bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Climate Change and Human Activities on Vegetation Development in Northeast China
Authors: Xue Lin , Kappas Martin , Wyss Daniel , Wang Chaoqun , Putzenlechner Birgitta , Thi Nhung Pham , Chen Jiquan
Published: March 2022

Vegetation in Northeast China (NEC) has faced dual challenges posed by climate change and human activities. However, the factors dominating vegetation development and their contribution remain unclear. In this study, we conducted a comprehensive evaluation of the response of vegetation in different land cover types, climate regions, and time scales to water availability from 1990 to 2018 based on the relationship between normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). The effects of human activities and climate change on vegetation development were quantitatively evaluated using the residual analysis method. We found that the area percentage with positive correlation between NDVI and SPEI increased with time scales. NDVI of grass, sparse vegetation, rain-fed crop, and built-up land as well as sub-humid and semi-arid areas (drylands) correlated positively with SPEI, and the correlations increased with time scales. The negatively correlated area was concentrated in humid areas or areas covered by forests and shrubs. Vegetation water surplus in humid areas weakens with warming, and vegetation water constraints in drylands enhance. Moreover, potential evapotranspiration had an overall negative effect on vegetation, and precipitation was a controlling factor for vegetation development in semi-arid areas. A total of 53% of the total area in NEC showed a trend of improvement, which is mainly attributed to human activities (93%), especially through the implementation of ecological restoration projects in NEC. The relative role of human activities and climate change in vegetation degradation areas were 56% and 44%, respectively. Our findings highlight that the government should more explicitly consider the spatiotemporal heterogeneity of the influence of human activities and water availability on vegetation under changing climate and improve the resilience of regional water resources. The relative proportions and roles map of climate change and human activities in vegetation change areas provide a basis for government to formulate local-based management policies.
Motion Estimation and Hand Gesture Recognition-Based Human–UAV Interaction Approach in Real Time
Authors: Yoo Minjeong , Na Yuseung , Song Hamin , Kim Gamin , Yun Junseong , Kim Sangho , Moon Changjoo , Jo Kichun
Published: March 2022

As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require safe design with high real-time performance. Therefore, in this paper, we propose a hybrid hand gesture system that combines an inertial measurement unit (IMU)-based motion capture system and a vision-based gesture system to increase real-time performance. First, IMU-based commands and vision-based commands are divided according to whether drone operation commands are continuously input. Second, IMU-based control commands are intuitively mapped to allow the UAV to move in the same direction by utilizing estimated orientation sensed by a thumb-mounted micro-IMU, and vision-based control commands are mapped with hand’s appearance through real-time object detection. The proposed system is verified in a simulation environment through efficiency evaluation with dynamic gestures of the existing vision-based system in addition to usability comparison with traditional joystick controller conducted for applicants with no experience in manipulation. As a result, it proves that it is a safer and more intuitive HUI design with a 0.089 ms processing speed and average lap time that takes about 19 s less than the joystick controller. In other words, it shows that it is viable as an alternative to existing HUI.
Methodology for Verifying the Indication Correctness of a Vessel Compass Based on the Spectral Analysis of Heading Errors and Reliability Theory
Authors: Jaskólski Krzysztof
Published: March 2022

This article presents a novel method for validating compass devices. To this end, the post-processing method was used, i.e., the previously recorded vessel’s heading from three compass devices was applied. A spectral analysis of the recorded heading in the frequency domain was conducted by applying the fast Fourier transform method. Based on a synthetic summary of the results of spectral analysis in the heading error frequency domain, the factors causing errors of low-frequency compass device indications, manifested by the vessel’s yawing due to the vessel steering errors on the pre-determined course and the external factors causing total drift of the vessel, were eliminated. To this end, the convolution functions in the form of the sum of input signals with an impulse response, i.e., the filter with a finite impulse response (FIR) and with an infinite impulse response (IIR), were used to compare the effectiveness of the methods estimating the vessel’s heading. The final stage of the research process in the methodology applied was the use of state- and time-discrete Markov processes whose task is to determine the matrices of the intensity of transitions between the states of individual compass systems.
An Ultrasonic Reverse Time Migration Imaging Method Based on Higher-Order Singular Value Decomposition
Authors: Zhang Yuncheng , Gao Xiang , Zhang Jiawei , Jiao Jingpin
Published: March 2022

An ultrasonic reverse time migration imaging method, based on high-order singular value decomposition, is proposed in the study to solve the problems of low signal-to-noise ratio (SNR) and excessive artifacts in defect ultrasonic detection imaging results of materials with high noise levels. In this method, based on the 3D structural properties of the ultrasonic full-matrix capture data, higher-order singular value decomposition is directly performed with the 3D data. The method overcomes the difficulty in selecting the number of singular values in the original singular value decomposition noise-reduction algorithm and realizes the one-step noise reduction processing of all the signals. Subsequently, the reverse time migration imaging is performed in the frequency domain, and high-quality acoustic images are obtained. The effects of the number of array elements, the center frequency of the excitation signal, and the number of defects on the denoising effect of the algorithm are investigated. It was experimentally demonstrated that the method could suppress the interference of noise signals and significantly improve the imaging SNR compared with total focusing method and the reverse time migration.
A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches
Authors: Agarwal Deepesh , Randall Philip , White Zachary , Bisnette Bayleigh , Dickson Jenalee , Allen Cross , Chamani Faraz , Prakash Punit , Ade Carl , Natarajan Balasubramaniam
Published: March 2022

Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2–3.5 GHz between a pair of planar antennas positioned across a participant’s arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2–3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).
Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network
Authors: Pitsik Elena N. , Frolov Nikita S. , Shusharina Natalia , Hramov Alexander E.
Published: March 2022

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.
Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review
Authors: Jorayeva Manzura , Akbulut Akhan , Catal Cagatay , Mishra Alok
Published: March 2022

Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.
Infrared Visual Sensing Detection of Groove Width for Swing Arc Narrow Gap Welding
Authors: Su Na , Wang Jiayou , Xu Guoxiang , Zhu Jie , Jiang Yuqing
Published: March 2022

To solve the current problem of poor weld formation due to groove width variation in swing arc narrow gap welding, an infrared passive visual sensing detection approach was developed in this work to measure groove width under intense welding interferences. This approach, called global pattern recognition, includes self-adaptive positioning of the ROI window, equal division thresholding and in situ dynamic clustering algorithms. Accordingly, the self-adaptive positioning method filters several of the nearest values of the arc’s highest point of the vertical coordinate and groove’s same-side edge position to determine the origin coordinates of the ROI window; the equal division thresholding algorithm then divides and processes the ROI window image to extract the groove edge and forms a raw data distribution of groove width in the data window. The in situ dynamic clustering algorithm dynamically classifies the preprocessed data in situ and finally detects the value of the groove width from the remaining true data. Experimental results show that the equal division thresholding algorithm can effectively reduce the influences of arc light and welding fume on the extraction of the groove edge. The in situ dynamic clustering algorithm can avoid disturbances from simulated welding spatters with diameters less than 2.19 mm, thus realizing the high-precision detection of the actual groove width and demonstrating stronger environmental adaptability of the proposed global pattern recognition approach.
A Review of Gas Measurement Set-Ups
Authors: Fuśnik Łukasz , Szafraniak Bartłomiej , Paleczek Anna , Grochala Dominik , Rydosz Artur
Published: March 2022

Measurements of the properties of gas-sensitive materials are a subject of constant research, including continuous developments and improvements of measurement methods and, consequently, measurement set-ups. Preparation of the test set-up is a key aspect of research, and it has a significant impact on the tested sensor. This paper aims to review the current state of the art in the field of gas-sensing measurement and provide overall conclusions of how the different set-ups impact the obtained results.
Wearable Sensors for Activity Recognition in Ultimate Frisbee Using Convolutional Neural Networks and Transfer Learning
Authors: Link Johannes , Perst Timur , Stoeve Maike , Eskofier Bjoern M.
Published: March 2022

In human activity recognition (HAR), activities are automatically recognized and classified from a continuous stream of input sensor data. Although the scientific community has developed multiple approaches for various sports in recent years, marginal sports are rarely considered. These approaches cannot directly be applied to marginal sports, where available data are sparse and costly to acquire. Thus, we recorded and annotated inertial measurement unit (IMU) data containing different types of Ultimate Frisbee throws to investigate whether Convolutional Neural Networks (CNNs) and transfer learning can solve this. The relevant actions were automatically detected and were classified using a CNN. The proposed pipeline reaches an accuracy of 66.6%, distinguishing between nine different fine-grained classes. For the classification of the three basic throwing techniques, we achieve an accuracy of 89.9%. Furthermore, the results were compared to a transfer learning-based approach using a beach volleyball dataset as the source. Even if transfer learning could not improve the classification accuracy, the training time was significantly reduced. Finally, the effect of transfer learning on a reduced dataset, i.e., without data augmentations, is analyzed. While having the same number of training subjects, using the pre-trained weights improves the generalization capabilities of the network, i.e., increasing the accuracy and F1 score. This shows that transfer learning can be beneficial, especially when dealing with small datasets, as in marginal sports, and therefore, can improve the tracking of marginal sports.
Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications
Authors: Manoni Alessandro , Gumiero Alessandro , Zampogna Alessandro , Ciarlo Chiara , Panetta Lorenzo , Suppa Antonio , Della Torre Luigi , Irrera Fernanda
Published: March 2022

In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days.
DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis
Authors: Karimi Raika , Mohammadi Arash , Asif Amir , Benali Habib
Published: March 2022

Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating “Radial Zoom” and “Rotation” motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively.
Risky Decision Making Due to Goal Conflicts in Firefighting—Debriefing as a Countermeasure to Enhance Safety Behavior
Authors: Hagemann Vera , Heinemann Lena , Peifer Corinna , Aust Fabienne , Holtz Maik
Published: March 2022

Firefighters act within extreme environments, work under threatening conditions and are often exposed to goal conflicts (e.g., self-protection vs. mission objective) during their missions. However, what are the consequences of these safety and task goal conflicts, and what countermeasures could help to reduce their occurrence? In an online survey, 340 firefighters were asked about goal conflicts, risky decision making, debriefings and the frequency of difficulties in teamwork during firefighting. Associations between the survey variables were determined by multivariate regression and mediation analyses. Data show that goal conflicts were associated with risky decision making and unsafe acts. Furthermore, debriefings were associated with fewer goal conflicts, as mediated by less-frequent difficulties with teamwork (communication, leadership and shared mental models). Though limited by the cross-sectional design of our study, the results provide evidence that debriefing is a valuable tool to reduce difficulties experienced with teamwork on missions and therefore reduce the occurrence of conflicting goals. Fewer goal conflicts are associated with a decrease in unsafe decisions and, thus, a safer working environment for firefighters. Accordingly, it is recommended to conduct debriefings, with an increased focus on team aspects.
Optimal Period for Winter Mountain Tourism in Romania
Authors: Rujescu Ciprian Ioan
Published: March 2022

Observations in Romania show that the months of January and February are starting to show an increasing interest for tourists in areas known for winter sports involving snow. This observation is at odds with the period hitherto considered traditional for winter tourism in Romania, i.e., from the end of December to the first few days of January, when school holidays and employee holidays are frequently scheduled. Analysis of the climatic data of recent years shows a shortening of the period when natural weather conditions are favorable for this type of tourism. In this paper it was statistically determined that the maximum share of negative temperature coverage of Romania’s territory tends to occur at present in the second half of January. It is therefore necessary to correlate the school and labor law timetables with the new climatic conditions and other measures to adapt to current conditions.
The Role of Eco-Industrial Parks in Promoting Circular Economy in Russia: A Life Cycle Approach
Authors: Abu-Qdais Hani A. , Kurbatova Anna I.
Published: March 2022

As an approach to move towards a sustainable waste management system, circular economy (CE) is gaining an increased interest by most countries. Russia is among the countries where the CE is one of the priorities of the country’s economy, with a market value of the CE is USD$ 755.05 billion. However, such a strategy is facing challenges and barriers which are country specific. This study aimed to review the status of the CE in Russia and to identify the obstacles that are hindering the country from achieving its objectives. Moreover, the study aimed to evaluate the role of eco-industrial parks (EIP) in Russia in promoting the CE model. The study findings indicate that the CE adoption in Russia is still in its early stages. To create an enabling environment for CE promotion in Russia, there is a need to overcome several institutional, technical, and social barriers. Russian higher educational institutions are playing a major role to create the critical mass of experts that will help the country transition towards a CE model. Using life cycle assessment (LCA) to analyze the environmental performance of one of the EIPs in Russia revealed that such enterprises are more sustainable than the business-as-usual scenarios, under which the generated solid waste is buried into landfill. The comparison shows that by diverting 1.813 million tons of mixed municipal solid waste that is generated in Moscow to EIP would lead to a reduction in environmental impacts. The total global warming potential of the EIP scenario is less, by 59%, than the direct landfilling scenario, while the eutrophication, acidification, smog, and ozone depletion are less, and fossil fuel depletion impacts under the second scenario are less, by 81%, 26%, 18%, and 81%, respectively. Furthermore, the health impacts including carcinogenic, non-carcinogenic, eco-toxicity were found to be 92%, 96%, and 96%, respectively, less than the baseline scenario.
The Digital Transformation of the Agricultural Value Chain: Discourses on Opportunities, Challenges and Controversial Perspectives on Governance Approaches
Authors: Martens Katrin , Zscheischler Jana
Published: March 2022

The extent to which the digitalisation of agriculture will make a significant contribution to solving urgent sustainability challenges will depend on the design of political, legal and economic frameworks. In this context, social discourses play a central role as they not only reflect collective interpretations and systems of meaning but also reproduce power relations in “truth regimes” and prepare policy actions. While a critical scientific debate on unintended side effects of the digital transformation on agriculture has recently emerged, there is little knowledge about the discourse relations beyond academia. This article presents the results of a discourse analysis during a two-day online conference on the digital transformation of the agricultural value chain. We systematically visited and analysed sessions and presentations. The aim was to identify the main themes, concepts and ideas and different perspectives among actors from science and practice. The results show a wide range of perceived opportunities and challenges but also controversies, especially regarding governance issues such as regulation versus nonregulation, centralised versus decentralised data sharing, the appropriate design of data sovereignty models and trust and evolving inequalities. In addition, it became apparent that discourses on digitalisation are largely expert affairs. We discuss and conclude that a sustainability-oriented digital transformation requires a critical perspective, reflexivity and an adaptive governance approach where science–society collaborations play a central role.
Study of Different Vegetation Types in Green Space Landscape Preference: Comparison of Environmental Perception in Winter and Summer
Authors: Duan Yifan , Li Shuhua
Published: March 2022

Through field perception in winter and summer, people’s preferences for vegetation types in green spaces with various structures (single-layer woodland, tree-shrub-grass composite woodland, tree-grass composite woodland, and single-layer grassland) and concrete and asphalt sites (areas) without plants are evaluated. The results show the following: (1) There are significant differences in landscape preference between the two seasons, and the environmental perception preference score for landscapes in summer is higher. (2) The preference score for the vegetation types in green spaces was single-layer woodland > tree-shrub-grass composite woodland > tree-grass composite woodland > single-layer grassland > concrete and asphalt sites (areas). The preference score for the single-layer woodland landscape in winter was significantly higher than that in summer. (3) The preferences of participants to carry out activities vary by season and the magnitude of thermal comfort; notably, rest and communication activities in green spaces with high thermal comfort, such as tree-shrub-grass composite woodland and single-layer woodland areas, are preferred in summer, and physical activities in the single-layer woodland landscape are preferred in winter. (4) Regardless of which environmental perception method is adopted, landscape preferences did not significantly differ by sex or professional background. Based on the above results, the two seasonal perception methods cannot replace each other. Therefore, the types of activities should be carefully selected to be appropriate for the specific vegetation types in green spaces and season in which they will occur, and the green space should be reasonably planned according to the thermal comfort of the vegetation types to provide a scientific basis for evaluating landscape perception and preferences in the future.
Climate Change Alters Soil Water Dynamics under Different Land Use Types
Authors: Horel Ágota , Zsigmond Tibor , Farkas Csilla , Gelybó Györgyi , Tóth Eszter , Kern Anikó , Bakacsi Zsófia
Published: March 2022

Land use and management affect soil hydrological processes, and the impacts can be further enhanced and accelerated due to climate change. In this study, we analyzed the possible long-term effects of different land use types on soil hydrological processes based on future climatic scenarios. Soil moisture and temperature probes were installed at four land use sites, a cropland, a vineyard, a meadow, and a forest area. Based on modeling of long-term changes in soil water content (SWC) using the HYDRUS 1D model, we found that changes in precipitation have a more pronounced effect on soil water content than changes in air temperature. Cropland is at the highest risk of inland water and SWC values above field capacity (FC). The number of days when the average SWC values are above FC is expected to increase up to 109.5 days/year from the current 52.4 days/year by 2081–2090 for the cropland. Our calculations highlight that the forest soil has the highest number of days per year where the SWC is below the wilting point (99.7 days/year), and based on the worst-case scenario, it can increase up to 224.7 days/year. However, general scenario-based estimates showed that vineyards are the most vulnerable to projected climate change in this area. Our study highlights the limitations of potential land use change for specific agricultural areas, and emphasizes the need to implement water retention measures to keep these agricultural settings sustainable.
A Life Cycle Assessment of Organic and Chemical Fertilizers for Coffee Production to Evaluate Sustainability toward the Energy–Environment–Economic Nexus in Indonesia
Authors: Rahmah Devi Maulida , Putra Agusta Samodra , Ishizaki Riaru , Noguchi Ryozo , Ahamed Tofael
Published: March 2022

Coffee is an important agricultural commodity that is branded according to its environmental criteria in the global market. Therefore, Indonesia’s coffee production system needs to be investigated to meet the demand for eco-labeling, which has become a consumer preference. This study aims to assess the comprehensive sustainability evaluation of coffee production nurtured by an organic fertilizing system (OFS), chemical-organic fertilizing system (COFS), and chemical fertilizing system (CFS) that focuses on the energy–environment–economic nexus. A life cycle assessment (LCA), life cycle cost analysis (LCC), and energy analysis were performed as methods to evaluate the environmental impact, economic performance, and energy requirement analysis. The results indicated that the OFS had superior performance in two sustainability aspects: resulting in the lowest environmental damage and generating the highest economic benefit. Simultaneously, COFS shows the highest sustainability performance as it consumes the least energy. In contrast, CFS indicated the lowest sustainability performance in all aspects: highest environmental impact, lowest economic benefit, and highest energy consumption. Therefore, OFS is strongly recommended to be applied broadly, considering its environmental and economic superiority. Consequently, massive OFS application was followed by higher energy consumption. Alternatively, COFS can be considered for application due to its higher energy performance, even though it can potentially result in higher environmental damage and lower economic benefit. However, the government should explicitly provide some effort for the broad application of OFS in financial and assistance support since the shifting process needs more time to adapt.
The Public’s Understanding of Superfoods
Authors: Kirsch Fabian , Lohmann Mark , Böl Gaby-Fleur
Published: March 2022

The term “superfood” is linked to food that is beneficial to health. However, there is no legal or clear scientific definition, and little is known about individual perceptions of this food group. Therefore, an online survey was conducted (n = 1006 respondents) to assess the public’s understanding of superfoods in Germany, including (a) the conceptual understanding of the term “superfood”, (b) corresponding consumption behavior, and (c) the risk–benefit appraisal. In total, 70% of respondents were aware of the term “superfood”, and 33% reported consuming superfoods at least once a week. The term was mainly associated with positive food properties, such as “healthy” and “contains vitamins”. Health benefits were rated to be much higher than potential health risks. In addition, imported foods were more likely to be labelled as superfoods, compared to domestic equivalents. In further analyses, we found women, younger people, and organic buyers to have an even more positive impression. That is, specific parts of the population may be particularly attracted to superfood products. Therefore, target-group specific information campaigns can be a useful tool to increase the population’s awareness of the potential health risks of superfood consumption and to highlight the presence of equivalent “domestic superfoods” in Germany.
An Analysis of Nanoparticles Derived from Coal Fly Ash Incorporated into Concrete
Authors: Neckel Alcindo , Pinto Diana , Adelodun Bashir , Dotto Guilherme L.
Published: March 2022

The environmental benefits of incorporating coal fly ash (CFA) into the concrete manufacturing process as a partial substitute for Portland cement are well known. What is less studied is the potential release of CFA derived nanomineral and amorphous nanoparticles during this process of incorporation. A thorough understanding of this makes it possible to understand the risks of exposure to particulates that are harmful to human health when CFA is mixed into concrete. The general objective of this study is to analyze airborne particulates released when CFA is mixed into concrete at the point of manufacture, focusing on the levels of nanominerals, amorphous nanoparticles and hazardous elements (HEs) contained within that are considered harmful to human health. These airborne particulates can be easily inhaled by plant workers in the absence of personal protective equipment. The authors analyzed samples of ash itself and collected actual airborne particulates using self-made passive samplers installed at the manufacturing plant. Regarding the ash analyzed, iron (Fe) was found in large amounts in relation to calcium (Ca), magnesium (Mg) and silicon (Si). The transport, disposal and application of CFA in civil construction projects can provide an increased efficiency and reduce overall costs associated with the production of concrete. However, CFA poses a threat to human health due to the significant amount of HEs, nanominerals, and amorphous nanoparticles found to be released into the environment at the manufacturing plant.
Spatial Analysis of Inequality in Thailand: Applications of Satellite Data and Spatial Statistics/Econometrics
Authors: Puttanapong Nattapong , Luenam Amornrat , Jongwattanakul Pit
Published: March 2022

To formulate and monitor the progress of development policies, acquiring data with sufficient spatiotemporal details is inevitable. With the increasing availability of open remote-sensing data and open-source software packages, this research suggested the novelty integration of satellite data and spatial analytical methods, enabling a timely and costless framework for assessing the nationwide socioeconomic condition. Specifically, the spatial statistical and spatial econometrical methods were applied to geospatial data to identify the clustering patterns and the localized associations of inequality in Thailand. The spatial statistical results showed that Bangkok and its vicinity had been a cluster of high socioeconomic conditions, representing the spatial inequality of development. In addition, results of the spatial econometrical models showed that the satellite-based indicators could identify the socioeconomic condition (with p-value < 0.010 and R-squared ranging between 0.345 and 0.657). Inequality indicators (i.e., Gini, Thiel and Atkinson) were then constructed by using survey-based and satellite-based data, informing that spatial inequality has been slowly declining. These findings recommended the new establishment of polycentric growth poles that offer economic opportunities and reduce spatial inequality. In addition, in accordance with Sustainable Development Goal 10 (reduced inequalities), this analytical framework can be applied to country-specific implications along with the global scale extensions.
Sustainable Development Directions for Wine Tourism in Douro Wine Region, Portugal
Authors: Trigo Ana , Silva Paula
Published: March 2022

Despite sustainable wine tourism being one of the hottest topics of the moment, there is still a considerable knowledge gap. If managed with due care and consideration of the region’s carrying capacity, wine tourism can be an essential regional development tool for improving business performance, environmental awareness, and community values. On the other hand, it can be responsible for negative environmental impacts, creating local resentment. This paper explored and contextualized wine tourism and sustainable winegrowing practices in the Portuguese Douro wine region. The aim was to offer practical recommendations and directives for such a unique landscape. From the adoption of regenerative agriculture and natural-based solutions at the field level to engaging in sustainability wine programs or certification schemes integrating both sectors, it is indispensable to develop frameworks to assess sustainability performance, tourism development, and the impact on the local population. Nevertheless, to accomplish business success and territorial development, a win–win relationship between all stakeholders is foremost needed. The Douro region must therefore start by investing in stronger collaboration networks between local actors through careful and integrated planning so that the needs of all interested parties, including its residents, are considered and incorporated in future sustainable tourism initiatives.
Re-Examining the Income–CO2 Emissions Nexus Using the New Kink Regression Model: Does the Kuznets Curve Exist in G7 Countries?
Authors: Liu Pei-Zhi , Narayan Seema , Ren Yi-Shuai , Jiang Yong , Baltas Konstantinos , Sharp Basil
Published: March 2022

More countries have made carbon neutral or net zero emission commitments since 2019. Within this context, re-examining the environmental Kuznets curve (EKC) hypothesis plays an essential role in sizing up the global economic development situation and realizing the global carbon emission reduction target. A methodological challenge in testing the EKC hypothesis, which states that increasing income makes CO2 emissions begin to decline beyond a turning point, lies in determining if this benchmark point exists. The EKC hypothesis between income and CO2 emissions is reassessed by applying a new kink regression model for the G7 countries from 1890 to 2015. Results reveal the inverted U-shaped nexus does not exist for US, Germany, Italy, Canada and Japan. For these five countries, the EKC curve has a turning point, but the positive impact of incomes on CO2 emissions becomes significantly smaller after the turning point. We describe this relationship as a pseudo-EKC. K.U.K. and France are the only exceptions, fitting the EKC hypothesis. Further analysis indicates that the relationship between income and SO2 emissions presents an inverted U-shaped curve. Moreover, we observe that the turning point occurs at different points in time for the different G7 countries. Therefore, environmental policies targeting pollutant emission reduction should consider the different characteristics of different pollutants and regions.
Examining the Socio-Economic and Natural Resource Risks of Food Estate Development on Peatlands: A Strategy for Economic Recovery and Natural Resource Sustainability
Authors: Yeny Irma , Garsetiasih Raden , Suharti Sri , Gunawan Hendra , Sawitri Reny , Karlina Endang , Narendra Budi Hadi , Surati  , Ekawati Sulistya , Djaenudin Deden , Rachmanadi Dony , Heriyanto Nur Muhammad , Sylviani  , Takandjandji Mariana
Published: March 2022

Given the huge impact of the COVID-19 pandemic on the food and agriculture sectors, rapid measures are needed to reduce the risk of food crises, especially among the poor and the most vulnerable communities. The government of Indonesia planned to establish the Food Estate National Program to ensure food security. Most of the area will be on peatlands, and as such, the program still faces pros and cons as it might open up opportunities for deforestation, threats to biodiversity, and loss of community livelihoods. We conducted the present research in Central Kalimantan to formulate a food estate (FE) development strategy by taking into account the potential benefits and risks to ensure increases in the local community’s welfare and the sustainability of biodiversity. Data were collected through field surveys, interviews, focus group discussion (FGD), and literature studies. The results show that the operation of a food estate on degraded peatlands has a moderate to high level of risk of negative impacts. Community activities and changes in farming methods through using more inputs and mechanical equipment are the most risky activities in FE development. The low substitutability of peatlands requires mitigation efforts as part of risk management. The operation of food systems on peatlands must be based on a strong sustainability perspective with a main principle of complementary resources. The main strategy is to protect natural resources and replace cultivated exotic plants with potential native peat plants with minimal risk. In addition, the policy and capacity building of farmers towards a business-oriented direction will maximize socioeconomic benefits. Utilization of biodiversity and low-impact cultivation techniques can ensure sustainability.
Changes in Soil Aggregate Fractions, Stability, and Associated Organic Carbon and Nitrogen in Different Land Use Types in the Loess Plateau, China
Authors: Zhang Yi , Li Peng , Liu Xiaojun , Xiao Lie
Published: March 2022

Rational land use can enhance soil nutrient sequestration and control erosion, but the mechanisms of the ecological restoration of soil-aggregate-associated carbon and nitrogen are still not well understood. A large-scale ecological restoration program was launched in the Loess Plateau during the 1990s. The ecological restoration programs involved converting slope farmland to woodland, grassland, shrub land, and terrace. We studied their effects in relation to cultivated land as control on soil aggregate structure and stability and their associated organic carbon and total nitrogen contents to 60 cm soil depth in the Loess Plateau. Our results indicate that the restoration practices reduced soil aggregate fragmentation, increased soil structure stability, and transformed micro-aggregates into small and large aggregates. Comparing with the soil aggregate >0.25 mm in cultivated land, the amount in woodland, grassland, shrub land and terrace increased by 71%, 66%, 46%, and 35%, respectively, which improved soil health overall. The mean weight diameter of aggregate indicates that soil aggregate stability (SAS) increased and soil hydraulic erosion resistance improved. In conclusion, ecological restoration directly or indirectly affected SAS through the influence of soil organic carbon and total nitrogen in different soil layers. Results of this study provide a scientific reference for understanding stabilization of soil aggregate and regional restoration.
How Symmetry Influences the Dissociation of Protonated Cyclic Peptides
Authors: Pérez-Mellor Ariel F. , Spezia Riccardo , Zehnacker Anne
Published: March 2022

Protonated cyclic dipeptides undergo collision-induced dissociation, and this reaction mechanism strongly depends on the symmetry and the nature of the residues. We review the main dissociation mechanism for a series of cyclic dipeptides, obtained through chemical dynamics simulations. The systems range from the symmetrical cyclo-(glycyl-glycyl), with two possible symmetrical protonation sites located on the peptide ring, to cyclo-(tyrosyl-prolyl), where the symmetry of protonation sites on the peptide ring is broken by the dissimilar nature of the different residues. Finally, cyclo-(phenylalanyl-histidyl) shows a completely asymmetric situation, with the proton located on one of the dipeptide side chains, which explains the peculiar fragmentation mechanism induced by shuttling the proton, whose efficiency is strongly dependent on the relative chirality of the residues.
Oblivious Access for Decentralized Database Systems: A New Asymmetric Framework from Smart Contracts
Authors: Guo Zhong-Yi , Chen Yu-Chi , Lin Hsiu-Ping
Published: March 2022

With the rapid development of cloud servers, storing data on cloud servers has become a popular option. However, cloud servers are centralized. Storing data on centralized cloud servers may involve some risks. For example, the data access pattern may be revealed when accessing data on cloud servers. Therefore, protecting a user’s patterns has become a crucial concern. Oblivious RAM (ORAM) is a candidate solution to hide the data access pattern. However, it inherently induces some overhead of accessing data, and many blockchain-based applications also do not consider the access pattern leakage issues. In this paper, we address these issues above by proposing a decentralized database system with oblivious access in a (parallel) smart contract model. The interactions of oblivious access are asymmetric where the smart contract side is expected to put much effort into computation. The proposed system slightly reduces the overhead of ORAM and overcomes the issues stemming from the centralization of servers. The main techniques are to use the garbled circuits to reduce the cost of communication and to combine with the parallel smart contract model to (conceptually) improve the performance of smart contract execution on the blockchain.
1/3 Order Subharmonic Resonance Control of a Mass-Damper-Spring Model via Cubic-Position Negative-Velocity Feedback
Authors: Kandil Ali , Hamed Yasser S. , Abualnaja Khadijah M. , Awrejcewicz Jan , Bednarek Maksymilian
Published: March 2022

A cubic-position negative-velocity (CPNV) feedback controller is proposed in this research in order to suppress the nontrivial oscillations of the 1/3 order subharmonic resonance of a mass-damper-spring model. Based on the Krylov–Bogoliubov (KB) averaging method, the model’s equation of motion is approximately solved and tested for stability. The nontrivial solutions region is plotted to determine where these solutions occur and try to quench them. The controller parameters can play crucial roles in eliminating such regions, keeping only the trivial solutions, and improving the transient response of the car’s oscillations. Different response curves and relations are included in this study to provide the reader a wide overview of the control process.