bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction
Authors: Wang Weilin , Mao Wenjing , Tong Xueli , Xu Gang
Published: March 2021

Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.
Long-Time Interval Satellite Image Analysis on Forest-Cover Changes and Disturbances around Protected Area, Zeya State Nature Reserve, in the Russian Far East
Authors: Khatancharoen Chulabush , Tsuyuki Satoshi , Bryanin Semyon V. , Sugiura Konosuke , Seino Tatsuyuki , Lisovsky Viktor V. , Borisova Irina G. , Wada Naoya
Published: March 2021

Boreal forest areas in the Russian Far East contained very large intact forests. This particular area is considered one of the most productive and diverse forests in the boreal biome of the world, and it is also home to many endangered species. Zeya State Nature Reserve is located at the southern margin of the boreal forest area in the Russian Far East and has rich fauna and flora. However, the forest in the region faced large-scale forest fires and clearcutting for timber recently. The information of disturbances is rarely understood. This study aimed to explore the effects of disturbance and forest dynamics around the reserve. Our study used two-year overlaid Landsat images from Landsat 5 Thematic Mapper (TM) and 8 Operational Land Imager (OLI), to generate forest-cover-change maps of 1988–1999, 1999–2010, and 2010–2016. In this paper, we analyze the direction of forest successional stages, to demonstrate the effectiveness of this protected area in terms of preventing human-based deforestation on the vegetation indices. The vegetation indices included the normalized burn ratio (NBR), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). The study provided information on the pattern of forest-cover change and disturbance area around the reserve. The NDWI was used to differentiate between water and non-water areas. The mean values of NBR and NDVI were calculated and determine the forest successional stages between burn, vegetation recovery, grass, mixed forest, oak forest, and birch and larch forest. The accuracy was assessed by using field measurements, field photos, and high-resolution images as references. Overall, our classification results have high accuracy for all three periods. The most disturbed area occurred during 2010–2016. The reserve was highly protected, with no human-disturbance activity. However, large areas from fire disturbance were found (137 km2) during 1999–2010. The findings also show a large area of disturbance, mostly located outside of the reserve. Mixed disturbance increased to almost 50 km2 during 2010–2016, in the buffer zone and outside of the reserve. We recommend future works to apply our methods to other ecosystems, to compare the forest dynamics and disturbance inside and outside the protected area.
Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
Authors: Zhang Yuan , Feng Xiaoming , Fu Bojie , Chen Yongzhe , Wang Xiaofeng
Published: March 2021

Water stress is one of the primary environmental factors that limits terrestrial ecosystems’ productivity. Hense, the way to quantify gobal vegetation productivity’s vulnerability under water stress and reveal its seasonal dynamics in response to drought is of great significance in mitigating and adapting to global changes. Here, we estimated monthly gross primary productivity (GPP) first based on light-use efficiency (LUE) models for 1982–2015. GPP’s response time to water availability can be determined by correlating the monthly GPP series with the multiple timescale Standardized Precipitation Evapotranspiration Index (SPEI). Thereafter, we developed an optimal bivariate probabilistic model to derive the vegetation productivity loss probabilities under different drought scenarios using the copula method. The results showed that LUE models have a good fit and estimate GPP well (R2 exceeded 0.7). GPP is expected to decrease in 71.91% of the global land vegetation area because of increases in radiation and temperature and decreases in soil moisture during drought periods. Largely, we found that vegetation productivity and water availability are correlated positively globally. The vegetation productivity in arid and semiarid areas depends considerably upon water availability compared to that in humid and semi-humid areas. Weak drought resistance often characterizes the land cover types that water availability influences more. In addition, under the scenario of the same level of GPP damage with different drought degrees, as droughts increase in severity, GPP loss probabilities increase as well. Further, under the same drought severity with different levels of GPP damage, drought’s effect on GPP loss probabilities weaken gradually as the GPP damage level increaes. Similar patterns were observed in different seasons. Our results showed that arid and semiarid areas have higher conditional probabilities of vegetation productivity losses under different drought scenarios.
Automatic Scheduling Tool for Balloon-Borne Planetary Optical Remote Sensing
Authors: Shi Zhen , Zhao Yong , He Fei , Yao Zhonghua , Rong Zhaojin , Wei Yong
Published: March 2021

The balloon-borne Planetary Atmosphere Spectroscopic Telescope (PAST), China’s first planetary optical remote-sensing project, will be launched for testing and conducting scientific flights during 2021 and 2022. Images of the planetary atmosphere and plasma in ultraviolet and visible wavelengths will be used to investigate the diversity of the planetary space environment in the solar system and their different drivers. Because simultaneous observation of multiple target planets in the solar system is possible, effective observation scheduling is critical to acquire high scientific merit spectroscopic imaging data. Herein, we demonstrate an automatic scheduling tool (AST) to aid the planning of observation schedules. The AST is primarily based on a planetary ephemeris and is realized on the basis of the geometrical information and optical requirements of the telescope. The temporal variations of the planetary reference frames can also be obtained to assist in the positioning and data processing of the telescope. As a part of the Chinese deep-space exploration plan, several ground-based planetary optical telescopes will be constructed in China in the future. With the use of the proposed AST, such telescopes can achieve maximum efficiency.
Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model
Authors: Zhu Kun , Zhang Xiaodong , Chen Guanzhou , Tan Xiaoliang , Liao Puyun , Wu Hongyu , Cui Xiujuan , Zuo Yinan , Lv Zhiyong
Published: March 2021

Satellite video single object tracking has attracted wide attention. The development of remote sensing platforms for earth observation technologies makes it increasingly convenient to acquire high-resolution satellite videos, which greatly accelerates ground target tracking. However, overlarge images with small object size, high similarity among multiple moving targets, and poor distinguishability between the objects and the background make this task most challenging. To solve these problems, a deep Siamese network (DSN) incorporating an interframe difference centroid inertia motion (ID-CIM) model is proposed in this paper. In object tracking tasks, the DSN inherently includes a template branch and a search branch; it extracts the features from these two branches and employs a Siamese region proposal network to obtain the position of the target in the search branch. The ID-CIM mechanism was proposed to alleviate model drift. These two modules build the ID-DSN framework and mutually reinforce the final tracking results. In addition, we also adopted existing object detection datasets for remotely sensed images to generate training datasets suitable for satellite video single object tracking. Ablation experiments were performed on six high-resolution satellite videos acquired from the International Space Station and “Jilin-1” satellites. We compared the proposed ID-DSN results with other 11 state-of-the-art trackers, including different networks and backbones. The comparison results show that our ID-DSN obtained a precision criterion of 0.927 and a success criterion of 0.694 with a frames per second (FPS) value of 32.117 implemented on a single NVIDIA GTX1070Ti GPU.
High-Dimensional Satellite Image Compositing and Statistics for Enhanced Irrigated Crop Mapping
Authors: Wellington Michael J. , Renzullo Luigi J.
Published: March 2021

Accurate irrigated area maps remain difficult to generate, as smallholder irrigation schemes often escape detection. Efforts to map smallholder irrigation have often relied on complex classification models fitted to temporal image stacks. The use of high-dimensional geometric median composites (geomedians) and high-dimensional statistics of time-series may simplify classification models and enhance accuracy. High-dimensional statistics for temporal variation, such as the spectral median absolute deviation, indicate spectral variability within a period contributing to a geomedian. The Ord River Irrigation Area was used to validate Digital Earth Australia’s annual geomedian and temporal variation products. Geomedian composites and the spectral median absolute deviation were then calculated on Sentinel-2 images for three smallholder irrigation schemes in Matabeleland, Zimbabwe, none of which were classified as areas equipped for irrigation in AQUASTAT’s Global Map of Irrigated Areas. Supervised random forest classification was applied to all sites. For the three Matabeleland sites, the average Kappa coefficient was 0.87 and overall accuracy was 95.9% on validation data. This compared with 0.12 and 77.2%, respectively, for the Food and Agriculture Organisation’s Water Productivity through Open access of Remotely sensed derived data (WaPOR) land use classification map. The spectral median absolute deviation was ranked among the most important variables across all models based on mean decrease in accuracy. Change detection capacity also means the spectral median absolute deviation has some advantages for cropland mapping over indices such as the Normalized Difference Vegetation Index. The method demonstrated shows potential to be deployed across countries and regions where smallholder irrigation schemes account for large proportions of irrigated area.
Using Information Content to Select Keypoints for UAV Image Matching
Authors: Mousavi Vahid , Varshosaz Masood , Remondino Fabio
Published: March 2021

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images.
Introduction of the Advanced Meteorological Imager of Geo-Kompsat-2a: In-Orbit Tests and Performance Validation
Authors: Kim Dohyeong , Gu Minju , Oh Tae-Hyeong , Kim Eun-Kyu , Yang Hye-Ji
Published: March 2021

Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose SATtellite-2A, GK2A), a new generation of Korean geostationary meteorological satellite, carry state-of-the-art optical sensors with significantly higher radiometric, spectral, and spatial resolution than the Communication, Ocean, and Meteorological Satellite (COMS) previously available in the geostationary orbit. The new Advanced Meteorological Imager (AMI) on GK2A has 16 observation channels, and its spatial resolution is 0.5 or 1 km for visible channels and 2 km for near-infrared and infrared channels. These advantages, when combined with shortened revisit times (around 10 min for full disk and 2 min for sectored regions), provide new levels of capacity for the identification and tracking of rapidly changing weather phenomena and for the derivation of quantitative products. These improvements will bring about unprecedented levels of performance in nowcasting services and short-range weather forecasting systems. Imagery from the satellites is distributed and disseminated to users via multiple paths, including internet services and satellite broadcasting services. In post-launch performance validation, infrared channel calibration is accurate to within 0.2 K with no significant diurnal variation using an approach developed under the Global Space-based Inter-Calibration System framework. Visible and near infrared channels showed unexpected seasonal variations of approximately 5 to 10% using the ray matching method and lunar calibration. Image navigation was accurate to within requirements, 42 µrad (1.5 km), and channel-to-channel registration was also validated. This paper describes the features of the GK2A AMI, GK2A ground segment, and data distribution. Early performance results of AMI during the commissioning period are presented to demonstrate the capabilities and applications of the sensor.
Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives
Authors: Mao Qi , Peng Jian , Wang Yanglin
Published: March 2021

Remotely sensed land surface temperature (LST) distribution has played a valuable role in land surface processes studies from local to global scales. However, it is still difficult to acquire concurrently high spatiotemporal resolution LST data due to the trade-off between spatial and temporal resolutions in thermal remote sensing. To address this problem, various methods have been proposed to enhance the resolutions of LST data, and substantial progress in this field has been achieved in recent years. Therefore, this study reviewed the current status of resolution enhancement methods for LST data. First, three groups of enhancement methods—spatial resolution enhancement, temporal resolution enhancement, and simultaneous spatiotemporal resolution enhancement—were comprehensively investigated and analyzed. Then, the quality assessment strategies for LST resolution enhancement methods and their advantages and disadvantages were specifically discussed. Finally, key directions for future studies in this field were suggested, i.e., synergy between process-driven and data-driven methods, cross-comparison among different methods, and improvement in localization strategy.
EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests
Authors: Antoniou Evangelos , Bozios Pavlos , Christou Vasileios , Tzimourta Katerina D. , Kalafatakis Konstantinos , G. Tsipouras Markos , Giannakeas Nikolaos , Tzallas Alexandros T.
Published: March 2021

Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.
Methods for Lowering the Power Consumption of OS-Based Adaptive Deep Brain Stimulation Controllers
Authors: Rodriguez-Zurrunero Roberto , Araujo Alvaro , Lowery Madeleine M.
Published: March 2021

The identification of a new generation of adaptive strategies for deep brain stimulation (DBS) will require the development of mixed hardware–software systems for testing and implementing such controllers clinically. Towards this aim, introducing an operating system (OS) that provides high-level features (multitasking, hardware abstraction, and dynamic operation) as the core element of adaptive deep brain stimulation (aDBS) controllers could expand the capabilities and development speed of new control strategies. However, such software frameworks also introduce substantial power consumption overhead that could render this solution unfeasible for implantable devices. To address this, in this work four techniques to reduce this overhead are proposed and evaluated: a tick-less idle operation mode, reduced and dynamic sampling, buffered read mode, and duty cycling. A dual threshold adaptive deep brain stimulation algorithm for suppressing pathological oscillatory neural activity was implemented along with the proposed energy saving techniques on an energy-efficient OS, YetiOS, running on a STM32L476RE microcontroller. The system was then tested using an emulation environment coupled to a mean field model of the parkinsonian basal ganglia to simulate local field potential (LFPs) which acted as a biomarker for the controller. The OS-based controller alone introduced a power consumption overhead of 10.03 mW for a sampling rate of 1 kHz. This was reduced to 12 μW by applying the proposed tick-less idle mode, dynamic sampling, buffered read and duty cycling techniques. The OS-based controller using the proposed methods can facilitate rapid and flexible testing and implementation of new control methods. Furthermore, the approach has the potential to become a central element in future implantable devices to enable energy-efficient implementation of a wide range of control algorithms across different neurological conditions and hardware platforms.
Dual Tunable MZIs Stationary-Wave Integrated Fourier Transform Spectrum Detection
Authors: Chen Xinyang , Huang Peijian , Wang Ning , Zhu Yong , Zhang Jie
Published: March 2021

In order to resolve spectral alias due to under sampling in traditional stationary-wave integrated Fourier transform (SWIFT) spectrometers, an all-on-chip waveguide based on dual tunable Mach-Zehnder interferometer (MZI) stationary-wave integrated Fourier transform technology (DTM-SWIFT) is proposed. Several gold nanowires are asymmetrically positioned at two sides of zero optical path difference and scatter the interference fringes information, which can avoid aliasing of spectral signals and help to gain high spectral resolution. A systematic theoretical analysis is carried on in detail, including the optical distribution characteristics based on multi-beam interference, stationary-wave theorem and signal reconstruction method based on the FT technology. The results show that the method can complete a resolution of 6 nm for Gauss spectrum reconstruction using only 6 gold nanowires, and a resolution of 5 cm−1 for Raman spectrum reconstruction using 25 gold nanowires.
Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model
Authors: Chen Jing , Huang Xinyu , Jiang Hao , Miao Xiren
Published: March 2021

Human activity recognition (HAR) has been a vital human–computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodology equipped with low-cost sensors is proposed for high-accuracy device-free human activity recognition. ESP8266, as the sensing hardware, was utilized to deploy the WiFi sensor network and collect multi-dimensional received signal strength indicator (RSSI) records. The proposed learning model presents a coarse-to-fine hierarchical classification framework with two-level perception modules. In the coarse-level stage, twelve statistical features of time–frequency domains were extracted from the RSSI measurements filtered by a butterworth low-pass filter, and a support vector machine (SVM) model was employed to quickly recognize the basic human activities by classifying the signal statistical features. In the fine-level stage, the gated recurrent unit (GRU), a representative type of recurrent neural network (RNN), was applied to address issues of the confused recognition of similar activities. The GRU model can realize automatic multi-level feature extraction from the RSSI measurements and accurately discriminate the similar activities. The experimental results show that the proposed approach achieved recognition accuracies of 96.45% and 94.59% for six types of activities in two different environments and performed better compared the traditional pattern-based methods. The proposed hierarchical learning method provides a low-cost sensor-based HAR framework to enhance the recognition accuracy and modeling efficiency.
ROS-Based Smart Walker with Fuzzy Posture Judgement and Power Assistance
Authors: Chang Yeong-Hwa , Sahoo Nilima , Chen Jing-Yuan , Chuang Shang-Yi , Lin Hung-Wei
Published: March 2021

In recent years the increased rate of the aging population has become more serious. With aging, the elderly sometimes inevitably faces many problems which lead to slow walking, unstable or weak limbs and even fall-related injuries. So, it is very important to develop an assistive aid device. In this study, a fuzzy controller-based smart walker with a distributed robot operating system (ROS) framework is designed to assist in independent walking. The combination of Raspberry Pi and PIC microcontroller acts as the control kernel of the proposed device. In addition, the environmental information and user postures can be recognized with the integration of sensors. The sensing data include the road slope, velocity of the walker, and user’s grip forces, etc. According to the sensing data, the fuzzy controller can produce an assistive force to make the walker moving more smoothly and safely. Apart from this, a mobile application (App) is designed that allows the user’s guardian to view the current status of the smart walker as well as to track the user’s location.
Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0
Authors: Tanuska Pavol , Spendla Lukas , Kebisek Michal , Duris Rastislav , Stremy Maximilian
Published: March 2021

One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.
Scientometric Analysis of Research on Socioemotional Wealth
Authors: Araya-Castillo Luis , Hernández-Perlines Felipe , Moraga Hugo , Ariza-Montes Antonio
Published: March 2021

Scientometric studies have become very important within the scientific environment in general, and in the family firm area in particular. This study aims at conducting a bibliometric analysis of socioemotional wealth within family firms. To this end, a background search of the terms family firm and socioemotional wealth has been carried out in the Web of Science, specifically in specialized journals published between 1975 and 2019 in the Science Citation Index. The resulting scientometric analyses are of the number of papers and citations, the main authors and journals, the WoS categories, the institutions, the countries and the word co-occurrence. One of the main conclusions of this paper is the abundance of studies that have been conducted on socioemotional wealth in family firms, which is reflected in the number of publications (501) and of citations of these studies (12,090). Another significant revelation is the copious number of authors, with Gómez-Mejía being the most relevant one and De Massis the one with the highest number of publications. Also noteworthy are the many USA-based institutions, with the Mississippi State University and the University of North Carolina being the two most prominent. In addition, studies have been carried out about family firms’ focus, mainly, on performance and ownership.
Evaluation of Road Infrastructure Projects: A Life Cycle Sustainability-Based Decision-Making Approach
Authors: Arshad Husnain , Thaheem Muhammad Jamaluddin , Bakhtawar Beenish , Shrestha Asheem
Published: March 2021

Economic growth, social wellbeing, and infrastructure are strongly interrelated and jointly contribute to national development. Therefore, evaluation and selection of a road infrastructure project direly need a comprehensive sustainability assessment integrating holistic decision criteria. This study presents an elaborate life cycle sustainability-based project evaluation tool, comprising an assessment framework, an integration model, and a decision framework. In the first phase, a life cycle sustainability assessment (LCSA) framework for road infrastructure is established using mixed methods. In the second phase, interviews are conducted to obtain pairwise comparisons among impact categories and subjective reasoning of their priorities. Analytical hierarchy process (AHP) is adopted to develop the LCSA integration model. The minimum threshold limits of impact categories are evaluated and integrated into the proposed decision framework. Further, thematic and cross-sectional analyses are performed on the interview findings to rationalize the proposed decision framework. The findings include a detailed and customized project assessment framework, an integration model, and a decision framework for the assessment of different project alternatives. This study helps policy- and decision-makers in selecting the project alternative by maximizing sustainability in road infrastructure projects. Insights into environmental and social externalities and their quantitative interpretation throughout the life of the road are also achieved.
Making the Invisible Visible: Eco-Art and Design against the Anthropocene
Authors: Cucuzzella Carmela
Published: March 2021

This paper examines a series of art and design installations in the public realm that aim to raise awareness or activate change regarding pressing ecological issues. Such works tend to place environmental responsibility on the shoulders of the individual citizen, aiming to educate but also to implicate them in the age of the Anthropocene. How and what these works aim to accomplish, are key to a better understanding the means of knowledge transfer and potential agents of change in the Anthropocene. We study three cases in this paper. These are examined through: (1) their potential to raise awareness or activate behavior change; (2) how well they are capable of making the catastrophic situations, which are invisible to most people, visible; and (3) how well they enable systemic change in the catastrophic situations. In the three cases studied, we find that they are successful in helping to raise awareness and even change individual behavior, they are successful in rendering the invisible visible, but they are incapable of engendering any systemic change of the catastrophic situations depicted.
Are Energy Consumption, Population Density and Exports Causing Environmental Damage in China? Autoregressive Distributed Lag and Vector Error Correction Model Approaches
Authors: Rahman Mohammad Mafizur , Vu Xuan-Binh (Benjamin)
Published: March 2021

This paper investigates whether energy consumption, population density, and exports are the main factors causing environmental damage in China. Using annual data from 1971–2018, unit root tests are applied for the stationarity analyses, and Autoregressive Distributed Lag (ARDL) bounds tests are used for the long-run relationships between the variables. A Vector Error Correction Model (VECM) Granger approach is employed to examine the causal relationships amongst the variables. Our findings show that the selected variables are cointegrated, and that energy consumption and economic growth are identified as the main reasons for CO2 emissions in both the short-run and long-run. In contrast, exports reduce CO2 emissions in the long-run. Short-run unidirectional Granger causality is found from economic growth to energy consumption, CO2 emissions and exports, and from CO2 emissions to energy consumption and exports. Moreover, long-run causal links exist between CO2 emissions and exports. Five policy recommendations are made following the obtained results.
Effects of Combined Application of Potassium Silicate and Salicylic Acid on the Defense Response of Hydroponically Grown Tomato Plants to Ralstonia solanacearum Infection
Authors: Jiang Ni-Hao , Zhang Shi-Han
Published: March 2021

Bacterial wilt, caused by soilborne pathogenic bacterium Ralstonia solanacearum, is a serious and widespread disease that affects global tomato production. Both silicon (Si) and salicylic acid (SA) play important roles in enhancing tomato resistance against bacterial wilt, however, their combined effects on the defense responses of infected tomato plants remain unknown. Hence, the combined effects of Si and SA on physiological and biochemical parameters of R. solanacearum-infected tomato plants were investigated. The combination treatment of Si and SA significantly decreased disease incidences, lipoxygenase (LOX) activity and ethylene (ET) production. The combined treatments were more prominent in improving the morphological traits of root systems, such as root length, root surface area, average root diameter and root volume. The activities of polyphenol oxidase (PPO) and peroxidase (POD) and the concentrations of total soluble phenolics (TSPs) and lignin-thioglycolic acid (LTGA) derivatives were significantly increased in the plants with combined treatments. Si in combination with SA could significantly enhance neutral invertase (NI) and acid invertases (AI) activities in the leaves of tomato plants at 3 days post-infection (dpi) compared with application of Si alone. Three defense-related genes, PAL, POD and pathogenesis-related protein 1 (PR1), were significantly induced in Si+SA treatment at 7 dpi when compared with individual application of Si or SA. The expression level of salicylic acid-binding protein 2 (SABP2) was significantly higher for combination treatment when compared with treatment of Si or SA alone. The possible mechanisms involved in the synergistic effects of Si and SA on the control of tomato bacterial wilt were proposed. This study indicates that under hypertonic conditions, the combined application of 2.0 mM potassium silicate (K2SiO3) and 0.5 mM SA had a synergistic effect on the control of tomato bacterial wilt.
Building Resilience: The Gendered Effect of Climate Change on Food Security and Sovereignty in Kakamega-Kenya
Authors: Liru Pauline , Heinecken Lindy
Published: March 2021

Climate change is a global threat, affecting the food security and food sovereignty of many depending on agriculture for their livelihoods. This is even more pronounced in Kenya, given their over-reliance on rain-fed crops and the frequency of floods and droughts in the country. Through qualitative interviews, this study set out to establish how climate change not only affects the food security, production and consumption of rural women farmers in Kakamega County, Kenya, but their response to climate shocks. Using resilience theory as a lens, we established that women use different pathways to mitigate the effects of climate change on their livelihoods. The study found that initially women adopt coping strategies that are reactive and not sustainable, but soon adapted their farming strategies, using their indigenous knowledge to exercise some control over both their food security and food sovereignty. Besides this, they use their human and social capital to expand their networks of support. By linking up to other organizations and gaining access to government support, they are able to challenge patriarchal relations that perpetuate poverty and inequality and bring about more transformative and sustainable responses to climate change.
Russian Tourism Enterprises’ Marketing Innovations to Meet the COVID-19 Challenges
Authors: Sheresheva Marina , Efremova Marina , Valitova Lilia , Polukhina Anna , Laptev Georgy
Published: March 2021

This paper discusses the results of a study aimed at gaining a deeper understanding of how the COVID-19 pandemic has changed the Russian tourism market and how local tourism enterprises respond to challenges caused by the COVID-19 pandemic. Summarizing their experience of doing business in crisis conditions can allow a more efficient response to similar crises in the future and contribute to a more resilient tourism sector in the aftermath of the pandemic. In order to meet the research objective, we combined various sources of operational statistical information about the state of the industry with the results of a survey conducted in the Nizhny Novgorod region and in-depth interviews with representatives of tourism enterprises. We found that Russian tourism enterprises have proved to be quite resilient by looking for additional opportunities, and paying special attention to staff development and team consolidation. However, there are problems faced by local tourism enterprises in obtaining state support. Therefore, tourism enterprises that rely on themselves and innovate to create services and products adjusted to new customer needs and preferences have a better chance to survive. In particular, the wider use of digital marketing instruments helps SMEs to propose a new value for customers aligned to the new requirements and trends in tourism, such as travel security, prevailing interest in individual and short-term trips for short distances, and the sharply increased demand for domestic tourism destinations.
English Validation of a Short Scale Designed to Detect Negative Attitudes towards Trans People (EANT)
Authors: Alonso-Martínez Laura , Heras-Sevilla Davinia , Fernández-Hawrylak María , Forrest Simon
Published: March 2021

(1) Background: Negative attitudes towards sexual minorities are widespread in our society. The Scale of Negative Attitudes towards Transgender people (EANT) has been tested in Spanish-speaking countries in order to assess its applicability as a measure of harmful predispositions towards trans individuals. Understanding these predispositions is important because of transformations in the rights of people in terms of respect for gender diversity. (2) Methods: For the validation of this scale, an online survey was developed and distributed to 362 UK university students aged 18 to 45 years (M = 21.43, SD = 3.42). The sample was randomly divided in half, carrying out the exploratory factor analysis for the first 180 students and performing the confirmatory factor analysis for the remaining 182. (3) Results: The validation of this unifactorial instrument in English was obtained, with a high internal consistency (α = 0.810) that suggests high applicability to measuring this construct, as well as showing expected relationships with typical variables (HATH, TIBS, gender, sexual orientation, religion and education). (4) Conclusions: This study assesses attitudinal tendencies and reveals how sexual prejudice is still implicit in our societies and makes the stigmatisation and discrimination of trans people visible. These findings support the development of strategies to tackle these predispositions.
Mediating Effects of Family Stress on the Relationship between Self-Efficacy and Midlife Crisis in Middle-Aged Men
Authors: Kwon Mihyoung , Oh Jihyun
Published: March 2021

Middle-aged men bridge younger and older generations and are burdened with social roles and responsibilities that increase their risk of poor health. The aim of this study was to investigate whether family stress had a mediating effect on the relationship between self-efficacy and midlife crisis in a sample of 198 middle-aged South Korean men. Mediation analysis was performed according to the Baron and Kenny method and using the Sobel test. This study confirmed that midlife crisis decreased with increased self-efficacy and increased with family stress level. Family stress was also a significant partial mediator of the relationship between self-efficacy and midlife crisis. As family stress influences all members of the family, interventions are required that engage all family members to alleviate stress. Strategies to boost self-efficacy should be utilized, and family support constitutes the best means of coping.
An Integrated Competitiveness Assessment of the Baltic Capitals Based on the Principles of Sustainable Development
Authors: Činčikaitė Renata , Meidute-Kavaliauskiene Ieva
Published: March 2021

The competition between cities involves the search for different ways and means to create, attract, sustain, and use diverse resources, knowledge, ideas, and innovations to support the economic growth of each individual city and, as a result, to strengthen the city’s position in the urban hierarchy both short-term and long-term. However, for each city, urbanisation does not only mean an increase in competitive economic power but also a number of problems such as pollution, poverty, crime and unemployment. In order to address the challenges posed by the urbanisation processes, the United Nations World Commission on Environment and Development (WCED) issued the 1987 Report that emphasised the need for sustainable socio-economic development that would also consider ecological factors. Given this, the article examines the issue of urban competitiveness on the basis of sustainable development principles. After a systematic and comparative analysis of the concepts published in the scientific literature, this article accomplishes the following: It defines the concept of competitiveness in urban areas; it identifies the inclusion aspects of the sustainable development principles into the assessment of urban competitiveness; it presents the research into urban competitiveness assessment models; and it carries out an integrated competitiveness assessment of the Baltic capitals in the period of 2014–2019 based on the principles of sustainable development by applying the integrated competitiveness assessment model that is based on the principles of sustainable development (MDK).
Effects of Canada Goldenrod Invasion on Soil Extracellular Enzyme Activities and Ecoenzymatic Stoichiometry
Authors: Hu Zhiyuan , Li Jiating , Shi Kangwei , Ren Guangqian , Dai Zhicong , Sun Jianfan , Zheng Xiaojun , Zhou Yiwen , Zhang Jiaqi , Li Guanlin , Du Daolin
Published: March 2021

The rapid expansion of Canada goldenrod (Solidago canadensis L.) in China has drawn considerable attention as it may not only decrease vegetation diversity but also alter soil nutrient cycling in the affected ecosystems. Soil extracellular enzymes mediate nutrient cycling by catalyzing the organic matter decomposition; however, the mechanisms by which alien plant invasion may affect soil extracellular enzymes remain unclear. The objective of this study was to investigate the responses of soil extracellular enzyme activities and ecoenzymatic stoichiometry to S. canadensis invasion. Several extracellular enzymatic activities related to carbon, nitrogen, and phosphorus cycling were measured using a fluorometric method. Ecoenzymatic stoichiometry was used as a proxy of soil microbial metabolic limitations. S. canadensis invasion appeared to be associated with decreased activities of enzymes and with substantial conversions of microbial metabolic carbon and nitrogen limitations. The changes in the activities of extracellular enzymes and the limitations of microbial metabolism were correlated with the alterations in the nutrient availability and resource stoichiometry in the soil. These findings reveal that the alterations in soil available nutrients associated with S. canadensis invasion may regulate extracellular enzymatic activities and cause microbial metabolic limitations, suggesting that S. canadensis invasion considerably affects biogeochemical cycling processes.
Toward Life Cycle Sustainability in Infrastructure: The Role of Automation and Robotics in PPP Projects
Authors: Hoeft Madeleine , Pieper Marianne , Eriksson Kent , Bargstädt Hans-Joachim
Published: March 2021

This article identifies how project life cycle characteristics and automation and robotic technologies influence the sustainability of public-private partnership (PPP) infrastructure projects. The result of the article is a model of how public and private collaborations can leverage technology and project organization to make infrastructure more sustainable. Based on a comprehensive literature review, the model subdivides sustainability into engineering, project management, environmental, social, and economic dimensions. Engineering sustainability concerns the applicability of technologies to infrastructure PPP sustainability. The project management sustainability is decisive for ultimately creating environmental, social and economic sustainability within and beyond infrastructure PPP projects. The model identifies that the procurement phase is of particular importance for sustainable infrastructure PPPs. Successful sustainable infrastructure procurement likely includes such factors as increased transparency, participation, and stable, capable project alliances with a shared vision and clear goals. The model also identifies that, throughout the whole project life cycle, actions in the form of collaboration, experimentation and platformization promote sustainability. The findings in this article add to the understanding of how transformation toward increased sustainability can be achieved by individual organizations, their network, and ecosystems of public, private and civic actors.
Kinetic and Isotherm Studies of Ni2+ and Pb2+ Adsorption from Synthetic Wastewater Using Eucalyptus camdulensis—Derived Biochar
Authors: Shafiq Muhammad , Alazba Abdulrahman Ali , Amin Muhammad Tahir
Published: March 2021

The production of biosorbents by waste biomass has attracted considerable attention due to the low cost and abundance of the raw materials. Here biochar produced from Eucalyptus camdulensis sawdust (EU-biochar) via pyrolysis at 600 °C was used as a potential biosorbent for Ni2+ and Pb2+ metal ions from wastewater. Characterization experiments indicated the formation of C- and O-bearing functional groups on the EU-biochar surface, while shifts and changes in the shape of C–H bands suggested the adsorption of Ni2+ and Pb2+ onto EU-biochar by interacting with surface carboxylic groups. Pb2+ was adsorbed more quickly than Ni2+, indicating a faster and stronger interaction of Pb2+ with EU-biochar compared to Ni2+. As the initial concentrations of both metal ions increased, the percentage removal decreased, whereas increasing the EU-biochar dose improved the percentage removal but impaired the adsorption capacity for Ni2+ and Pb2+. The adsorption capacity could only be improved without affecting the percentage removal of both ions by increasing the pH of the metal solutions. The sorption efficiency of EU-biochar and the removal mechanism of Ni2+ and Pb2+ were further explored using non-linear and linear forms of kinetic and isotherm models.