Authors: Reis Kimberley , Desha Cheryl , Campbell Sioux , Liddy Prudence
Published: February 2022

As a consequence of disasters such as pandemics and severe weather events, Australian communities often face ‘food shortages’ resulting from the reduced availability of food and reduced access to available food. These food shortages can be acutely felt by vulnerable populations, comprising people in communities who are already dealing with social or economic disadvantages. Despite growing calls to ensure food access for everyone during and following disasters, efforts are still largely ad hoc, champion based and highly variable in their reliability and quality of supply. There is also a disconnect between disaster-related food relief and improving business continuity towards local economic resilience. This study sought to tackle these challenges by exploring how ‘local food access’ could be integrated within disaster management mechanisms, to support the most vulnerable and also contribute to local economic resilience. This paper discusses the findings of a study undertaken in Cairns, a north-eastern Australian regional authority, and involves a review of disaster management planning and policy artifacts, an online survey, and semi-structured interviews with stakeholders providing care and advocacy for vulnerable populations. Synthesizing the findings, we present a disaster-management-focused ‘Local Food Access Model’ that connects shorter food supply chains to improved disaster response, resilience and contingency-planning agendas. Applying this model to Cairns, we conclude the multiple benefits and immediate stakeholder readiness for a virtual (online) food resilience and contingency hub, to enable the connection of local food availability and access information within existing disaster management processes. The research method used, the model, and the case-specific findings provide government decision makers with a useful process, a local food-access schematic and a case study example to support immediate improvements in disaster resilience for vulnerable populations.
Shared Micromobility: Between Physical and Digital Reality
Authors: Bylieva Daria , Lobatyuk Victoria , Shestakova Irina
Published: February 2022

Moving around the city is a problem for the development of most megacities. Due to digital technologies, each city dweller is connected by information and communication channels with the city infrastructure, receiving information and choosing the available modes of movement. Shared micromobility in terms of digital solutions is a convenient service, while reducing congestion and emissions, and preventing air and noise pollution; however, the physical and social dimension of the city is experiencing problems, with growing public health concerns, high overall environmental costs, clutter in the streets, etc. This presentation presents a case study of the relatively recent emergence of shared micromobility in St. Petersburg and attitudes towards its users. In addition to the direct process of use and the experience gained, the factors that determine the social influence and perception of micromobility are highlighted. The highest ratings of the digital component and the rather high importance of such factors as environmental friendliness and safety make it possible to recommend the creation of an interactive digital system that unites riders.
A Structured Review of Emotional Barriers to WASH Provision for Schoolgirls Post-Disaster
Authors: Garfias Royo Margarita , Parrott Elinor , Pacheco Emily-Marie , Ahmed Imaduddin , Meilianda Ella , Kumala Intan , Oktari Rina Suryani , Joffe Helene , Parikh Priti
Published: February 2022

Pubescent girls face unique emotional barriers to returning to school after a disaster concerning water, sanitation and hygiene (WASH). This paper explores themes of WASH, gender violence, the lack of dignity and sense of shame arising from inadequate WASH facilities for girls in disaster settings. We conducted a structured literature review of 126 sources to investigate the emotional constraints facing pubescent girls concerning WASH in schools in Indonesia, a region prone to frequent disasters. Findings are synthesised into four major themes: psychological experiences of WASH, challenges faced by girls in schools, barriers to inclusive WASH provision and how to create a holistic approach to WASH. Key conclusions include the need for interdisciplinary research, cross sectoral collaboration, more evidence and research in Indonesia, especially regarding menstrual hygiene management, improved toilet design to reduce the physical barriers linked to emotional barriers and inclusive design for those with disabilities.
The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty
Authors: Meidute-Kavaliauskiene Ieva , Yıldırım Figen , Ghorbani Shahryar , Činčikaitė Renata
Published: February 2022

The value of superior supply network design is becoming increasingly important, especially in the perishable supply chain. Due to the recent developments in perishable products, perishable product supply chain (PPSC) management has attracted many researchers. The purpose of this study was to present a multi-period and multi-echelon perishable supply chain with regards to procurement time, cycle cost, and customer satisfaction. This study presented a new form of location-routing in a supply chain network for perishable products, accounting for environmental considerations, cost, procurement time, and customer satisfaction, such that the total costs, delivery time, and the emission of pollutants in the network were minimized while customer satisfaction was maximized. We formulated the problem as a multi-objective, nonlinear, mixed-integer program and the hybrid approach was proposed to solve the model. The mean error of the proposed algorithm for the objective function compared to the exact method in solving the sample problems was less than 3.4%. The computational results revealed the efficiency of the proposed algorithm for a wide range of issues of various sizes.
Assessing How Big Insurance Firms Report and Manage Carbon Emissions: A Case Study of Allianz
Authors: Dawson Chloe , Dargusch Paul , Hill Genia
Published: February 2022

Carbon management is an important topic for investigation to ensure the accountability of firms in meeting Paris Agreement targets. Transparency and rigorous scrutiny are needed to keep industries on track to accomplish a reduction in greenhouse gas emissions. To maintain a healthy environment, and promote human and ecosystem health, it will be vital to limit global warming to below 2 °C. Allianz presents a good example of carbon management as they are a leading insurance firm that utilises the Global Reporting Initiative (GRI) standards to report their greenhouse gas emissions. Allianz has promoted important initiatives such as the Net-Zero Climate Alliance and made an array of pledges that promote net-zero business operations by 2050. In 2020, Allianz reported greenhouse gas emissions equivalent to 384,178 tCO2, a 31% reduction in their emissions compared to 2019 figures. Procuring carbon credits is the main mechanism that Allianz has used to reduce their reportable emissions, as well as making investments into renewable energies—wind and solar. This study is limited by the information provided by Allianz and the accuracy in which they have reported their greenhouse gas emissions and emissions reductions. In the last reporting year, Allianz produced the greatest carbon emissions in the EU/USA insurance sector, producing 189,061 tCO2e more than their closest competitor. To achieve net-zero emissions, Allianz will need to increase their investment into carbon offsets and transition to 100% renewable energy use, while concurrently reducing their investment into coal and gas mining industries. This research gives an insight into the greenhouse gas emissions being produced by insurance/investment firms while also detailing the emissions reduction methods that are being employed. This study synthesises scientific literature with business reports to present a detailed account of industry carbon emissions, emissions reductions, and overall progress towards meeting net-zero pledges, in line with Paris Agreement targets. The recommendations made in this study are based on the information provided by Allianz and are designed to be within the scope of what would be possible for this firm. The aim of this study was to determine the actions and issues in the process of carbon management with a specific focus on Allianz. Key objectives of this research are: 1. To determine the net-zero pledges made by Allianz; 2. To determine the carbon emissions and emissions reductions made by Allianz compared to other firms in the sector; and 3. To determine how these emissions reductions have been achieved.